The Saint Louis University Mental Status Examination (SLUMS) is a brief, clinician-administered cognitive screening instrument developed by Tariq and colleagues at Saint Louis University in 2006. It was designed as an alternative to the MMSE with improved sensitivity to mild cognitive impairment and early dementia. The SLUMS includes items assessing orientation, attention, memory, and executive function, and is particularly useful in older adult populations in primary care and geriatric settings.

Clinician-administered cognitive screening instrument

Tariq, S. H., Tumosa, N., Chibnall, J. T., Perry, M. H., & Morley, J. E. (2006). Comparison of the Saint Louis University Mental Status Examination and the Mini-Mental State Examination for detecting dementia and mild neurocognitive disorder—A pilot study. American Journal of Geriatric Psychiatry, 14(11), 900-910.

Morley, J. E., Malmstrom, T. K., & Miller, D. K. (2012). A simple frailty screening tool for primary care. Journal of the American Geriatrics Society, 60(1), 137-141.

Millard, F. C., Tariq, S. H., Tumosa, N., & Morley, J. E. (2003). Validation of the Saint Louis University Mental Status Examination in nonagenarians. Journal of the American Geriatrics Society, 51(Suppl 1), S40.

mmse adas-cog addenbrookes-cognitive-examination dementia-rating-scale frontal-assessment-battery


Salivary Biomarker Analysis

Oral Fluid Biomarker Testing for Disease Assessment

saliva testing
salivary diagnostics
oral biomarker assessment

Diagnostic microbiology and biochemistry

Multiple innovators (Giannobile, Malamud, et al.)

Salivary biomarker analysis detects protein, molecular, or microbial markers in saliva that indicate oral and systemic disease. Salivary diagnostics assess risk and activity of dental caries, periodontal disease, oral cancer, and other conditions. Biomarkers include antimicrobial proteins (lysozyme, lactoferrin), inflammatory mediators (interleukins, TNF-alpha), cariogenic bacteria (Streptococcus mutans), and virulence factors. Point-of-care saliva testing offers rapid, non-invasive alternatives to conventional laboratory methods, enabling chairside diagnosis and personalized risk assessment.

Laboratory and point-of-care diagnostics

Giannobile, W. V., McDevitt, J. T., Niedbala, R. S., Malamud, D., & Prozorovsky, T. (2009). Translating molecular diagnostics into clinical practice: Designing the next generation of oral health technologies. Advances in Dental Research, 23(1), 80-89.

Siqueira, W. L., & Salih, E. (2012). Proteoglycans and proteomics in oral fluids: translating from the laboratory to clinical practice. Advances in Dental Research, 24(1), 74-77.

Devic, M., Glenski, M., Phelps, K., Lynch, T., & Brice, D. (2015). Use of salivary biomarkers in dentistry: A systematic review. Journal of Evidence-Based Dental Practice, 15(1), 1-11.

periodontal-probing gingival-index dmft-index dental-erosion-index


Sample Entropy

SampEn
Sample Entropy (SampEn)
Örneklem Entropisi
Nonlinear Complexity Measure

Sample Entropy (SampEn) is a nonlinear measure of the complexity and regularity of a time series. Introduced by Richman and Moorman in 2000 as an improvement over Approximate Entropy (ApEn), it quantifies the likelihood that similar patterns of a given length in the series remain similar when extended by one additional data point. A higher SampEn value indicates greater irregularity and complexity, while a lower value indicates more regularity or self-similarity.

Input: Univariate time series

Output: Non-negative scalar (entropy value)

Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology, 278(6), H2039–H2049.


Sampling Methods in Research

Sampling Techniques and Sampling Frame Design

probability and non-probability sampling

William G. Cochran and Leslie Kish (1950s–1970s)

Sampling is the process of selecting a subset of individuals, observations, or units (the sample) from a larger population to study. Sampling methods are broadly classified into probability (random) and non-probability (non-random) approaches. Probability methods—random sampling, stratified sampling, cluster sampling, systematic sampling—enable statistical inference to the population and allow calculation of confidence intervals and margins of error. Non-probability methods—convenience, purposive, snowball, quota sampling—are practical for exploratory or qualitative research but do not support formal statistical generalization. Cochran's Sampling Techniques (1977) and Kish's Survey Sampling (1965) are foundational references; modern applications span surveys, experiments, qualitative studies, and clinical trials.

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

Kish, L. (1965). Survey Sampling. John Wiley & Sons.

Patton, M. Q. (2015). Qualitative Research & Evaluation Methods (4th ed.). SAGE Publications.


SAPEVO-M

SAPEVO-M (Simple Aggregation of Preferences Expressed by Ordinal Vectors — Multi-Decision Maker) is a weighting multi-criteria decision-making (MCDM) method introduced by Gomes, L. F. A. M. de Mello, J. C. C. B. S. Costa, H. G. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Ordinal pairwise comparisons from multiple DMs aggregated into priority weights

Gomes, L. F. A. M., de Mello, J. C. C. B. S., Costa, H. G. (2020). SAPEVO-M: a group ranking approach based on preference vectors. Pesquisa Operacional


SAPROF

Vivienne de Vogel, Corine de Ruiter, Yvonne Bouman, Merike de Vries Robbé

The Structured Assessment of Protective Factors for Violence Risk (SAPROF) is a 17-item structured professional judgment tool developed by de Vogel, de Ruiter, Bouman, and colleagues (2012) to identify protective factors and strengths in individuals undergoing violence risk assessment. It complements risk assessment instruments (e.g., HCR-20v3) by systematically evaluating resilience, social support, motivation, and positive functioning—domains that mitigate violence risk and inform rehabilitation potential.

de Vogel, V., de Ruiter, C., Bouman, Y., & de Vries Robbé, M. (2012). SAPROF: Structured Assessment of Protective Factors for violence risk (Version 3). Forum Educatief.

de Vogel, V., & de Ruiter, C. (2009). The Structured Assessment of Protective Factors for violence risk (SAPROF): Development and preliminary validity. International Journal of Forensic Mental Health, 8(4), 263–269.

hcr-20 violence-risk-appraisal-guide level-of-service-inventory structured-professional-judgment


SAR Image Analysis

Synthetic Aperture Radar Processing
Radar Remote Sensing Analysis
Microwave Imaging Analysis
SAR Görüntü Analizi

Synthetic Aperture Radar (SAR) Image Analysis is an active microwave remote sensing pipeline that processes complex-valued radar backscatter data to characterize land cover, surface roughness, moisture, and structural properties. Foundational treatment was consolidated by Jong-Sen Lee and Eric Pottier in their 2009 CRC Press volume, which established the polarimetric framework widely adopted by research and operational communities working with satellites such as Sentinel-1, ALOS PALSAR, and RADARSAT.

Active microwave image processing pipeline

Complex-valued backscatter amplitude and phase

Lee, J.-S., & Pottier, E. (2009). Polarimetric Radar Imaging: From Basics to Applications. CRC Press.


SARIMA model

Seasonal Autoregressive Integrated Moving Average Model

SARIMA
seasonal ARIMA
Box-Jenkins seasonal model
ARIMA with seasonal component

SARIMA extends ARIMA by adding seasonal autoregressive and moving-average operators to capture repeating patterns at fixed intervals — such as monthly, quarterly, or annual cycles. Denoted SARIMA(p,d,q)(P,D,Q)s, it is the standard workhorse for univariate seasonal time series forecasting in econometrics, economics, and official statistics.

Univariate equally-spaced time series with seasonal pattern

Box, G. E. P., Jenkins, G. M., & Reinsel, G. C. (1976). Time Series Analysis: Forecasting and Control (revised ed.). Holden-Day.

Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.

arima-model arma-model autoregressive-model moving-average-model vector-autoregression exponential-smoothing


SARIMA

Seasonal Autoregressive Integrated Moving Average

seasonal ARIMA
Box-Jenkins seasonal model
SARIMA — Mevsimsel ARIMA

Box & Jenkins (seasonal extension of ARIMA)

SARIMA is a seasonal extension of the Box-Jenkins ARIMA model that adds seasonal differencing and seasonal autoregressive and moving-average terms. Developed within the Box, Jenkins, Reinsel and Ljung framework (5th edition, 2015), it forecasts series whose pattern repeats on a yearly, monthly, or weekly period.

Maximum likelihood (Box-Jenkins methodology)

Box, G.E.P., Jenkins, G.M., Reinsel, G.C. & Ljung, G.M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.

Hyndman, R.J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.

sarimax ets-model holt-winters prophet-model state-space-model


SARIMAX

Seasonal ARIMA with Exogenous Regressors

SARIMAX — Dışsal Değişkenli Mevsimsel ARIMA

Box & Jenkins (ARIMA framework); SARIMAX extension with exogenous regressors

SARIMAX extends the seasonal ARIMA (Box-Jenkins) model by adding exogenous explanatory variables, so it can capture the effect of holidays, economic indicators, or policy variables on a time series. It combines non-seasonal and seasonal autoregressive and moving-average dynamics with external regressors, and is estimated by maximum likelihood in state-space form.

Maximum likelihood via state-space (Kalman filter)

Hyndman, R. J. & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts.

Box, G. E. P., Jenkins, G. M., Reinsel, G. C. & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). Wiley.

arima prophet ets-exponential-smoothing holt-winters state-space-model bvar


Data Saturation in Qualitative Research

Theoretical and Thematic Saturation in Qualitative Data Collection

saturation
theoretical saturation
thematic saturation
sampling to saturation

Data saturation is a foundational principle in qualitative research describing the point at which data collection yields no new themes, codes, or insights—additional data becomes redundant. Introduced by Glaser and Strauss (1967) in their work on grounded theory, saturation guides decisions about sample size and when to stop recruiting participants. Saturation is not a fixed number but a dynamic endpoint determined by examining whether new data are adding substantively new information. The concept is central to claims of rigor and theoretical adequacy in qualitative research, signaling that the researcher has gathered sufficient data to understand the phenomenon in depth.

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

Strauss, A., & Corbin, J. (1998). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (2nd ed.). SAGE Publications.

Bowen, G. A. (2008). Naturalistic inquiry and saturation (S): Determining when enough is enough. Journal of Research in Education, 18(1), 137-152.

