N-SPOTIS

N-SPOTIS (Neutrosophic extension of SPOTIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Abdel-aziem, A. H., Mohamed, H. K., Abdelhafeez, A. in 2023. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Abdel-aziem, A. H., Mohamed, H. K., Abdelhafeez, A. (2023). Neutrosophic Decision Making Model for Investment Portfolios Selection and Optimizing based on Wide Variety of Investment Opportunities and Many Criteria in Market. Neutrosophic Systems with Applications


N-TODIM

N-TODIM (Neutrosophic extension of TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Ji, P., Zhang, H. Y., Wang, J. Q. in 2018. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Neutrosophic outranking/ranking — Single-Valued Neutrosophic Set (SVNS: T, I, F; T,I,F ∈ [0,1], T+I+F ≤ 3)

Ji, P., Zhang, H. Y., Wang, J. Q. (2018). A projection-based TODIM method under multi-valued neutrosophic environments and its application in personnel selection. Neural Computing and Applications

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


N-TOPSIS

N-TOPSIS (Neutrosophic extension of TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Biswas, P., Pramanik, S., Giri, B. C. in 2016. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Biswas, P., Pramanik, S., Giri, B. C. (2016). TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing and Applications


N-VIKOR

N-VIKOR (Neutrosophic extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Bausys, R., Zavadskas, E. K. in 2015. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Tooranloo, Hossein Sayyadi, Ayatollah, Arezoo Sadat (2024). Neutrosophic VIKOR approach for multi-attribute group decision-making. Operations Research and Decisions


N-WASPAS

N-WASPAS (Neutrosophic WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Nie, R. Wang, J. Wang, T. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Single-valued neutrosophic extension of WASPAS

Nie, R., Wang, J., Wang, T. (2017). A hybrid multiple criteria decision making model for investment selection based on WASPAS method and fuzzy sets. Journal of Intelligent & Fuzzy Systems


N-WISP

N-WISP (Neutrosophic WISP) is a ranking multi-criteria decision-making (MCDM) method introduced by Stanujkić, D. Karabašević, D. Popović, G. Smarandache, F. Stanimirović, P. S. Saračević, M. Katsikis, V. N. in 2022. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Stanujkić, D., Karabašević, D., Popović, G., Smarandache, F., Stanimirović, P. S., Saračević, M., Katsikis, V. N. (2022). A Single Valued Neutrosophic Extension of the Simple WISP Method. Informatica 33(3):635-651


N-WPM

N-WPM (Neutrosophic extension of WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Ye, J. in 2014. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Ye, J. (2014). A multicriteria decision-making method using aggregation operators for simplified neutrosophic sets. Journal of Intelligent & Fuzzy Systems


N400/P600 Analysis

Event-Related Potential (ERP) Component Analysis: N400 and P600

ERP Analysis
Neurophysiological Semantics

N400/P600 Analysis is a neurocognitive method using electroencephalography (EEG) to measure event-related potentials (ERPs) that reflect brain responses to linguistic stimuli. The N400 component (a negative deflection at 400 ms) indexes semantic processing and surprise; the P600 component (a positive deflection at 600 ms) reflects syntactic processing and reanalysis. Discovered by Marta Kutas and Steven Hillyard in 1980, these components reveal the neural basis of language comprehension in real time.

Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: Brain potentials reflect semantic incongruity. Science, 207(4427), 203-205.

Osterhout, L., & Holcomb, P. J. (1992). Event-related brain potentials elicited by syntactic anomaly. Journal of Memory and Language, 31(6), 785-806.

Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique (2nd ed.). Cambridge, MA: MIT Press.


NAIADE

NAIADE (Novel Approach to Imprecise Assessment and Decision Environments) is a ranking multi-criteria decision-making (MCDM) method introduced by Munda, G. in 1995. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Fuzzy pairwise equity/inequality comparison (semantic distance)

Munda, G. (1995). Multicriteria Evaluation in a Fuzzy Environment: Theory and Applications in Ecological Economics. Physica-Verlag, Heidelberg


Naive Bayes

Naive Bayes Sınıflandırıcı
naive bayes classifier
simple Bayes
Gaussian Naive Bayes
Multinomial Naive Bayes

Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.

Probabilistic classifier (Bayes' theorem with conditional independence)

Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.

logistic-regression random-forest svm-classification decision-tree k-nearest-neighbors


Named Entity Recognition

NER
entity tagging
Adlandırılmış Varlık Tanıma (NER)

Named entity recognition (NER) is a natural-language-processing task that automatically detects and labels entities in text — such as people, organisations, locations, and dates. Surveyed by Nadeau and Sekine (2007) and later advanced with neural architectures by Lample et al. (2016), it turns free-running text into tagged spans that downstream tools can use.

Person / organisation / location / date and similar

Rule-based / machine-learning / neural sequence labelling

Output: Text spans tagged with entity type labels

Nadeau, D. & Sekine, S. (2007). A survey of named entity recognition. Lingvisticae Investigationes.

Lample, G. et al. (2016). Neural Architectures for Named Entity Recognition. NAACL.


Nanoindentation

nanoindentation
instrumented indentation
depth-sensing indentation

Nanoindentation, or instrumented indentation, is a technique for measuring the hardness and elastic modulus of materials by pressing a hard probe into a sample surface and continuously recording load and penetration depth. Developed by Oliver and Pharr in 1992, nanoindentation enables measurement of mechanical properties of thin films, small volumes, and nanoscale structures with spatial resolution approaching micrometers. It is the standard tool in materials science for characterizing coatings, interfaces, and mechanical properties at the submicron scale.

Oliver, W. C., & Pharr, G. M. (1992). An improved technique for determining hardness and elastic modulus using load and displacement sensing indentation experiments. Journal of Materials Research, 7(6), 1564-1583.

Fischer-Cripps, A. C. (2004). Nanoindentation (2nd ed.). Springer-Verlag.

Hay, J. L., & Crawford, B. (2011). Measuring substrate-independent modulus of thin films. Journal of Materials Research, 26(6), 727-738.


NANSON

NANSON (Nanson — iterative Borda elimination) is a aggregationoperator multi-criteria decision-making (MCDM) method introduced by Orakçı, E. in 2024. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Orakçı, E. (2024). Çok Kriterli Karar Verme Problemleri için Toplulaştırma Teknikleri. Özgür Yayınları

borda condorcet copeland dodgson topsis vikor ahp


NARDL Model

Nonlinear Autoregressive Distributed Lag Model

nonlinear ARDL
asymmetric ARDL
Doğrusal Olmayan ARDL (NARDL)

The NARDL model, introduced by Shin, Yu and Greenwood-Nimmo in 2014, extends the ARDL framework to capture asymmetric long-run and short-run relationships, testing whether positive and negative changes in a regressor affect the dependent variable differently.

Asymmetric cointegration / error-correction model

ARDL bounds testing with positive/negative partial sum decomposition

Shin, Y., Yu, B. & Greenwood-Nimmo, M. (2014). Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework. In: Sickles, R. & Horrace, W. (Eds.), Festschrift in Honor of Peter Schmidt. Springer.

ardl-model star-model system-gmm ols-regression quantile-regression


Narrative Analysis

narrative inquiry
life history analysis
biographical research
Anlatı Analizi (Narrative Analysis)

1967 (foundational); 2008 (canonical handbook)

Catherine Kohler Riessman (seminal synthesis, 2008); roots in Labov & Waletzky (1967)

Narrative analysis is a qualitative research method, synthesised canonically by Catherine Kohler Riessman (2008), that examines how individuals storise their lived experiences and construct meaning through the telling. Drawing on life history, biographical, and narrative inquiry traditions, it treats the story itself — not just its content — as the unit of analysis, attending to temporal sequence, plot structure, and the social context in which a narrative is produced.

In-depth interviews, life histories, biographical accounts, textual narratives

3 (exploratory); 10+ recommended for pattern analysis

Riessman, C.K. (2008). Narrative Methods for the Human Sciences. Sage.

phenomenology case-study discourse-analysis grounded-theory ethnography content-analysis


Narrative Inquiry

Narrative Analysis
Narrative Research
Life Story Method

Narrative inquiry is a qualitative research methodology that treats stories and life narratives as primary data, analyzing how individuals construct meaning and identity through storytelling. Developed by D. Jean Clandinin and F. Michael Connelly (2000), narrative inquiry examines the narratives people tell about their lives, experiences, and transitions, understanding that people make sense of experience through narrative.

Clandinin, D. J., & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. Jossey-Bass.

Lieblich, A., Tuval-Mashiach, R., & Zilber, T. (1998). Narrative research: Reading, analysis, and interpretation. Sage Publications.

Chase, S. E. (2005). Narrative inquiry: Multiple lenses, approaches, voices. In N. K. Denzin & Y. S. Lincoln (Eds.), The Sage handbook of qualitative research (3rd ed., pp. 651–679). Sage Publications.

phenomenological-research case-study-research life-history-method narrative-analysis restorying


Narrative Review

traditional review
expert review
unsystematic review
narrative synthesis

Pre-20th century practice; peer-reviewed methodological guidance from 2000s onward

Traditional academic practice; formalized discussion by Green, Johnson & Adams (2006)

A narrative review is a broad, author-directed synthesis of published literature on a topic, written to summarize, interpret, and contextualize existing knowledge without following the rigorous, pre-registered search and selection protocols that characterize systematic reviews. It draws on the author's expertise to weave disparate sources into a coherent account that identifies themes, debates, and directions for future research.

Published literature (articles, books, reports)

Green, B. N., Johnson, C. D., & Adams, A. (2006). Writing narrative literature reviews for peer-reviewed journals: secrets of the trade. Journal of Chiropractic Medicine, 5(3), 101–117.