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59-82.

in-depth-interview-method focus-group-methodology grounded-theory thematic-analysis


SAW

SAW (Simple Additive Weighting) is a ranking multi-criteria decision-making (MCDM) method introduced by Fishburn, P. C. in 1967. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Fishburn, P. C. (1967). Additive utilities with incomplete product sets: Application to priorities and assignments. Operations Research

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


SAXS

Small-Angle X-ray Scattering (SAXS) is a solution-phase X-ray scattering technique that measures the overall shape and size of macromolecules and nanoparticles by analyzing scattering intensity at low angles (0.1-10 degrees). Developed by Kratky and colleagues in the 1950s, SAXS provides information about molecular radius, aggregation state, and overall shape without requiring crystallization or fixing, making it ideal for studying native protein conformations and dynamics.

Glatter, O., & Kratky, O. (1982). Small Angle X-ray Scattering. Academic Press.

Koch, M. H., Vachette, P., & Svergun, D. I. (2003). Small-angle scattering: a view on the properties, structures and structural changes of biological macromolecules in solution. Quarterly Reviews of Biophysics, 36(2), 147-227.


SCAD Penalized Regression

Smoothly Clipped Absolute Deviation Penalized Regression

SCAD (Smoothly Clipped Absolute Deviation) is a variable selection and regularization method developed by Fan and Li (2001) that addresses limitations of L1 penalization (lasso). SCAD uses a non-concave penalty that automatically performs variable selection while maintaining oracle properties: it recovers the true underlying model as if the true predictors were known in advance.

Penalized regression with non-concave penalty

Fan, J., & Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96(456), 1348-1360.

Zou, H., & Li, R. (2008). One-step sparse estimates in nonconcave penalized likelihood models. Annals of Statistics, 36(4), 1509-1533.

Wang, H., Li, G., & Tsai, C. L. (2007). Regression coefficient and autoregressive order shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(1), 63-78.


Scaffold Porosity Analysis

Tissue Engineering Scaffold Porosity Analysis

Pore size distribution
Porosity measurement
Scaffold characterization

Scaffold porosity analysis characterizes the pore structure of tissue engineering scaffolds, including total porosity, pore size distribution, pore shape, and pore interconnectivity. Essential for predicting cell seeding, nutrient diffusion, and mechanical properties, this quantitative approach bridges scaffold design and biological performance.

Hutmacher, D. W. (2000). Scaffolds in tissue engineering bone and cartilage. Biomaterials, 21(24), 2529-2543.

Zhou, W. Y., Wang, M., & Cheung, W. L. (2008). Synthesis of non-layered potassium niobate nanowires with enhanced photocatalytic performance. Nanotechnology, 19(8), 085604.


Scale development

questionnaire construction
instrument development
measurement scale construction
psychometric scale building

Multiple contributors; codified by Robert DeVellis and Lee Anna Clark & David Watson

Scale development is a structured, multi-step process for creating psychometrically sound measurement instruments that capture latent psychological constructs. It encompasses construct definition, item generation, expert review, exploratory and confirmatory factor analysis, reliability estimation, and validity evidence collection — producing a final set of items suitable for quantitative research.

Ordinal or interval item responses (Likert-type, rating scales)

DeVellis, R. F. (2016). Scale Development: Theory and Applications (4th ed.). SAGE Publications.

Clark, L. A. & Watson, D. (1995). Constructing validity: Basic issues in objective scale construction. Psychological Assessment, 7(3), 309–319.

exploratory-factor-analysis confirmatory-factor-analysis item-response-theory cronbachs-alpha content-validity construct-validity


Scale-Space Theory

Scale-Space Theory and Multi-Scale Image Analysis

Multi-scale analysis
Gaussian scale-space

Scale-space theory, developed by Witkin and Lindeberg, provides a principled mathematical framework for analyzing images at multiple scales simultaneously. By treating scale as an explicit dimension and using Gaussian blurring, scale-space theory enables detection and analysis of features at appropriate scales, solving the fundamental problem of 'which scale should I analyze at?'

Theoretical framework for multi-scale processing

Lindeberg, T. (1994). Scale-space theory: A basic tool for analyzing structures at different scales. Journal of Applied Statistics, 21(2), 225–270.

Witkin, A. P. (1983). Scale-space filtering. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI), 1019–1022.

sift-feature-detection blob-detection harris-corner-detection canny-edge-detection orb-feature-descriptor


Scaling Up Health Interventions

Scaling Up Health Interventions: A Systematic Approach to Expanding Successful Pilot Programs from Single Sites to Health Systems

scaling up
expansion
scale
dissemination

Simmons, R., Fajans, P., Ghiron, L. (World Health Organization)

Scaling Up is the deliberate expansion of successful health interventions from pilot sites to entire health systems, regions, or countries. Formalized by the World Health Organization (WHO) and Simmons et al. (2007), scaling up is distinct from simple dissemination; it requires systematic planning, financial modeling, capacity building, and policy alignment to ensure interventions work at scale. A pilot that succeeds brilliantly with champion leadership, dedicated funding, and motivated staff may fail when scaled to routine settings with limited resources. Scaling Up frameworks help practitioners anticipate and overcome these challenges.

Simmons, R., Fajans, P., & Ghiron, L. (Eds.). (2007). Scaling Up Health Service Delivery: From Pilot Innovations to Policies and Programmes. World Health Organization, Geneva.

Yamey, G. (2011). Scaling up global health interventions: A call for papers. The Lancet, 378(9802), e40-e41.

World Health Organization. (2008). Scaling Up Health Service Delivery: From Pilot Innovations to Policies and Programmes. WHO, Geneva.

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


Scan Sampling

Scan Sampling Behavioral Observation Method

instantaneous sampling
scan observation
group sampling

Scan Sampling (also called instantaneous sampling) is a behavioral observation method in which an observer records the state of all group members simultaneously at regular time intervals. Introduced alongside focal animal sampling by Jeanne Altmann in 1974, scan sampling is efficient for quantifying activity budgets and group-level behavioral patterns in multiple animals without the labor of focal observation.

Altmann, J. (1974). Observational study of behavior: sampling methods. Behaviour, 49(3-4), 227-267.

Martin, P., & Bateson, P. P. (1993). Measuring Behaviour: An Introductory Guide (2nd ed.). Cambridge University Press.

Coad, N., Al-Rasheid, K. A., & Sluydts, V. (2002). Instantaneous sampling of group-living primates. Primates, 43(2), 105-110.


Scatchard Analysis

Scatchard Plot Analysis of Receptor Binding

Scatchard plot
binding analysis
Kd determination

Scatchard analysis is a graphical method for determining ligand-receptor binding affinity (Kd) and binding capacity (Bmax) from binding data. Developed by George Scatchard in 1949, the Scatchard plot linearizes hyperbolic binding curves, enabling visual detection of multiple binding sites and quantitative parameter estimation.

Scatchard, G. (1949). The attractions of proteins for small molecules and ions. Annals of the New York Academy of Sciences, 51(4), 660-672.

Rosenthal, H. E. (1967). A graphic method for the determination and presentation of binding parameters in a complex system. Analytical Biochemistry, 20(3), 525-532.


Simple Clinical Colitis Activity Index

Walmsley, R. S., Ayres, R. C., Pounder, R. E., and Allan, R. N.

The Simple Clinical Colitis Activity Index (SCCAI) is a practical, bedside tool for assessing disease activity in ulcerative colitis and colonic Crohn's disease. Published in 1998 by Walmsley and colleagues, the SCCAI condenses disease assessment into six items that can be administered in a office visit without laboratory or endoscopic data. It provides rapid, reproducible quantification of disease severity and is ideal for frequent monitoring in routine clinical practice.

Walmsley, R. S., Ayres, R. C., Pounder, R. E., & Allan, R. N. (1998). A simple clinical colitis activity index. Gut, 43(1), 29–32.

mayo-score-uc harvey-bradshaw-index ibdq-short cdai-crohns


Scenario Analysis

Scenario Analysis and What-If Simulation

what-if analysis
what-if simulation
stress testing
scenario planning
Senaryo Analizi ve What-If Simülasyonu

1950s (origins); widely adopted in management since 1970s

Peter Schwartz (scenario planning formalization), Herman Kahn (RAND Corporation, 1950s–60s)

Scenario analysis is a structured analytical approach that systematically compares system outputs across different combinations of uncertain input values. When paired with a quantitative model, it becomes a simulation — capable of stress-testing assumptions and projecting the range of plausible outcomes. Formalised in strategic planning by Peter Schwartz and Herman Kahn from the 1950s onward, the method is widely used in policy evaluation, business forecasting, financial risk assessment, and scientific model exploration.

Structured analytical approach / simulation

Output: Comparative system outputs across defined scenarios (pessimistic / base / optimistic)

Goodwin, P. & Wright, G. (2014). Decision Analysis for Management Judgment (5th ed.). Wiley.

Schwartz, P. (1991). The Art of the Long View. Currency Doubleday.

monte-carlo-simulation sensitivity-analysis decision-tree-analysis multi-criteria-decision-analysis global-sensitivity-analysis


Scheffé Test

simultaneous confidence intervals for contrasts

The Scheffé test is a post-hoc multiple comparison procedure that controls the family-wise error rate simultaneously for all possible linear contrasts among group means following a significant ANOVA. Introduced by Henry Scheffé in his landmark 1953 Biometrika paper, it is the most general and conservative standard post-hoc method, remaining valid regardless of how many or which contrasts are examined after seeing the data.

most conservative among standard post-hoc tests

Scheffé, H. (1953). A method for judging all contrasts in the analysis of variance. Biometrika, 40(1–2), 87–110.

Scheffé, H. (1959). The Analysis of Variance. Wiley.

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

tukey-hsd bonferroni-correction one-way-anova two-way-anova dunnett-test fisher-lsd


Schild Analysis

Schild analysis is a quantitative method for characterizing competitive receptor antagonism developed by Henry Schild in 1947. It uses dose-response curves in the presence and absence of antagonist to estimate the antagonist affinity constant (pA2), enabling standardized comparison of antagonist potency across drugs and experimental systems.

Schild, H. O. (1947). pA, a new scale for the measurement of drug antagonism. Journal of Physiology, 106(3), 337-357.

Arunlakshana, O., & Schild, H. O. (1959). Some quantitative uses of drug antagonisms. British Journal of Pharmacology and Chemotherapy, 14(1), 48-58.