Ferrari, R. (2015). Writing narrative style literature reviews. Medical Writing, 24(4), 230–235.

systematic-literature-review scoping-review integrative-review umbrella-review rapid-review bibliometric-analysis


NASA Task Load Index

The NASA Task Load Index (NASA-TLX) is a multidimensional subjective workload assessment tool developed by Sandra Hart and Lowell Staveland at NASA's Ames Research Center in 1988. It measures six dimensions of cognitive and physical task load to quantify operator workload across diverse task domains, from aviation and process control to human-computer interaction. The TLX has become the gold standard for workload measurement in human factors research and applied settings.

Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human Mental Workload (pp. 139-183). Elsevier Science Publishers.

cognitive-load-scale workload-profile situational-awareness-rating operator-performance-scale


NASA-TLX

The NASA Task Load Index (TLX) is a multi-dimensional subjective workload assessment tool developed at NASA Ames Research Center by Sandra Hart and Lowell Staveland in the 1980s. TLX measures perceived mental workload across six dimensions—mental demand, physical demand, temporal demand, performance, effort, and frustration—allowing researchers and practitioners to understand the cognitive and affective burden of tasks and interfaces. The instrument is widely used in human factors, cognitive engineering, and HCI to identify task bottlenecks and evaluate system designs.

Multi-dimensional post-task questionnaire for measuring subjective mental workload

Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of empirical and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human Mental Workload (pp. 139–183). Elsevier.

Hart, S. G. (1986). NASA Task Load Index. Moffett Field, CA: NASA Ames Research Center.

system-usability-scale think-aloud-protocol cognitive-walkthrough attrakdiff-ueq


Nash Equilibrium

Nash Equilibrium (Lemke-Howson Algorithm)

Lemke-Howson Equilibrium
Completely Labeled Pair

Nash Equilibrium is a game-theoretic solution concept where no player can unilaterally deviate to improve their payoff. Formalized by John Nash in 1950, the Lemke-Howson algorithm computationally finds equilibria in bimatrix games by identifying completely labeled vertex pairs in the strategy polytopes.

Nash, J. F. (1950). Equilibrium points in N-person games. Proceedings of the National Academy of Sciences, 36(1), 48-49.

Lemke, C. E., & Howson Jr, J. T. (1964). Equilibrium points of bimatrix games. Journal of the Society for Industrial and Applied Mathematics, 12(2), 413-423.

subgame-perfect-equilibrium bayesian-nash-equilibrium vcg-mechanism shapley-value


National Identity Scale

NIS
National Attachment Scale
Patriotism Scale

The National Identity Scale measures the strength and character of individuals' identification with their nation, including attachment to national symbols, pride in national achievements, and sense of belonging to the national community. Developed by Kosterman and Feshbach (1989), it distinguishes patriotism (pride in national accomplishments, willingness to serve) from nationalism (belief in national superiority, willingness to act against outsiders). The measure has become essential in comparative politics, examining how national identity shapes political behavior, attitudes toward immigration, support for international cooperation, and electoral choices.

Kosterman, R., & Feshbach, S. (1989). Toward a measure of patriotic and nationalistic attitudes. Political Psychology, 10(2), 257-274.

Anderson, B. (2006). Imagined communities: Reflections on the origin and spread of nationalism (Revised Edition). London: Verso.

Smith, T. W., & Jarkko, L. (2010). National pride in cross-national perspective. International Journal of Public Opinion Research, 22(1), 74-101.


Natural Experiment

Natural Experiment (Quasi-Experimental Design)

natural quasi-experiment
naturally occurring experiment
exogenous shock design
as-if randomization

1990s (formal methodological articulation); earlier in epidemiology (John Snow, 1854)

Varied; systematized in econometrics and political science (e.g., Meyer 1995; Angrist & Krueger 1991)

A natural experiment exploits a real-world event, policy, or circumstance that assigns individuals to treatment and control conditions in a way that is plausibly random — or at least exogenous to the outcome of interest. Because the researcher does not control assignment, it occupies a middle ground between a true randomized controlled trial and purely observational research, offering stronger causal leverage than conventional observational designs when the as-if randomization assumption holds.

Observational data with exogenous assignment mechanism (administrative, survey, longitudinal)

Meyer, B. D. (1995). Natural and quasi-experiments in economics. Journal of Business and Economic Statistics, 13(2), 151–161.

Dunning, T. (2012). Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge University Press.

randomized-controlled-trial difference-in-differences regression-discontinuity-design instrumental-variable field-experiment quasi-experimental-design


Natural Language Generation

NLG
data-to-text
text generation
Doğal Dil Üretimi (NLG)

1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)

Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)

Natural Language Generation (NLG) is the branch of natural language processing that automatically produces fluent, human-readable text from structured data, knowledge graphs, or semantic representations. Formalised in the classical pipeline by Reiter and Dale (2000) and surveyed comprehensively by Gatt and Krahmer (2018), NLG powers applications ranging from automated financial reporting and weather bulletins to data storytelling and conversational agents.

NLP generative task — structured data to natural language

Input: Structured data, knowledge graphs, or semantic representations

Output: Fluent, human-readable natural language text

BLEU, BERTScore, METEOR, human evaluation

Gatt, A. & Krahmer, E. (2018). Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation. Journal of Artificial Intelligence Research, 61, 65-170.

Reiter, E. & Dale, R. (2000). Building Natural Language Generation Systems. Cambridge University Press.

seq2seq transformer-nlp text-summarization machine-translation retrieval-augmented-generation gpt-finetuning automatic-text-evaluation


Nature Relatedness Scale

Connection to nature and environmental health

Nisbet, E. K.; Zelenski, J. M.; Murphy, S. A.

The NRS is a 21-item self-report instrument measuring individuals' psychological connection to and identification with the natural world. Developed by Nisbet, Zelenski, and Murphy in 2009, it captures three dimensions of nature relatedness: self-identification with nature, environmental concern and responsibility, and immersion in natural experiences.

Nisbet, E. K., Zelenski, J. M., & Murphy, S. A. (2009). The nature relatedness scale: Linking individuals' connection with nature to environmental concern and behavior. Environment and Behavior, 41(5), 715–740.

White, M. P., Alcock, I., Wheeler, B. W., & Depledge, M. H. (2019). Would you be happier living in a greener urban area? A fixed-effects analysis of panel data. Ecological Economics, 121, 199–204.

Kuo, M. (2015). How might contact with nature promote mental health? Promising mechanisms and a possible central pathway. Frontiers in Psychology, 6, 1093.

yoga-self-efficacy-scale holistic-caring-inventory spiritual-care-competence-scale attitudes-cam-scale


N-BEATS

N-BEATS (Neural Basis Expansion Analysis for Interpretable Time Series Forecasting)

N-BEATS — Nöral Zaman Serisi Tahmini
Neural Basis Expansion Analysis
neural basis expansion

N-BEATS is a deep learning architecture for time series forecasting, introduced by Oreshkin and colleagues in 2020, built from interpretable trend and seasonality stacks. It was the first purely neural forecasting model to reach state-of-the-art performance on the M4 competition without relying on any classical statistical components.

Deep neural forecasting architecture (interpretable basis expansion)

Oreshkin, B.N. et al. (2020). N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. ICLR.

Makridakis, S., Spiliotis, E. & Assimakopoulos, V. (2020). The M4 Competition: 100,000 Time Series and 61 Forecasting Methods. International Journal of Forecasting, 36(1), 54–74.

temporal-fusion-transformer informer deepar arima random-forest


NDF/ADF Analysis

Neutral Detergent Fiber and Acid Detergent Fiber Analysis

fiber fractionation
detergent fiber analysis
forage quality assessment

Neutral Detergent Fiber (NDF) and Acid Detergent Fiber (ADF) analysis is a chemical fractionation method that separates feed components into digestible and indigestible portions based on their resistance to sequential detergent treatments. Developed by Peter J. Van Soest in the 1960s, NDF/ADF analysis provides rapid estimates of forage quality and feed digestibility, making it fundamental to ruminant nutrition and feed evaluation worldwide.

Van Soest, P. J., & Wine, R. H. (1967). Use of detergents in the analysis of fibrous feeds: II. A rapid method for the determination of fiber and lignin. Journal of the Association of Official Analytical Chemists, 50(1), 50-55.

Goering, H. K., & Van Soest, P. J. (1970). Forage Fiber Analysis (Apparatus, Reagents, Procedures and Some Applications). Agricultural Handbook No. 379, USDA-ARS.

Mertens, D. R. (2002). Gravimetric determination of amylase-treated neutral detergent fiber in feeds with refluxing in beakers or crucibles: collaborative study. Journal of AOAC International, 85(6), 1217-1240.


Neck Disability Index

NDI
NDI Scale
Neck Disability Questionnaire

The Neck Disability Index (NDI) is a 10-item patient-reported outcome measure assessing the impact of neck pain and dysfunction on daily activities and quality of life. Developed by Vernon and Mior in 1991, NDI is the most widely used outcome measure in neck pain research and clinical practice, applicable to acute whiplash, cervical radiculopathy, chronic neck pain, and post-operative cervical conditions.

Vernon, H., & Mior, S. (1991). The Neck Disability Index: a study of reliability and responsiveness. Journal of Manipulative and Physiological Therapeutics, 14(7), 409–415.

Poole, G. D., Bailey, N., & Wilkinson, K. (2009). The Neck Disability Index: A cross-validation study. Physical Therapy Reviews, 14(4), 221–228.

oswestry-disability-index womac ndi-child dash-outcome-measure


NDVI

The Normalized Difference Vegetation Index (NDVI) is a spectral index computed from satellite or aerial multispectral imagery that quantifies vegetation greenness and vigor. Introduced by Rouse and colleagues in 1973 using Landsat data, NDVI has become the most widely used remote sensing metric for vegetation monitoring, drought assessment, crop productivity forecasting, and land cover change detection.

Spectral index for vegetation assessment

Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite Symposium Proceedings, 1, 309-317.