Schizotypal Personality Questionnaire

Schizotypal Personality Questionnaire (SPQ)

The SPQ is a 74-item self-report measure of schizotypal personality traits across cognitive-perceptual, interpersonal, and disorganized domains. Developed by Adrian Raine in 1991 based on DSM-III-R schizotypal personality disorder criteria, it is the most widely used dimensional measure of schizotypy on the psychosis spectrum. The SPQ is valuable for identifying psychotic-like experiences and personality features that may increase psychosis vulnerability.

Raine, A. (1991). The SPQ: A scale for the assessment of schizotypal personality based on DSM-III-R criteria. Schizophrenia Bulletin, 17(4), 555–564.


School Climate Scale

The School Climate Scale (SCS) is an institutional assessment tool that measures the overall social and emotional environment of a school. Grounded in organizational climate research, instruments such as Hoy and Tarter's Organizational Climate Description Questionnaire (OCDQ) evaluate dimensions including principal leadership, teacher collaboration, student relationships, and school safety, providing schools with data on the intangible but crucial aspects of school life that affect student outcomes and well-being.

Anderson, C. S. (1982). The search for school climate: a review of the research. Review of Educational Research, 52(3), 368-420.

Hoy, W. K., Tarter, C. J., & Kottkamp, R. B. (2000). Open Schools/Healthy Schools: Measuring Organizational Climate. Sage Publications.

student-engagement-scale sense-of-belonging-scale teaching-effectiveness-scale student-satisfaction-survey


School Engagement Scale

SES
Student Engagement
Academic Engagement

The School Engagement Scale (SES) conceptualizes and measures school engagement as a multidimensional construct encompassing behavioral engagement (participation in academic and school activities), emotional engagement (motivation, interest, sense of belonging), and cognitive engagement (willingness to exert effort, persistence in learning). Developed by Fredricks, Blumenfeld, and Paris (2004), school engagement is a critical predictor of academic achievement, grade retention, and high school dropout.

Multidimensional school engagement measurement

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74(1), 59-109.

Appleton, J. J., Christenson, S. L., & Furlong, M. J. (2008). Student engagement with school: Critical conceptual and methodological issues of the construct. Psychology in the Schools, 45(5), 369-386.

pediatric-quality-of-life-pedsql cbcl-child-behavior strengths-difficulties-questionnaire vanderbilt-adhd-scale


SCHULZE

SCHULZE (Schulze Method — Beat-path Condorcet-consistent rank aggregation) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Schulze, M. in 2011. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Rank aggregation (beat-path, polynomial time, Condorcet-consistent)

Schulze, M. (2011). A new monotone and clone-independent single-winner election method. Voting Matters


Science Mapping

Science Mapping and Knowledge Domain Visualization

knowledge mapping
domain mapping
research landscape visualization

Science mapping is a bibliometric visualization method that creates visual representations of research domains, showing the structure, development, and relationships of scientific fields. Using bibliographic data (citations, keywords, authors, journals), science mapping algorithms generate network diagrams where nodes represent documents, concepts, or authors and edges represent relationships (citation, collaboration, semantic similarity). The resulting maps make invisible intellectual structures visible, enabling researchers to understand field topology, identify emerging areas, and navigate disciplinary landscapes. Pioneered by Börner, Chen, and Boyack in the 2000s, science mapping has become a standard tool in research evaluation and strategic planning.

Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255.

Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.

co-citation-analysis bibliographic-coupling keyword-co-occurrence vosviewer-citespace research-front-identification


Scientific Text Mining

Bilimsel Metin Madenciliği
scholarly NLP
academic text mining
scientific literature mining

2019–2020 (modern transformer era); roots in earlier computational linguistics

Community-developed; SciBERT (Beltagy et al., 2019) and SPECTER (Cohan et al., 2020) are landmark models

Scientific text mining is a natural-language-processing pipeline applied to academic literature. Grounded in domain-specific pretrained models such as SciBERT (Beltagy et al., 2019) and SPECTER (Cohan et al., 2020), it automatically extracts hypotheses, methodologies, findings, and scholarly contributions from full-text papers or abstracts, enabling systematic review automation, research-trend analysis, and science mapping at scale.

Input: Full text or abstracts of academic papers

Output: Extracted hypotheses, methods, findings, and contributions; trend maps; structured metadata

Health, Social Sciences, Education, Natural Sciences, Business — all weighted ≥ 1.4

Beltagy, I., Lo, K., & Cohan, A. (2019). SciBERT: A Pretrained Language Model for Scientific Text. EMNLP 2019.

Cohan, A., Feldman, S., Beltagy, I., Downey, D., & Weld, D. (2020). SPECTER: Document-Level Representation Learning using Citation-Informed Transformers. ACL 2020.

sentiment-analysis topic-modeling named-entity-recognition systematic-review-automation bibliometric-analysis


Scientific Writing Clarity

Principles of Clear, Precise Scientific Communication

clarity in writing
scientific communication
technical writing

Scientific writing tradition; modern frameworks from Greenhalgh (1997), Strunk & White (2000), and writing educators

Clear scientific writing enables readers to understand methodology, results, and implications without confusion. Clarity is not ornamental—it is essential to scientific integrity. Unclear writing obscures findings, enables misinterpretation, wastes readers' time, and reduces impact and citations. Scientific clarity requires active voice (when appropriate), conciseness (eliminating redundancy), precise word choice (correct terminology), logical organization, and transparent reasoning. These principles apply across disciplines and are supported by style guides (APA, Vancouver), writing textbooks, and journal editors' expectations. Clear writing also helps authors think more precisely; the act of writing clearly often reveals gaps or inconsistencies in logic.

Strunk, W., Jr., & White, E. B. (2000). The Elements of Style (4th ed.). New York: Longman.

Greenhalgh, T. (1997). How to read a paper: The basics of evidence based medicine. British Medical Journal, 315(7112), 180–184.

Pierson, R. (2009). Better Writing for Better Science. London: Pearson Education.

imrad-structure abstract-writing figure-table-reporting statistical-reporting-standards


Scientometric Analysis

Scientometric Analysis of Scientific Literature

scientometrics
science of science
quantitative science studies
research evaluation analysis

1969 (term); 1963 (Price's foundational work)

V. V. Nalimov and Z. M. Mulchenko (term coined); Derek J. de Solla Price (foundational methods)

Scientometric analysis applies statistical and computational methods to publication and citation data to measure the growth, structure, and impact of scientific fields. Drawing on databases such as Web of Science, Scopus, or OpenAlex, it quantifies output trends, identifies leading authors and institutions, maps intellectual networks, and evaluates research impact — transforming large bibliographic corpora into evidence-based portraits of how knowledge develops and spreads.

Publication metadata (titles, authors, journals, citations, keywords, abstracts)

Nalimov, V. V., & Mulchenko, Z. M. (1969). Naukometriya: Izucheniye razvitiya nauki kak informatsionnogo protsessa [Scientometrics: The Study of the Development of Science as an Information Process]. Nauka.

Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25(4), 348–349.

bibliometric-analysis co-citation-analysis bibliographic-coupling co-word-analysis science-mapping systematic-literature-review


SCImago Journal Rank

SJR
SCImago Journal Rank
Prestige-weighted impact

SCImago Group (Spanish research consortium)

SCImago Journal Rank (SJR) is a prestige-weighted metric measuring journal citation impact based on Scopus data, developed by SCImago Group (a Spanish research consortium) in 2010. Unlike raw citation counts, SJR values citations from high-prestige journals more heavily than those from lower-prestige journals, similar to Google's PageRank algorithm. This prestige weighting approach accounts for field-specific citation cultures and provides fairer cross-discipline comparisons than raw impact factor. SJR is widely used for journal ranking, quality assessment, and publication targeting, complementing traditional Impact Factor with a prestige dimension.

González-Pereira, B., Guerrero-Bote, V. P., & Moya-Anegón, F. (2010). The SJR indicator: A new indicator of journals' scientific prestige. Scientometrics, 82(2), 391-400.

SCImago. (2024). SJR Rankings & Methodology. Retrieved from https://www.scimagojr.com/

Elsevier Scopus. (2023). CiteScore and SJR Comparison. Retrieved from https://blog.scopus.com/

scopus-database impact-factor journal-citation-reports web-of-science h-index


SCINet

SCINet (Sample Convolution and Interaction Network)

Temporal Downsampling Convolution Network

SCINet is a deep learning architecture for multi-step time-series forecasting introduced by Liu et al. at NeurIPS 2022. Its core idea is a recursive binary-tree structure of SCI-Blocks, each of which splits an input sequence into odd- and even-indexed sub-sequences, applies convolutional filters to model cross-subsequence interactions, and then merges the learned representations. This hierarchical downsampling strategy enables the network to capture temporal dependencies at multiple resolutions simultaneously.

Hierarchical convolutional time-series forecasting network

Iterative odd-even downsampling with convolutional interaction

Liu, M., Zeng, A., Chen, M., Xu, Z., Lai, Q., Ma, L., & Xu, Q. (2022). SCINet: Time series modeling and forecasting with sample convolution and interaction. NeurIPS.


SCOFF

SCOFF Questionnaire
Sick, Control, One, Fat, Food

The SCOFF is a five-question screening tool for eating disorders, developed by Morgan, Reid, and Lacey at the University of Leeds in 1999. Its acronym—Sick, Control, One, Fat, Food—represents its five core items. The SCOFF is exceptionally brief, takes less than 2 minutes to administer, and was designed to identify cases of anorexia nervosa and bulimia nervosa in primary care and medical settings. It remains one of the fastest and most widely used screening instruments globally.

Clinician-administered or self-report screening questionnaire

Morgan, J. F., Reid, F., & Lacey, J. H. (1999). The SCOFF questionnaire: Assessment of a new screening tool for eating disorders. BMJ, 319(7223), 1467–1468.

Cotton, M. A., Ball, C., & Robinson, P. (2003). Four simple screening questions can help identify eating disorders. Journal of General Internal Medicine, 18(1), 53–56.

Solmi, M., Veronese, N., Favaro, A., et al. (2015). Inflammatory cytokines and C-reactive protein across the lifespan of bipolar disorder: Systematic review and meta-analysis. Brain, Behavior, and Immunity, 70, 193–204.

ede-q three-factor-eating-questionnaire body-shape-questionnaire binge-eating-scale


Scoping Review Methodology

Scoping Review (Arksey & O'Malley Framework, JBI Extension)

Scoping Review
Scoping Study
Scope of the Field

Arksey & O'Malley (2005), Extended by JBI (2020) and PRISMA-ScR (2018)

A scoping review is a structured, transparent literature mapping method that identifies and synthesizes evidence across a defined topic without formally assessing study quality or generating pooled effect estimates. Developed by Arksey and O'Malley (2005) and refined by the Joanna Briggs Institute (JBI) and PRISMA-ScR (2018), scoping reviews answer 'what evidence exists and in what forms' rather than 'what does the evidence conclude'—making them ideal for charting emerging fields, knowledge gaps, and the scope of a literature base before conducting a systematic review or as a standalone rapid knowledge synthesis.

Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.

Tricco, A. C., Lillie, E., Zarin, W., et al. (2018). PRISMA extension for Scoping Reviews (PRISMA-ScR): Checklist and explanation. Annals of Internal Medicine, 169(7), 467–473.

Peters, M. D. J., Godfrey, C., McInerney, P., Munn, Z., Tricco, A. C., & Khalil, H. (2020). Chapter 11: Scoping reviews. In JBI Manual for Evidence Synthesis. JBI.

systematic-review evidence-synthesis-framework literature-mapping rapid-review-methodology narrative-review


Scoping Review

scoping study
literature scoping
evidence mapping review
rapid evidence map

A scoping review is a systematic evidence-synthesis method that maps the breadth and nature of research on a topic — identifying key concepts, evidence types, and gaps — without necessarily appraising study quality or pooling effect sizes. Developed by Arksey and O'Malley (2005) and refined by Levac and colleagues (2010), it is particularly valuable for emerging or heterogeneous fields where a full systematic review would be premature or infeasible.

Published literature (articles, reports, grey literature)

Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: advancing the methodology. Implementation Science, 5(1), 69.

systematic-literature-review narrative-review integrative-review umbrella-review mapping-review bibliometric-analysis


Scopus Database

Scopus, owned by Elsevier, is the world's largest abstract and citation database covering peer-reviewed journals, conference proceedings, and book chapters across all scientific disciplines. Launched in 2004, Scopus now indexes over 37 million documents from more than 6,500 journals, with expanded coverage of open-access publications and emerging regional journals. Scopus provides researchers and institutions with comprehensive citation tracking, field-normalized impact metrics (CiteScore, SJR, SNIP), and analytical tools for literature discovery, research evaluation, and institutional benchmarking.

Elsevier. (2024). Scopus: The largest abstract and citation database of peer-reviewed literature. Retrieved from https://www.elsevier.com/products/scopus

Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: a comparative analysis. Scientometrics, 106(1), 213-228.

Elsevier. (2023). Scopus CiteScore Metrics. Retrieved from https://www.elsevier.com/products/scopus/cite-score

web-of-science scimago-journal-rank impact-factor h-index doaj-directory


SCOR Model

The Supply Chain Operations Reference Model is a standardized framework for supply chain management developed by the Supply Chain Council (now APICS) in 1996. SCOR provides a structured approach to identify, evaluate, and improve supply chain processes across organizations, regardless of industry. It integrates planning, sourcing, manufacturing, delivery, and returns into a coherent operational model.

Stewart, G. (1997). Supply chain operations reference model: SCOR, logistics information management, Vol. 10 No. 5, pp. 62-74.

Bolstorff, P., & Rosenbaum, R. (2003). The supply chain handbook: The definitive guide to optimizing global supply and logistics. Hoboken, NJ: John Wiley & Sons.

aggregate-planning material-requirements-planning kanban vendor-managed-inventory job-shop-scheduling


SCORAD

European Task Force on Atopic Dermatitis (ETFAD)

The SCORAD is a comprehensive clinician-administered tool for measuring the extent and severity of atopic dermatitis (eczema). Developed by the European Task Force on Atopic Dermatitis in 1993, it combines objective clinical assessment with subjective symptom reporting. It is the gold standard for atopic dermatitis severity in clinical trials and dermatology practice.

Kunz B, Oranje AP, Labrèze L, et al. Clinical validation and guidelines for the SCORAD index: consensus report of the European Task Force on Atopic Dermatitis. Dermatology. 1997;195(1):10-19.

Hanifin JM, Rajka G. Diagnostic features of atopic dermatitis. Acta Derm Venereol Suppl (Stockh). 1980;92:44-47.

easi poem dlqi-children pruritus-visual-analog-scale


Score-Based Generative Model

Skor Tabanlı Üretici Model (Score-Based / SDE)

A score-based generative model, introduced by Yang Song and Stefano Ermon in 2019 and generalized to the stochastic differential equation (SDE) framework in 2021, learns the gradient of the data density — the score — rather than predicting noise directly, and uses it to generate new samples. It is the mathematical generalization that unifies diffusion models under a continuous-time formulation.

Score-based generative model (SDE framework)

Song, Y. & Ermon, S. (2019). Generative Modeling by Estimating Gradients of the Data Distribution. NeurIPS 32, 11895–11907.

Song, Y. et al. (2021). Score-Based Generative Modeling through Stochastic Differential Equations. ICLR.

variational-autoencoder neural-ode pca deep-reinforcement-learning capsule-network


Scratch Wound Assay

wound healing assay
gap closure assay
migration assay

The scratch wound assay (also called the wound healing assay or gap closure assay) is a simple, cost-effective method for measuring cell migration in vitro. Developed and standardized by Liang, Park, and Guan in 2007, the assay involves creating a defined gap (wound) in a monolayer of confluent cells using a pipette tip or specialized tool, then monitoring the rate at which cells migrate into the gap over hours to days. The scratch wound assay is widely used to evaluate the effects of growth factors, inhibitory compounds, and biomaterial extracts on cell motility and wound healing potential.

Liang, C. C., Park, A. Y., & Guan, J. L. (2007). In vitro scratch assay: a convenient and inexpensive method for analysis of cell migration in vitro. Nature Protocols, 2(2), 329-333.

Jonkman, J. E. N., Cathcart, J. A., Xu, F., et al. (2014). An introduction to the wound healing assay using live-cell microscopy. Cell Adhesion & Migration, 8(5), 440-451.

Rodriguez, L. G., Wu, X., & Guan, J. L. (2009). Wound-healing assay. In Cell Migration: Developmental Methods and Protocols. Humana Press, pp. 23-29.

transwell-assay live-dead-assay mtt-mts-assay bmp-release


Screening Test Evaluation

Evaluation of Screening Tests and Screening Programs

screening study
screening performance evaluation
screening accuracy assessment
STE

1968 (Wilson-Jungner principles); statistical framework developed 1970s–2000s

Wilson & Jungner (WHO criteria, 1968); foundational work by Pepe, Altman, and others in statistical test evaluation

Screening test evaluation is a systematic epidemiological approach for assessing whether a test or program can accurately and cost-effectively identify individuals with a condition before symptoms appear. It quantifies diagnostic performance metrics — sensitivity, specificity, predictive values, and the ROC curve — and evaluates whether a screening program meets established public health criteria for adoption and harm-benefit balance.

Observational diagnostic / epidemiological evaluation design

Binary or continuous test results, reference standard (gold standard) outcomes, population-level screening data

Wilson, J. M. G., & Jungner, G. (1968). Principles and Practice of Screening for Disease. World Health Organization. Public Health Papers No. 34.

Pepe, M. S. (2003). The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press.

diagnostic-accuracy-study cohort-study cross-sectional-epidemiological-study kaplan-meier-analysis dose-response-analysis case-control-study


SD-WEIGHT

SD-WEIGHT (Standard Deviation Weight — objective weighting by column standard deviation) is a weight objective multi-criteria decision-making (MCDM) method introduced by Various in 1980. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

(). UNCONFIRMED — SD-WEIGHT specific seminal not confirmed via systematic literature search. PENDING

ahpsort aploco aras aroman artasi cobra cocoso codas


Seakeeping Strip Theory

strip theory
2D strip method
seakeeping prediction

Seakeeping strip theory is a method for predicting the dynamic motion of a ship in regular and irregular waves by decomposing the hull into two-dimensional transverse sections (strips) and computing the hydrodynamic forces on each strip. Developed by Salvesen, Tuck, and Faltinsen in 1970, the method efficiently estimates ship heave, pitch, and roll motions, accelerations, and loads without resorting to expensive three-dimensional computational fluid dynamics. Seakeeping analysis using strip theory is standard in ship design and operational planning.

Salvesen, N., Tuck, E. O., & Faltinsen, O. (1970). Ship motions and sea loads. Journal of the Society of Naval Architects and Marine Engineers, 78(4), 250–287.

Journée, J. M. J. (1992). Prediction of speed-dependent ship motions and capsizing in irregular head seas. Ph.D. thesis, Delft University of Technology.

Faltinsen, O. M. (1990). Sea Loads on Ships and Offshore Structures. Cambridge University Press.


Seattle Angina Questionnaire

The Seattle Angina Questionnaire (SAQ) is a 19-item self-report measure that evaluates the frequency and severity of angina symptoms, functional limitations, and disease-specific quality of life in patients with coronary artery disease. Developed by Spertus and colleagues in 1995, the SAQ has become the gold-standard symptom-specific QoL instrument in cardiology and is recommended by major guidelines for assessing angina burden and treatment response.

Spertus, J. A., Winder, J. A., Dewhurst, T. A., Deyo, R. A., Prodzinski, J., McDonell, M., & Fihn, S. D. (1995). Development and evaluation of a health-related quality of life measure for men with erectile dysfunction. Journal of the American College of Cardiology, 25(2), 149–155.

kansas-city-cardiomyopathy minnesota-heart-failure duke-activity-status-index new-york-heart-association-class


SECA

SECA (Simultaneous Evaluation of Criteria and Alternatives) is a ranking multi-criteria decision-making (MCDM) method introduced by Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Simultaneous weight derivation and ranking (objective optimisation)

Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., Antucheviciene, J. (2018). Simultaneous evaluation of criteria and alternatives (SECA) for multi-criteria decision-making. Informatica


Second-Order Reliability Method

The Second-Order Reliability Method (SORM) is an extension of FORM that improves failure probability estimates by accounting for the curvature of the limit-state surface at the design point. Introduced by Fiessler, Neumann, and Rackwitz in 1979, SORM provides more accurate approximations for nonlinear failure surfaces while remaining computationally efficient. It has become the standard refinement when FORM accuracy is insufficient.

Fiessler, B., Neumann, H. J., & Rackwitz, R. (1979). Quadratic limit states in structural reliability. Journal of the Engineering Mechanics Division, 105(4), 661-676.