Jackson, R. D. (1983). Spectral indices in n-space. Remote Sensing of Environment, 13(5), 409-421.


NEAT

NeuroEvolution of Augmenting Topologies (NEAT)

Topology and Weight Evolving Artificial Neural Networks (variant)

NEAT is a genetic algorithm for evolving artificial neural networks introduced by Kenneth Stanley and Risto Miikkulainen in 2002. Unlike methods that evolve weights alone, NEAT simultaneously evolves both the topology (structure) and the connection weights of neural networks. It achieves this through a direct genome encoding with historical markings that enable meaningful crossover between networks of different structures, making it applicable to reinforcement learning, game playing, and control tasks without requiring a predefined architecture.

Direct graph encoding with historical markings

Stanley, K. O., & Miikkulainen, R. (2002). Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2), 99–127.


Necessary Condition Analysis

Necessary Condition Analysis (NCA) is a set-theoretic method developed by Dul (2016) that identifies conditions necessary (but not necessarily sufficient) for an outcome to occur. Unlike regression, which estimates average effects, NCA identifies absolute thresholds: conditions that must be present at a certain level for the outcome to be possible, regardless of other factors.

Dul, J. (2016). Necessary Condition Analysis (NCA): Logic and methodology of "necessary but not sufficient" causality. Organizational Research Methods, 19(1), 10-52.

Dul, J. (2018). A strategy for dealing with flaws and limitations in quantitative research. Organizational Research Methods, 21(1), 104-125.

Dul, J. (2019). Necessary Condition Analysis (NCA) version 3.3: A User Manual. Europeanstudies.org. Retrieved from https://www.erim.eur.nl/people/jan-dul/


Need for Cognition in Politics Scale

Need for Cognition in Political Context Scale (NFC-P)

The Need for Cognition in Politics Scale measures individual differences in the tendency to engage in and enjoy effortful cognitive processing related to political information and decision-making. Originally conceptualized by Cacioppo and Petty (1982), the trait reflects whether individuals seek, process, and rely on substantive information when forming political attitudes. High NFC individuals prefer detailed policy discussions; low NFC individuals may rely on heuristics, endorsements, or emotional appeals.

Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116-131.

Petty, R. E., & Cacioppo, J. T. (1988). Attitudes and persuasion: Classic and contemporary approaches. Dubuque, IA: William C. Brown.

Geuens, M., De Pelsmacker, P., & Moons, I. (2010). Developing a short version of the Need for Cognition scale. Journal of Personality Assessment, 92(1), 37-44.


Need for Cognition Scale

NCS
Cacioppo Need for Cognition
Intellectual Engagement Scale

The Need for Cognition Scale (NCS) is an 18-item measure assessing individual differences in the tendency to engage in and enjoy cognitive effort. Developed by John Cacioppo and Richard Petty in 1982, the NCS operationalizes need for cognition as a stable personality trait reflecting preference for thinking about complex problems, enthusiasm for intellectual pursuits, and intrinsic enjoyment of cognitive challenge. A brief 9-item version (NCS-9) is also available. The scale has become standard in psychology research examining motivation for learning, persuasion, decision-making, and academic achievement.

Intellectual engagement and cognitive motivation measure

Cacioppo, J. T., & Petty, R. E. (1982). The need for cognition. Journal of Personality and Social Psychology, 42(1), 116–131.

Cacioppo, J. T., Petty, R. E., & Kao, C. F. (1984). The efficient assessment of need for cognition. Journal of Personality Assessment, 48(3), 306–307.

Coutinho, S. A. (2007). The relationship between goals, metacognition, and academic success. Educate Journal, 7(1), 39–47.


Needs Assessment Tool Palliative Care

Developed by palliative care researchers and clinicians to address systematic gap assessment

The Needs Assessment Tool for Palliative Care (NAPC) is a comprehensive, multidomain assessment framework designed to systematically identify unmet palliative and supportive care needs in patients with advanced illness and their families. Rather than a numerical scale, the NAPC functions as a structured clinical interview and resource allocation guide, helping palliative care teams deliver holistic, person-centered care by addressing physical, psychological, social, spiritual, and practical dimensions simultaneously.

Clinician-rated interview or patient self-report

Gardiner, C., Brereton, L., Frey, R., Wilkinson, J., & Ingleton, C. (2011). Exploring the financial impact of palliative care on patients and families. Current Opinion in Supportive and Palliative Care, 5(1), 58–65.

Ahmed, N., Bestall, J. C., Ahmedzai, S. H., Payne, S. A., Clark, D., & Noble, B. (2004). Systematic review of the problems and issues of accessing specialist palliative care by patients with non-malignant illnesses. Journal of Palliative Medicine, 7(2), 290–297.

mcgill-quality-of-life caregiver-qol-cancer support-team-assessment-schedule patient-dignity-inventory comfort-care-checklist


Negation Detection

Negation Detection (Negation Scope Identification)

Olumsuzlama Tespiti (Negation Detection)

2001 (NegEx); scope learning formalised by 2009

Chapman et al. (NegEx algorithm, 2001); Morante & Daelemans (scope learning, 2009)

Negation detection is a natural-language-processing task that locates negation cues in text — words or phrases such as 'no', 'not', 'without', or 'denies' — and determines the span of text (the scope) whose meaning those cues invert. Formalised for clinical text by Chapman et al. (2001) with the NegEx algorithm and extended to scope learning in biomedical literature by Morante and Daelemans (2009), the method is essential wherever the difference between a finding being present and its being explicitly ruled out carries real consequences.