Breitung, K. (1984). Asymptotic approximations for multinormal integrals. Journal of Engineering Mechanics, 110(3), 357-366.

Hohenbichler, M., & Rackwitz, R. (1988). Improvement of second-order reliability estimates by importance sampling. Journal of Engineering Mechanics, 114(12), 2195-2199.

Melchers, R. E. (2002). Structural Reliability Analysis and Prediction (2nd ed.). John Wiley & Sons.

first-order-reliability-method rainflow-counting highly-accelerated-life-testing response-surface-desirability-function


Secondary Traumatic Stress Scale

Occupational trauma exposure and clinician burnout

The STSS is a 17-item self-report scale measuring secondary traumatic stress (STS)—trauma-related symptoms experienced by professionals exposed to others' trauma through their work. Developed by Bride and colleagues in 2004, the STSS operationalizes the concept of secondary traumatic stress disorder, a recognized occupational health concern affecting mental health professionals, physicians, first responders, and others in trauma-exposed occupations. The scale is used for occupational health screening, research on clinician burnout, and organizational assessment of workplace trauma exposure.

Bride, B. E., Robinson, M. M., Edwards, B., & Lochner, B. (2004). Development and validation of the Secondary Traumatic Stress Scale. Journal of Traumatic Stress, 17(3), 231-239.

Figley, C. R. (1995). Compassion fatigue: Coping with secondary traumatic stress disorder in those who treat the traumatized. Brunner/Mazel.


Secure Multi-Party Computation

MPC
Multi-Party Computation
Privacy-Preserving Computation
Güvenli Çok Taraflı Hesaplama

Secure Multi-Party Computation (SMPC) is a cryptographic paradigm that enables two or more parties to jointly compute a function over their private inputs without revealing those inputs to one another. Introduced by Andrew Yao in 1982 through his seminal garbled-circuit construction, SMPC provides provable privacy guarantees grounded in computational hardness assumptions. It underpins modern privacy-preserving data analysis, enabling collaborative computation on sensitive datasets in finance, healthcare, and machine learning.

Polynomial in input size under honest-but-curious model

Semi-honest and malicious adversary variants

Yao, A. C. (1982). Protocols for secure computations. 23rd Annual Symposium on Foundations of Computer Science, 160–164.


SED Fitting

Spectral Energy Distribution Fitting for Galaxy Properties

SED Analysis
Spectral Energy Distribution Method
Photometric Redshift

Spectral Energy Distribution (SED) fitting is the technique of comparing observed photometric measurements of galaxies across many wavelengths against theoretical predictions from stellar population synthesis models. By fitting models to observations, astronomers estimate galaxy properties including redshift, mass, age, star formation rate, and dust content without requiring expensive spectroscopic observations.

Bruzual, G., & Charlot, S. (2003). Stellar population synthesis at arbitrary metallicity with the Bruzual & Charlot models. Monthly Notices of the Royal Astronomical Society, 344(3), 1000-1028.

Conroy, C. (2009). Modeling the panchromatic SED evolution of galaxies. The Astrophysical Journal, 699(1), 486-506.

Arnouts, S., et al. (2007). Photometric redshifts from CFHTLS using 13-band photometry. Astronomy & Astrophysics, 476(1), 137-150.


Seed Germination Test

Standard Seed Germination Testing and Viability Assessment

Germination assay
Seed vigor test
Seed quality evaluation

International Seed Testing Association (ISTA)

Seed Germination Test is an analytical and physiological pipeline for assessing seed viability and germination rate under controlled conditions. Standardized by ISTA (International Seed Testing Association), this method quantifies the proportion of seeds capable of normal seedling development and informs seed quality certification, planting decisions, and storage management.

International Seed Testing Association (2015). International Rules for Seed Testing. Zurich, Switzerland.

Bewley, J. D., Bradford, K. J., Hilhorst, H. W., & Nonogaki, H. (2013). Seeds: Physiology of development, germination and dormancy (3rd ed.). Springer, New York.

crop-growth-simulation crop-yield-estimation phenological-observation nitrogen-use-efficiency soil-fertility-management


Seemingly Unrelated Regression

seemingly unrelated regression equations

Seemingly Unrelated Regressions, introduced by Arnold Zellner in 1962, is a system regression method that estimates several linear equations jointly when their error terms are correlated across equations. By exploiting that cross-equation correlation through generalized least squares, it is more efficient than estimating each equation separately by OLS.

Feasible generalized least squares (FGLS)

Zellner, A. (1962). An Efficient Method of Estimating Seemingly Unrelated Regressions and Tests for Aggregation Bias. Journal of the American Statistical Association, 57(298), 348-368.

ols-regression three-stage-least-squares two-stage-least-squares panel-fixed-effects system-gmm


Segment Anything Model

A Foundation Model for Image Segmentation

Deep Learning, Image Segmentation, Foundation Models

Segment Anything Model (SAM) is a foundation model introduced by Kirillov et al. in 2023 that can segment any object in an image given various forms of prompts. SAM is trained on a massive dataset of diverse images and learns to segment objects based on minimal user input such as points, boxes, or text descriptions.

Kirillov, A., Mintun, E., Darrell, T., & Girshick, R. (2023). Segment Anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4015-4026).

swin-transformer detr masked-autoencoders vision-transformer


SegRNN

SegRNN (Segment Recurrent Neural Network)

Segment RNN
Segment Recurrent Neural Network
SegRNN forecaster
Bölümlü Tekrarlayan Sinir Ağı

SegRNN is a recurrent neural network architecture for long-term time series forecasting proposed by Shengsheng Lin et al. in 2023. Instead of processing one time step at a time, SegRNN partitions input sequences into fixed-length segments and feeds each segment as a single token into a GRU. This segment-based design drastically reduces the number of recurrent iterations, addressing the well-known difficulty RNNs face when modeling very long dependencies over many individual steps.

Segment-based recurrent forecasting model

Input: Univariate or multivariate time series

Output: Multi-step ahead point forecasts

Lin, S., Lin, W., Wu, W., Zhao, F., Mo, R., & Zhang, H. (2023). SegRNN: Segment recurrent neural network for long-term time series forecasting. arXiv preprint.


SEIR Model

The SEIR model is a deterministic compartmental model that partitions a closed population into four epidemiological states: Susceptible (S), Exposed (E), Infectious (I), and Recovered (R). It extends the classic SIR framework by explicitly incorporating a latent period during which individuals have been infected but are not yet infectious. The model was systematically formalized by Anderson and May (1991) and remains a cornerstone of mathematical epidemiology for diseases with non-negligible incubation periods.

Anderson, R. M., & May, R. M. (1991). Infectious Diseases of Humans: Dynamics and Control. Oxford University Press.


Seismic Full-Waveform Inversion

Seismic Full-Waveform Inversion (FWI) is a computational technique that reconstructs detailed subsurface velocity and impedance models by iteratively fitting synthetic seismic waveforms to observed data. Introduced by Albert Tarantola in 1984, FWI has become the leading method for high-resolution imaging in exploration geophysics, engineering seismology, and subsurface characterization.

Seismic imaging and model parameterization technique

Tarantola, A. (1984). Inversion of seismic reflection data in the acoustic approximation. Geophysics, 49(8), 1259-1266.

Virieux, J., & Operto, S. (2009). An overview of full waveform inversion in exploration geophysics. Geophysics, 74(6), WCC1-WCC26.


Seismic Reflection Interpretation

seismic interpretation
seismic data analysis

Seismic reflection interpretation is the process of extracting meaningful geological information from seismic survey data, which is collected by recording elastic waves reflected from rock layers beneath the surface. Developed and systematized in the mid-20th century, this method is foundational in petroleum exploration and engineering geology. It enables geoscientists to image subsurface structures, identify hydrocarbon prospects, and assess hazards without drilling.

Yilmaz, Ö. (2001). Seismic Data Analysis: Processing, Inversion, and Interpretation of Seismic Data. Society of Exploration Geophysicists.

Sheriff, R. E., & Geldart, L. P. (2002). Exploration Seismology (2nd ed.). Cambridge University Press.

Brown, A. R. (2011). Interpretation of Three-Dimensional Seismic Data (7th ed.). American Association of Petroleum Geologists.

well-log-analysis stratigraphic-correlation geophysical-inversion geologic-mapping petrographic-analysis


Selected Area Electron Diffraction

SAED
electron diffraction pattern
TEM diffraction

Selected Area Electron Diffraction (SAED) is a crystallographic technique in transmission electron microscopy that obtains electron diffraction patterns from micron-sized or sub-micron crystalline regions. Developed from fundamental principles of electron wave behavior and integrated into TEM instruments by the mid-20th century, SAED enables direct observation of reciprocal space, crystal symmetry, and defect structures with spatial resolution unattainable by X-ray diffraction. It is essential for studying local crystal structure, phase identification, and characterizing nanoscale materials.

Williams, D. B., & Carter, C. B. (2009). Transmission Electron Microscopy: A Textbook for Materials Science (2nd ed.). Springer.

Cullity, B. D., & Stock, S. R. (2014). Elements of X-ray Diffraction (3rd ed.). Pearson Education.

Hirsch, P. B., Howie, A., Nicholson, R. B., Pashley, D. W., & Whelan, M. J. (1977). Electron Microscopy of Thin Crystals (2nd ed.). Butterworths.


Selection Sweep (Tajima's D)

Selective Sweep Detection using Tajima's D Statistic

Tajima's D test
Selective sweep analysis
Neutrality test

Tajima's D is a statistical test designed to detect selective sweeps—recent, rapid fixation of advantageous mutations—from patterns of genetic variation in DNA sequences. Developed by Fumio Tajima in 1989, this test measures deviations from neutrality by comparing different measures of DNA sequence diversity. A significant Tajima's D value indicates departure from neutral evolution, suggesting positive selection, population structure, or demographic events.

Tajima, F. (1989). Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics, 123(3), 585–595.

Braverman, J. M., Hudson, R. R., Kaplan, N. L., Langley, C. H., & Stephan, W. (1995). The hitchhiking effect on the site frequency spectrum of DNA polymorphisms. Genetics, 140(2), 783–796.