Input: Plain or structured text (clinical notes, scientific articles, etc.)

Output: Annotated negation cues and their semantic scope over target expressions

Clinical NLP, biomedical text mining, sentiment analysis

Chapman, W.W., Bridewell, W., Hanbury, P., Cooper, G.F., & Buchanan, B.G. (2001). A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries. Journal of the American Medical Informatics Association, 8(6), 606-614.

Morante, R. & Daelemans, W. (2009). Learning the Scope of Hedge Cues in BioMedical Texts. Proceedings of the BioNLP 2009 Workshop, Association for Computational Linguistics, 28-36.

dependency-parsing named-entity-recognition sentiment-analysis clinical-text-mining coreference-resolution information-extraction


Negative Binomial Regression

NB regression
NB2 regression
negatif binom regresyonu

Hilbe (textbook treatment); generalized linear model framework

Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data.

Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press.

poisson-regression zero-inflated-poisson-regression logistic-regression ols-regression panel-fixed-effects


National Eye Institute Visual Function Questionnaire-25

The NEI-VFQ-25 is a 25-item self-report questionnaire measuring the impact of vision loss on health-related quality of life across multiple functional and psychological domains. Developed by the National Eye Institute (Mangione et al., 2001), it is the most widely used vision-specific QoL instrument in ophthalmology and serves as the gold standard for quantifying patient-reported visual disability across diverse eye conditions.

Mangione, C. M., Lee, P. P., Gutierrez, P. R., et al. (2001). Development of the 25-item National Eye Institute Visual Function Questionnaire. Arch Ophthalmol, 119(7), 1050-1058.

Mangione, C. M., Berry, S., Spritzer, K., et al. (1998). Identifying the content area for the 51-item National Eye Institute Visual Function Questionnaire. Arch Ophthalmol, 116(2), 227-233.

visual-function-index glaucoma-quality-of-life ocular-surface-disease-index low-vision-quality-of-life


Nelson-Aalen Estimator

Nelson-Aalen Cumulative Hazard Estimator

Nelson-Aalen kümülatif hazard tahmincisi

The Nelson-Aalen estimator is a non-parametric estimator of the cumulative hazard function from right-censored time-to-event data. Developed by Wayne Nelson for reliability hazard plotting in 1972 and placed on a rigorous counting-process foundation by Odd Aalen in 1978, it accumulates the ratio of observed events to the number at risk at each event time, providing the natural hazard-scale companion to the Kaplan-Meier survival curve.

Non-parametric cumulative hazard estimator

Counting-process increment of events over the at-risk set

Output: Cumulative hazard function H(t)

Nelson, W. (1972). Theory and applications of hazard plotting for censored failure data. Technometrics, 14(4), 945–966.

Aalen, O. (1978). Nonparametric inference for a family of counting processes. Annals of Statistics, 6(4), 701–726.

kaplan-meier cox-regression log-rank-test weibull-regression frailty-model


Nemenyi Test

Nemenyi Testi — Friedman Post-Hoc
Nemenyi multiple comparison test
Nemenyi procedure

The Nemenyi test is a nonparametric post-hoc multiple comparison procedure introduced by Peter Nemenyi in his 1963 Princeton doctoral thesis. It is applied after a significant Friedman test to identify which specific pairs of conditions differ from each other in a repeated-measures or blocked design.

Nonparametric post-hoc multiple comparison

Nemenyi, P. (1963). Distribution-Free Multiple Comparisons. PhD thesis, Princeton University.

friedman-test conover-iman-test kruskal-wallis wilcoxon-signed-rank repeated-measures-anova


NEO Personality Inventory — Revised

NEO Personality Inventory — Revised (NEO PI-R)

NEO PI-R
Costa and McCrae Personality Inventory

The NEO PI-R is a comprehensive 240-item self-report personality assessment that measures five major personality dimensions and thirty lower-order facets. Developed by Paul Costa and Robert McCrae in the early 1990s, it operationalizes the Five-Factor Model of personality—one of the most empirically validated trait taxonomies in psychological science. The measure has become the gold standard for personality assessment in clinical, research, and occupational settings.

Costa, P. T., & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO PI-R) and NEO Five-Factor Inventory (NEO-FFI) professional manual. Psychological Assessment Resources.

McCrae, R. R., & Costa, P. T. (1989). The structure of interpersonal traits: Expanding the interpersonal circumplex. Journal of Personality and Social Psychology, 48(6), 1670–1680.

Costa, P. T., & McCrae, R. R. (2008). The Revised NEO Personality Inventory (NEO PI-R). In G. J. Boyle, G. Matthews, & D. H. Saklofske (Eds.), The SAGE handbook of personality theory and assessment: Vol. 2. Personality measurement and testing (pp. 179–198). SAGE Publications.


SNAP-II

SNAP-II is a six-variable physiological scoring system designed to quantify acute illness severity in very low birth weight (VLBW) neonates and predict mortality risk. Developed by Richardson and colleagues in 2001 as a refinement of the original SNAP, it incorporates readily available bedside physiological variables (mean blood pressure, lowest body temperature, hypoxemia, seizures, urine output, and sepsis indicators) measured within the first 12 hours of life. SNAP-II is widely used in neonatal quality improvement, clinical research, and benchmarking of NICU outcomes.

Richardson, D. K., Gray, J. E., Gortmaker, S. L., Goldmann, D. A., Purohit, D. M., & Paige, D. (2001). Declining Severity Adjusted Mortality: Evidence of Improving Neonatal Intensive Care. Pediatrics, 108(2), 331-337.

Richardson, D. K., Corcoran, J. D., Escobar, G. J., & Lee, S. K. (1993). SNAP-II: Simplified Newborn Physiology Score and Estimation of Mortality Risk in Very Low Birth Weight Infants. Pediatrics, 91(1), 33-41.


NBAS

The NBAS, commonly known as the Brazelton Scale, is a comprehensive neurobehavioral assessment tool designed to evaluate the behavioral competencies of newborns. Developed by T. Berry Brazelton and colleagues in 1973 and refined through multiple editions, it examines 28 behavioral items and 18 elicited reflex items to characterize a newborn's neurological integrity, behavioral capabilities, and individuality. The NBAS has become a foundational instrument in developmental pediatrics, neonatal neurology, and early intervention research.

Brazelton, T. B., & Nugent, J. K. (1995). Neonatal Behavioral Assessment Scale (3rd ed.). Cambridge University Press.

Nugent, J. K., Keefer, C. H., Minear, S., Johnson, L. C., & Blanchard, Y. (2007). Understanding Newborn Behavior and Early Relationships: The Newborn Behavioral Observations (NBO) System Handbook. Brookes Publishing.


N-PASS

The N-PASS is a five-item behavioral and physiological assessment tool designed to measure pain, agitation, and sedation in neonates across the full spectrum from profound sedation to severe pain. Developed by Hummel et al. in 2008, it is validated for both ventilated and non-ventilated infants in NICU settings and provides a rapid bedside assessment combining facial expression, extremity tone, vital sign changes, state of consciousness, and cry characteristics.

Hummel, P., Puchalski, M., Creech, S. D., & Weiss, M. G. (2008). Clinical Reliability and Validity of the N-PASS: Neonatal Pain, Agitation and Sedation Scale with Prolonged Ventilation Patients. Journal of Perinatology, 28(1), 55-60.

Hummel, P. (2008). Neonatal Pain, Agitation and Sedation Scale. Advances in Neonatal Care, 8(5), 285-287.


Nested case-control

NCC study
nested CC design
case-control within cohort
density sampling case-control

Nathan Mantel (1973); D. C. Thomas (1977 formalization)

A nested case-control study is an efficient observational design embedded within a defined cohort. For each participant who develops the outcome of interest (a case), a small number of matched controls are sampled from those still at risk at the same point in time. This density-sampling strategy yields odds ratios that approximate incidence-rate ratios from the full cohort at a fraction of the data-collection cost — making it the preferred alternative when measuring exposures for all cohort members would be prohibitively expensive or technically demanding.

Time-to-event data from an existing cohort (exposure, covariate, and outcome records)

Thomas, D. C. (1977). Addendum to: Methods of cohort analysis: Appraisal by application to asbestos mining. Journal of the Royal Statistical Society, Series A, 140(4), 469–491.

Mantel, N. (1973). Synthetic retrospective studies and related topics. Biometrics, 29(3), 479–486.

cohort-study case-control-study case-crossover-design prospective-cohort-study cox-proportional-hazards survival-analysis


Nested Logit

Tree Logit Model
Hierarchical Logit Model
Generalized Extreme Value Logit
İç İçe Logit Modeli

The Nested Logit model is a discrete choice framework that groups mutually exclusive alternatives into hierarchical nests, allowing correlated unobserved utilities within each nest while maintaining independence across nests. Introduced formally by Ben-Akiva and Lerman (1985) and grounded in McFadden's Generalized Extreme Value (GEV) theory, it extends the standard Multinomial Logit by relaxing the restrictive Independence of Irrelevant Alternatives assumption within predefined groups of similar alternatives.

Categorical (mutually exclusive alternatives grouped in nests)

Ben-Akiva, M., & Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press.


Net Promoter Score

Customer satisfaction and loyalty measurement

Net Promoter Score (NPS) is a customer loyalty and satisfaction metric developed by Fred Reichheld in 2003, measured through a single question: How likely is it that you would recommend our company/product/service to a friend or colleague? The metric categorizes respondents into promoters, passives, and detractors, providing a straightforward indicator of customer advocacy and business growth potential.