Fay, J. C., & Wu, C. I. (2000). Hitchhiking under positive Darwinian selection. Genetics, 155(3), 1405–1413.

coalescent-theory mcdonald-kreitman-test hka-test f-statistics


Selective Coding

focused coding
theoretical integration
GT selective coding
core category coding

1967 (Glaser & Strauss); refined 1990 (Strauss & Corbin)

Barney Glaser & Anselm Strauss (classic GT); systematised by Anselm Strauss & Juliet Corbin; constructivist variant by Kathy Charmaz

Selective coding is the third and final analytic phase of grounded theory, in which the researcher systematically identifies one central or core category that integrates all other major categories developed during open and axial coding. The outcome is a coherent, data-grounded substantive theory that explains the main social process or phenomenon under study. First formalized by Glaser and Strauss (1967) and later elaborated by Strauss and Corbin (1990) and Kathy Charmaz (2006), selective coding transforms fragmented mid-level categories into a unified theoretical account.

Interview transcripts, field notes, documents, observational records

15–40 participants (until theoretical saturation)

Strauss, A., & Corbin, J. (1990). Basics of Qualitative Research: Grounded Theory Procedures and Techniques. Sage.

Charmaz, K. (2006). Constructing Grounded Theory: A Practical Guide Through Qualitative Analysis. Sage.

grounded-theory thematic-analysis content-analysis narrative-analysis phenomenology case-study


Self-Attention

Multi-Head Self-Attention (Transformer Core)

Öz-Dikkat ve Çok Başlı Dikkat (Multi-Head Self-Attention)

Multi-head self-attention, introduced by Vaswani and colleagues in 2017, is the mechanism that lets every position in a sequence compute its relationship to all other positions in parallel. It is the core of the Transformer architecture and the foundation underneath BERT, GPT, and T5.

Classification, prediction, representation learning on text

Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.

Devlin, J. et al. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL.

bert-finetuning gpt-finetuning lora-peft random-forest xgboost


Self-Compassion Scale Short Form

Filip Raes, Kristin D. Neff, and colleagues at Leuven University

The Self-Compassion Scale Short Form (SCS-SF) is a 12-item self-report instrument measuring self-compassion, a construct closely related to mindfulness emphasizing how individuals respond to personal suffering and failure with kindness and understanding. Developed by Raes, Neff, and colleagues in 2011 and published in Mindfulness, the SCS-SF is a brief version of the original 26-item Self-Compassion Scale. The scale measures self-compassion through six dimensions: Self-Kindness, Self-Judgment, Common Humanity, Isolation, Mindfulness, and Over-Identification. The SCS-SF has become a standard measure in psychological research on self-compassion, emotion regulation, mental health, and the mechanisms underlying mindfulness-based interventions.

Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the Self-Compassion Scale. Mindfulness, 2(4), 207-216.

five-facet-mindfulness-questionnaire freiburg-mindfulness-inventory philadelphia-mindfulness-scale cognitive-and-affective-mindfulness toronto-mindfulness-scale


Self-Compassion Scale

The Self-Compassion Scale (SCS) is a 26-item measure assessing self-compassion—the capacity to relate to oneself with kindness and understanding, rather than harsh self-judgment, in the face of difficulty or failure. Developed by Kristin Neff in 2003, the SCS operationalizes self-compassion as a multidimensional construct involving self-kindness (versus self-criticism), common humanity (versus isolation), and mindfulness (versus over-identification with negative emotions). The instrument has become standard in clinical, positive psychology, and health psychology research examining resilience, well-being, and mental health.

Mindful self-kindness and psychological resilience measure

Neff, K. D. (2003). The development and validation of a scale to measure self-compassion. Self and Identity, 2(3), 223–250.

Neff, K. D. (2016). Self-compassion: The proven power of being kind to yourself. William Morrow.

Ates, Z., & Blacker, J. (2019). Well-being and resilience: Does self-compassion matter? British Journal of Guidance & Counselling, 47(2), 214–228.


Basic Psychological Needs Scale

Martin Gagné, Edward L. Deci, and Richard M. Ryan

The Basic Psychological Needs Questionnaire (BPNQ), developed by Gagné (2003) and grounded in Self-Determination Theory by Deci and Ryan, measures satisfaction of three fundamental human psychological needs: Autonomy, Competence, and Relatedness. According to Self-Determination Theory, these three needs are universally necessary for psychological health, well-being, and intrinsic motivation across all life domains. The 21-item BPNQ assesses the extent to which an individual perceives these needs are being met in their current context. It is widely used in research examining motivation, well-being, mental health, exercise engagement, work satisfaction, education, and psychotherapy effectiveness.

Gagné, M. (2003). The role of autonomy support and autonomy orientation in prosocial behavior engagement. Motivation and Emotion, 27(3), 199-223.

Deci, E. L., & Ryan, R. M. (2000). The 'what' and 'why' of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268.


Gbenga Ogedegbe, Antoinette Schoenthaler, and colleagues

The Self-Efficacy for Appropriate Medication Use Scale (SEAMS) is a brief self-report measure designed to assess patients' confidence in their ability to manage medications appropriately across diverse contexts and challenges. Grounded in Bandura's self-efficacy theory, the SEAMS evaluates patients' perceived capacity to adhere to medication regimens despite potential barriers—forgetfulness, side effects, cost constraints, complexity, or changes in routine. The scale has demonstrated strong predictive validity for medication adherence and clinical outcomes in hypertension, diabetes, asthma, and other chronic diseases, making it valuable for identifying patients with low medication management confidence who need additional support.

Ogedegbe, G., Schoenthaler, A., & Richardson, T. (2007). An Exploration of Contextual Factors and Antihypertensive Medication Adherence in Hypertensive African Americans. American Journal of Health-System Pharmacy, 64(23), 2510-2516. (SEAMS adapted from original research on self-efficacy in medication adherence.)


Self-Organized Criticality

SOC
Sandpile Model
Critical Self-Organization
Kendiliğinden Örgütlenen Kritiklik

Self-Organized Criticality (SOC) is a dynamical systems framework introduced by Per Bak, Chao Tang, and Kurt Wiesenfeld in 1987 to explain how large, dissipative systems spontaneously evolve toward a critical state without external fine-tuning. At the critical state, the system produces scale-invariant fluctuations — avalanches whose size and duration follow power-law distributions — and generates 1/f (pink) noise in its power spectrum.

Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of 1/f noise. Physical Review Letters, 59(4), 381–384.


Self-Organizing Map

SOM
Kohonen map
Kohonen network
öz-örgütlemeli harita

A self-organizing map is an unsupervised neural network, introduced by Teuvo Kohonen in 1982, that projects high-dimensional data onto a low-dimensional (usually two-dimensional) grid of prototype vectors while preserving the data's topology — nearby inputs map to nearby grid cells. It is used for visualization, clustering, and exploratory analysis, turning complex data into an ordered, interpretable map.

Unsupervised neural network for topology-preserving mapping

Output: Low-dimensional (usually 2D) ordered grid of prototypes

Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69.

Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464–1480.

k-means-clustering t-sne umap locally-linear-embedding


Self-Plagiarism and Text Recycling

Self-Plagiarism and Text Recycling: Reusing One's Own Previously Published Work Without Disclosure

text recycling
self-copying
duplicate publication
redundant publication

Self-plagiarism, or text recycling, occurs when an author reuses substantial portions of their own previously published work in a new publication without disclosure or acknowledgment. This includes republishing the same article in different venues, duplicating methods sections across multiple papers, or reusing discussion sections. While the intellectual property is the author's own, self-plagiarism is considered misconduct because it violates the principle that published work represents new research and it may inflate publication counts.

Roig, M. (2015). Avoiding plagiarism, self-plagiarism, and other questionable writing practices: A guide to ethical writing. U.S. Department of Health and Human Services Office of Research Integrity.

Research Integrity Journal. (2022). Salami publishing and duplicate submission: A systematic review. Research Integrity and Peer Review, 8, 1-12.

International Committee of Medical Journal Editors (ICMJE). (2023). Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals. Journal of the American Medical Association, 330(6), 567-575.


Self-Stigma of Seeking Help Scale

The Self-Stigma of Seeking Help Scale (SSSH) is a 10-item self-report measure assessing the degree to which individuals experience shame, embarrassment, or fear of judgment related to seeking psychological or mental health help. Developed by David L. Vogel, Nathan G. Wade, and Stephanie Haake in 2006, the SSSH captures self-directed stigma about help-seeking—the belief that seeking help is shameful or will lead to negative judgments by others. The scale is used in research on mental health literacy, treatment-seeking behavior, and barriers to care across diverse populations.

Vogel, D. L., Wade, N. G., & Haake, S. (2006). Measuring the self-stigma associated with seeking psychological help. Journal of Counseling Psychology, 53(3), 325-337.

internalized-stigma-mental-illness link-stigma-scale recovery-assessment-scale mental-health-recovery-measure


Self-supervised Active Learning

Self-supervised Active Learning (SSL-AL hybrid label-efficient framework)

semi-supervised active learning with self-supervision

Bengar et al. and concurrent works (multiple groups)

Self-supervised Active Learning (SSL-AL) is a label-efficient machine-learning paradigm that pre-trains a model on unlabeled data using self-supervised objectives, then strategically queries a human oracle for the most informative labels using an active-learning acquisition function. The result is strong predictive performance with a fraction of the annotation cost required by fully supervised approaches.

Hybrid active-learning and self-supervised pre-training framework

Images, text, or structured data with very few labeled examples

Bengar, J. Z., van de Weijer, J., Twardowski, B., & Raducanu, B. (2021). Reducing Label Effort: Self-Supervised Meets Active Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 1631–1639.

Zhan, X., Wang, Q., Huang, K.-H., Xiong, H., Dou, D., & Chan, A. B. (2022). A comparative survey of deep active learning. arXiv preprint arXiv:2203.13450.

active-learning self-supervised-learning semi-supervised-learning contrastive-learning transfer-learning label-propagation


Self-supervised Autoencoder Anomaly Detection (Pretext-Task Reconstruction-Based Anomaly Detection)

self-supervised reconstruction anomaly detection

pretext-task autoencoder anomaly detection

contrastive autoencoder anomaly detection

Golan & El-Yaniv; broader self-supervised anomaly detection community

Self-supervised autoencoder anomaly detection trains an autoencoder using self-supervised pretext tasks — such as predicting geometric transformations or solving jigsaw puzzles — on unlabeled normal data, then flags as anomalous any input whose reconstruction error or pretext-task score deviates substantially from the learned normal distribution.

Unsupervised / self-supervised deep learning

Unlabeled data (images, tabular, time-series)

Golan, I. & El-Yaniv, R. (2018). Deep one-class classification via geometric transformations. Advances in Neural Information Processing Systems (NeurIPS), 31.

Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Müller, K.-R. (2021). A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 109(5), 756–795.


Self-supervised BERT-based Text Classification (Pretrain then Fine-tune)

masked LM pretraining with classification head

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)

Self-supervised BERT-based classification uses Google's Bidirectional Encoder Representations from Transformers (BERT), pretrained on massive unlabelled text via masked-language modelling, and fine-tunes it on labelled examples to assign text into categories. It consistently achieves state-of-the-art accuracy on sentiment analysis, topic classification, intent detection, and similar NLP tasks even with limited labelled data.

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019, 4171–4186. Association for Computational Linguistics.

Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019). How to Fine-Tune BERT for Text Classification? In China National Conference on Chinese Computational Linguistics (CCL 2019), LNCS 11856, 194–206. Springer.

bert-pretraining transformer-encoder roberta text-cnn distilbert transfer-learning-nlp


Self-supervised Boosting

SSL boosting
self-supervised ensemble boosting
pretext-task boosting
SSL-Boost

Self-supervised boosting integrates self-supervised pretext tasks into the boosting framework — covering AdaBoost, gradient boosting, and their modern variants — to leverage large pools of unlabeled data. By first learning feature representations from unlabeled samples and then running sequential weak-learner ensembles on pseudo-labeled data, it achieves competitive accuracy even when ground-truth labels are scarce.

Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (pp. 189–196). ACL.

self-supervised-gradient-boosting self-supervised-learning semi-supervised-boosting boosting xgboost active-learning-boosting


Self-supervised convolutional neural network

Self-Supervised Convolutional Neural Network

Self-supervised CNN
SSL-CNN
contrastive CNN
pretext-task CNN

LeCun, Y. (CNN backbone); Chen et al. and He et al. (self-supervised visual frameworks)

A self-supervised convolutional neural network (CNN) learns powerful visual representations from unlabeled images by solving pretext tasks — such as contrastive instance discrimination or masked-patch prediction — and then fine-tunes on a small labeled set. This approach dramatically reduces dependence on large annotated datasets while preserving the spatial feature-extraction strengths of convolutional architectures.

Unlabeled images; small labeled fine-tuning set

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119, 1597–1607.

He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), 9729–9738.


Self-supervised DBSCAN

Self-supervised Representation Learning with DBSCAN Clustering

SSL-DBSCAN
self-supervised density clustering
contrastive DBSCAN
representation-based DBSCAN

Ester et al. (DBSCAN base); pipeline pattern established in multiple works c. 2018–2021

Self-supervised DBSCAN is a two-stage unsupervised pipeline that first trains a neural encoder on a pretext task — such as contrastive learning or masked reconstruction — to produce compact, semantically meaningful embeddings from unlabeled data, and then applies DBSCAN in the resulting embedding space to discover arbitrarily shaped clusters without requiring any class labels.

Two-stage pipeline (self-supervised pre-training + density-based clustering)

Unlabeled high-dimensional data (images, text, tabular)

Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press.

Zhan, X., Liu, Z., Luo, P., Tang, X., & Loy, C. C. (2018). Rethinking deep neural network training for face recognition: A geometric approach. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2045–2054.

dbscan self-supervised-learning hdbscan gaussian-mixture-model k-means semi-supervised-dbscan


Self-supervised Decision Tree

SSL decision tree
self-supervised tree classifier
pseudo-label decision tree
unsupervised-guided decision tree

Multiple authors (active research area, 2010s–2020s)

Self-supervised Decision Tree learning combines the interpretability of classical decision trees with the ability to exploit large quantities of unlabeled data through self-supervised pretext tasks. The model learns useful feature representations or node-split criteria from unlabeled samples before refining predictions on a small labeled set, bridging the gap between fully supervised trees and purely unsupervised clustering.

Self-supervised ensemble/single tree model

Tabular, mixed; large unlabeled datasets with small labeled subsets

decision-tree random-forest semi-supervised-learning contrastive-learning gradient-boosting label-propagation


Self-supervised Diffusion Model

Self-supervised Diffusion Model (Denoising Diffusion with Self-supervised Representation Learning)

denoising diffusion with self-supervised pretraining

Ho, J. et al.; extended by Chen, T. et al. and subsequent self-supervised diffusion works

A self-supervised diffusion model couples the iterative noise-and-denoise generative process of denoising diffusion probabilistic models with a self-supervised representation learning objective — such as contrastive or masked prediction loss — so that the model simultaneously learns to generate realistic data and to produce semantically meaningful representations without any labeled examples.

Generative model with self-supervised representation objective

Images, audio, video, sequential data (unlabeled)

Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851.

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), 119, 1597–1607.

denoising-diffusion-probabilistic-model score-matching contrastive-learning masked-autoencoder variational-autoencoder generative-adversarial-network


Self-supervised Federated learning

Self-supervised Learning in Federated Settings

FedSSL
Federated Self-supervised Learning
Federated Contrastive Learning
Self-supervised Federated Pretraining

McMahan et al. (federated); Zhuang et al. and others (federated SSL combination)

Self-supervised Federated Learning combines federated training — where data never leaves local devices — with self-supervised pretext tasks such as contrastive learning or masked prediction. Clients learn general-purpose representations from their own unlabeled data and share only model updates, not raw data, with a central server that aggregates them into a global encoder.

Federated self-supervised pretraining paradigm

Unlabeled distributed data (images, text, sensor readings)

Zhuang, W., Wen, Y., & Zhang, S. (2021). Divergence-aware Federated Self-Supervised Learning. In International Conference on Learning Representations (ICLR 2022).

self-supervised-learning federated-learning transfer-learning semi-supervised-learning contrastive-learning few-shot-learning


Self-supervised Few-shot Learning

SSL-FSL
self-supervised meta-learning
unsupervised few-shot learning
self-supervised prototypical learning

Gidaris, S. et al.; Su, J.-C. et al. (concurrent seminal works)

Self-supervised Few-shot Learning (SSL-FSL) combines self-supervised pretraining on large unlabeled corpora with few-shot meta-learning so that a model can recognize new categories from only a handful of labeled examples. By learning rich, transferable representations without expensive annotation, SSL-FSL addresses the fundamental bottleneck of supervised few-shot methods: the need for labeled support data at scale.

Hybrid learning paradigm (self-supervised pretraining + few-shot adaptation)

Image, text, or multimodal data with abundant unlabeled and scarce labeled examples

Gidaris, S., Bursuc, A., Komodakis, N., Perez, P., & Cord, M. (2019). Boosting Few-Shot Visual Learning with Self-Supervision. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 8059–8068.

Su, J.-C., Maji, S., & Hariharan, B. (2020). When Does Self-Supervision Improve Few-Shot Learning? European Conference on Computer Vision (ECCV), Lecture Notes in Computer Science, vol 12371, 645–660.

contrastive-learning meta-learning prototypical-networks siamese-network transfer-learning maml


Self-supervised GAN

Self-supervised Generative Adversarial Network

GAN with self-supervised auxiliary tasks

Self-supervised GAN augments a standard Generative Adversarial Network with one or more self-supervised auxiliary tasks — such as predicting image rotation or patch position — that stabilise adversarial training and yield a discriminator that learns rich, transferable representations from unlabeled data without requiring manual annotations.

Generative model with self-supervised auxiliary tasks

Images, unlabeled or minimally labeled data

Chen, T., Zhai, X., Ritter, M., Lucic, M., & Houlsby, N. (2019). Self-Supervised GANs via Auxiliary Rotation Loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12154–12163.

Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2021). Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1), 857–876.


Self-supervised Gaussian Mixture Model

SS-GMM
self-supervised GMM
semi-supervised Gaussian mixture model
self-supervised clustering with GMM

Multiple authors (Zhai et al., 2019; earlier formulations in semi-supervised GMM literature)

A Self-supervised Gaussian Mixture Model (SS-GMM) combines self-supervised representation learning with a probabilistic Gaussian mixture prior to discover meaningful clusters in unlabeled or partially labeled data. By leveraging pretext tasks to learn rich embeddings before fitting a GMM, it achieves cluster quality that standard GMMs applied to raw features rarely reach, especially on complex image, text, or biological data.

Probabilistic generative model with self-supervised pretraining

Continuous or high-dimensional unlabeled (plus optional labeled) data

Zhai, X., Oliver, A., Kolesnikov, A., & Beyer, L. (2019). S4L: Self-supervised semi-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 1476–1485.

gaussian-mixture-model variational-autoencoder deep-clustering expectation-maximization contrastive-learning semi-supervised-learning


Self-supervised Gaussian Process

self-supervised Gaussian process regression

Fortuin, V. et al.; broader self-supervised GP literature

Self-supervised Gaussian Process (SSL-GP) combines the principled uncertainty quantification of Gaussian processes with self-supervised pretraining, learning expressive kernels or latent representations from unlabeled data before fitting a GP on a small labeled set. This makes the approach especially powerful in low-labeled-data regimes where a conventional GP would overfit or produce poorly calibrated uncertainty estimates.

Probabilistic model (self-supervised GP pretraining + kernel learning)

Continuous, time-series, tabular, partially labeled datasets

Fortuin, V., Rätsch, G., & Mandt, S. (2020). GP-VAE: Deep probabilistic time series imputation using Gaussian process variational autoencoders. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 108, 1651–1661.

gaussian-process semi-supervised-gaussian-process self-supervised-learning bayesian-gaussian-process variational-autoencoder active-learning-gaussian-process


Self-supervised Gradient Boosting

Self-supervised Gradient Boosting (SSL-GBM)

SSL gradient boosting
self-supervised boosting
semi-supervised gradient boosting
SSL-GBM

Various researchers (Zhang et al. and others)

Self-supervised gradient boosting extends the classic gradient boosting framework by incorporating self-supervised pretext tasks to exploit unlabeled data. The model first learns useful feature representations from unannotated samples, then uses those representations to guide the sequential ensemble of weak learners, achieving strong predictive performance even when labeled examples are scarce.

Ensemble (self-supervised + gradient boosting)

Tabular data, mixed labeled and unlabeled

Zhang, Y., Zhang, J., & Yang, Q. (2022). Self-Supervised Gradient Boosting for Semi-Supervised Learning on Tabular Data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

xgboost random-forest semi-supervised-learning lightgbm gradient-boosting contrastive-learning


Self-supervised GRU

SS-GRU
Self-supervised Gated Recurrent Unit
GRU with self-supervised pretraining
Unsupervised GRU pretraining

Cho, K. et al. (GRU); self-supervised training paradigm from broader SSL literature

Self-supervised GRU trains a Gated Recurrent Unit network using automatically constructed supervision signals — such as next-step prediction or masked token recovery — derived from the unlabeled data itself. The learned sequence representations are then fine-tuned on small labeled datasets, making high-quality sequential modeling feasible when annotations are scarce.

Sequential / time-series / text (unlabeled or partially labeled)

Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014.

Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2023). Self-Supervised Learning: Generative or Contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1), 857–876.


Self-supervised Image Classification

Self-supervised Learning for Image Classification

contrastive visual representation learning

unsupervised pretraining for image classification

Chen et al. (SimCLR); He et al. (MoCo); Grill et al. (BYOL); Caron et al. (DINO)

Self-supervised image classification trains a deep visual encoder on large unlabeled image datasets by solving proxy tasks — such as predicting which two augmented views of the same image are similar — and then fine-tunes only a lightweight classifier head on labeled examples. Pioneered by frameworks such as SimCLR and MoCo around 2020, it drastically reduces the need for expensive manual annotation while achieving accuracy rivaling fully supervised models.

Unlabeled or partially labeled image datasets

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 1597–1607.

He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020). Momentum Contrast for Unsupervised Visual Representation Learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9729–9738.

convolutional-neural-network vision-transformer transfer-learning contrastive-learning generative-adversarial-network knowledge-distillation


Self-supervised Instance Segmentation

Self-supervised Instance Segmentation (Label-free Object Mask Learning)

SSIS
unsupervised instance segmentation
label-free instance segmentation
self-supervised mask prediction

Wang et al. (FreeSOLO); Caron et al. (DINO)

Self-supervised instance segmentation learns to detect and delineate individual object instances in images without any human-annotated masks or bounding boxes. Instead of relying on costly pixel-level labels, it exploits self-supervised pretraining, multi-view consistency, and pseudo-label generation to discover and segment objects purely from raw image data.

Self-supervised deep learning for pixel-level object delineation

Wang, X., Zhu, Z., Cao, G., Yao, Z., Jiang, Z., & Ye, J. (2022). FreeSOLO: Learning to Segment Objects without Annotations. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14176–14186.

Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021). Emerging Properties in Self-Supervised Vision Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 9650–9660.

instance-segmentation semantic-segmentation self-supervised-learning contrastive-learning vision-transformer panoptic-segmentation


Self-supervised Isolation Forest

Self-supervised Isolation Forest (SSL-augmented Anomaly Detection)

SSL Isolation Forest
self-supervised iForest
semi-supervised isolation forest
contrastive isolation forest

Liu, F. T., Ting, K. M., & Zhou, Z.-H. (iForest); SSL extensions by multiple authors

Self-supervised Isolation Forest augments the classic Isolation Forest anomaly detector with a self-supervised pre-training stage. A pretext task — such as predicting rotation, masked features, or contrastive pairs — is solved without labels to learn a richer feature representation, which is then used when building the isolation trees, yielding sharper anomaly scores on complex, high-dimensional tabular data.

Ensemble anomaly detector with self-supervised pre-training

Tabular, multivariate continuous or mixed features

Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation Forest. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM), pp. 413–422.

isolation-forest one-class-svm autoencoder local-outlier-factor contrastive-learning deep-svdd


Self-supervised K-means

self-supervised clustering with K-means
deep clustering with K-means
unsupervised K-means with pseudo-labels
SSL K-means

Caron, M. et al. (DeepCluster framework)

Self-supervised K-means is a clustering technique that combines K-means assignment with self-supervised representation learning. The model alternates between clustering unlabeled data points into K groups and using those cluster assignments as pseudo-labels to refine an underlying feature representation, yielding increasingly coherent clusters without any human-annotated ground truth.

Unlabeled data (tabular, image, or feature embeddings)

Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018). Deep Clustering for Unsupervised Learning of Visual Features. In Proceedings of the European Conference on Computer Vision (ECCV), 132–149.

k-means self-supervised-learning semi-supervised-k-means gaussian-mixture-model online-k-means ensemble-k-means


Self-supervised K-nearest neighbors

Self-supervised K-Nearest Neighbors (SSL-kNN)

Self-supervised K-nearest neighbors (SSL-kNN) combines representation learning without labels with a non-parametric k-NN classifier. A neural encoder is first trained via a self-supervised objective — such as contrastive or masked prediction — so that semantically similar samples cluster together in the embedding space. A simple k-NN lookup on those embeddings then assigns class labels, serving both as a lightweight probe and as a practical classifier.

Self-supervised + non-parametric classifier

Unlabeled or partially labeled feature vectors, images, or embeddings

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning (ICML), PMLR 119, 1597–1607.

Wu, Z., Xiong, Y., Yu, S. X., & Lin, D. (2018). Unsupervised feature learning via non-parametric instance discrimination. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3733–3742.

self-supervised-learning k-nearest-neighbors semi-supervised-k-nearest-neighbors transfer-learning contrastive-learning metric-learning


Self-supervised LDA Topic Model

Self-supervised Latent Dirichlet Allocation Topic Model

SSL-LDA
self-supervised topic modeling
self-supervised LDA
contrastive LDA

2003 (LDA); self-supervised variants from 2020

Blei, D. M., Ng, A. Y., Jordan, M. I. (LDA); self-supervised extension by multiple authors (2020s)

Self-supervised LDA combines the probabilistic generative framework of Latent Dirichlet Allocation with self-supervised pretraining signals — such as masked-word prediction or contrastive document objectives — to guide topic discovery without requiring hand-labeled training data. The result is topic representations that are simultaneously grounded in distributional statistics and enriched by language structure learned from raw text.

Unlabeled or minimally labeled text corpora

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.

Meng, Y., Huang, J., Zhang, Y., & Han, J. (2022). Topic Discovery via Latent Space Clustering of Pretrained Language Model Representations. Proceedings of WWW 2022, ACM.

lda-topic-model nmf-topic-model semi-supervised-lda-topic-model bert-based-classification sentence-embeddings topic-modeling


Self-supervised Learning

Self-supervised Learning (Pretext-task Representation Learning)

SSL
self-supervised pre-training
pretext-task learning
unsupervised representation learning

LeCun, Y. and community (formalized ~2018–2020)

Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.

Unlabeled data (images, text, audio, video, tabular)

LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/

transfer-learning semi-supervised-learning contrastive-learning few-shot-learning autoencoders representation-learning


Self-supervised LightGBM

Self-supervised Learning with LightGBM (Gradient Boosting with Self-supervised Pretraining)

SSL-LightGBM
self-supervised gradient boosting
pretraining LightGBM
pseudo-label LightGBM

Ke, G. et al. (LightGBM); self-supervised paradigm adapted from broader SSL literature

Self-supervised LightGBM combines the self-supervised learning paradigm with the LightGBM gradient boosting framework to exploit large volumes of unlabeled tabular data. A self-supervised pretext task — such as masked feature prediction or contrastive corruption — generates rich feature representations or pseudo-labels that are then used to train or fine-tune a LightGBM model, substantially improving performance in label-scarce regimes.

Hybrid (self-supervised pretraining + gradient boosting)

Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems, 30.

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Self-Supervised Learning. Proceedings of the 37th International Conference on Machine Learning (ICML).

lightgbm self-supervised-learning semi-supervised-lightgbm gradient-boosting xgboost transfer-learning


Self-supervised Logistic Regression

Self-supervised Representation Learning with Logistic Regression Classifier

contrastive pretraining with logistic classifier

Chen et al. (SimCLR linear evaluation protocol, 2020); logistic probe practice widely adopted across SSL literature

Self-supervised logistic regression is a two-stage pipeline in which a neural encoder is first trained on abundant unlabeled data through a self-supervised pretext task — such as contrastive learning or masked prediction — and then the frozen learned representations are classified with a standard logistic regression model trained on a small labeled dataset. This linear evaluation protocol is widely used to benchmark the quality of self-supervised representations.

Self-supervised pretraining + supervised linear classification

Unlabeled data for pretraining; small labeled set for logistic regression fine-tuning

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), 1597–1607.

van Engelen, J. E., & Hoos, H. H. (2020). A survey on semi-supervised learning. Machine Learning, 109(2), 373–440.

self-supervised-learning logistic-regression-ml semi-supervised-logistic-regression transfer-learning contrastive-learning self-supervised-decision-tree


Self-supervised Metric learning

self-supervised representation learning with metric loss

2020 (modern contrastive formulation); foundations 1990s–2000s

Chen, T. et al. (SimCLR); earlier metric learning foundations by Bromley, LeCun (1994)

Self-supervised metric learning trains a neural encoder to embed inputs so that semantically similar items lie close together in vector space, using automatically generated pseudo-labels instead of human annotations. By combining self-supervised pretext tasks with contrastive or triplet-based metric objectives, it produces transferable, label-efficient representations applicable to retrieval, clustering, and few-shot classification.

Self-supervised representation learning with metric objective

Unlabeled or partially labeled; images, text, audio, tabular embeddings

Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML 2020), PMLR 119, 1597–1607.

Khosla, P., Tian, Y., Wang, X., Liu, C., Krishnan, D., Isola, P., & Tian, Y. (2020). Supervised Contrastive Learning. Advances in Neural Information Processing Systems (NeurIPS 2020), 33, 18661–18673.

contrastive-learning siamese-network triplet-network self-supervised-learning representation-learning metric-learning


Self-supervised Naive Bayes

Self-supervised Naive Bayes (EM-augmented Generative Classifier)

Self-training Naive Bayes
EM Naive Bayes
Expectation-Maximization Naive Bayes
Pseudo-label Naive Bayes

Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T.

Self-supervised Naive Bayes extends the classic Naive Bayes classifier to exploit large pools of unlabeled data by iteratively assigning soft pseudo-labels through an Expectation-Maximization loop. Originally demonstrated for text classification by Nigam et al. (2000), the approach can substantially improve accuracy when labeled examples are scarce but unlabeled data are plentiful.

Labeled + unlabeled tabular or text data

Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39(2-3), 103–134.

Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-supervised Learning. MIT Press.

naive-bayes semi-supervised-naive-bayes self-supervised-learning semi-supervised-learning expectation-maximization self-supervised-logistic-regression