Reichheld, F. F. (2003). The One Number You Need to Grow. Harvard Business Review, 81(12), 46-54.

Reichheld, F. F. (2006). The Ultimate Question: Driving Good Profits and True Growth. Harvard Business School Press.

Keiningham, T. L., Cooil, B., Aksoy, L., & Andreassen, T. W. (2007). A Longitudinal Examination of Net Promoter and Firm Financial Performance. Journal of Marketing Research, 44(3), 468-482.

brand-equity-measurement customer-satisfaction-analysis customer-lifetime-value advertising-effectiveness-study customer-journey-mapping


Netnography

online ethnography
virtual ethnography
cyber-ethnography
digital ethnography

1997 (coined); 2010 (first comprehensive methodology book)

Netnography is a qualitative research method that adapts the principles of cultural ethnography to the study of online communities and social media environments. Coined by Robert Kozinets in 1997 and systematised in his 2010 handbook, netnography treats digital spaces — forums, social networks, blogs, review sites — as naturally occurring field sites where communities gather, share meanings, and construct identities. The method combines unobtrusive observation of digital traces with active participation and, where appropriate, direct member interaction.

Online text, images, video, and social media posts; archived forum discussions; digital community interactions

1 community or multiple online communities; hundreds to thousands of posts sampled purposively

Kozinets, R. V. (2010). Netnography: Doing Ethnographic Research Online. Sage.

Kozinets, R. V. (2020). Netnography: The Essential Guide to Qualitative Social Media Research (3rd ed.). Sage.

ethnography grounded-theory content-analysis discourse-analysis thematic-analysis case-study


Network Analysis of Case Law

Network Analysis of Case Law and Legal Precedent

citation network analysis
legal precedent mapping
case law graph analysis

Network analysis of case law applies graph-theoretic and network science methods to study the structure and dynamics of legal precedent systems. Developed systematically by James Fowler and colleagues in 2011, this method treats legal citations as directed edges in a network where nodes represent court decisions and edges represent precedent relationships. By analyzing the topology of these networks, researchers uncover patterns in how law evolves, which precedents are most influential, and how legal doctrine spreads across jurisdictions.

Network science and legal informatics method

Lupo, G., & Bailey, J. (2014). Artificial intelligence and legal practice. Academic Press.

Fowler, J. H., Johnson, S. L., & Spriggs, J. F. (2011). Network analysis and the law: measuring the web of law. Journal of Empirical Legal Studies, 8(1), 171-198.

Bommarito, M., & Katz, D. M. (2012). Properties of the United States code: Network analysis and textual entropy. SSRN Electronic Journal.


Network-based Co-citation Analysis

co-citation network analysis
bibliometric network co-citation
co-citation mapping
CCA network approach

1973 (co-citation); network-analytic extension widely adopted 2000s–2010s

Henry Small (co-citation foundation); network visualization extended by Chaomei Chen and others

Network-based co-citation analysis is a bibliometric technique that measures how often pairs of documents are cited together by later works, then models those relationships as a weighted network. Nodes represent documents (or authors or journals), edges represent co-citation frequency, and network algorithms identify clusters of intellectually related literature. It is widely used in systematic and scoping reviews to map the intellectual structure of a research field.

Citation databases (Web of Science, Scopus, PubMed); co-citation frequency matrices

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

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 bibliometric-analysis science-mapping co-word-analysis scientometric-analysis


Network-Based Copy Number Variation Analysis

network CNV analysis
CNV network propagation
graph-based CNV analysis
network-integrated copy number analysis

Fabio Vandin, Benjamin Raphael and colleagues (HotNet framework); Matthew Leiserson et al. (HotNet2)

Network-based copy number variation analysis integrates genome-wide CNV data with biological interaction networks — such as protein-protein interaction (PPI) or pathway networks — to identify functionally coherent regions, driver genes, and altered subnetworks that raw CNV calling alone would miss. By propagating CNV signals through the network graph, the method reveals coordinated genomic dosage imbalances that converge on common biological functions, making it especially powerful in cancer genomics and rare-disease studies.

Segmented CNV profiles (array CGH, SNP array, or WGS), biological network graphs (PPI, pathway, co-expression)

Vandin, F., Upfal, E., & Raphael, B. J. (2012). De novo discovery of mutated driver pathways in cancer. Genome Research, 22(2), 375–385.

Leiserson, M. D. M., Vandin, F., Wu, H.-T., Dobson, J. R., Eldridge, J. V., Thomas, J. L., Papoutsaki, A., Kim, Y., Niu, B., McLellan, M., Lawrence, M. S., Gonzalez-Perez, A., Tamborero, D., Cheng, Y., Ryslik, G. A., Lopez-Bigas, N., Getz, G., Ding, L., & Raphael, B. J. (2015). Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nature Genetics, 47(2), 106–114.


Network-based epigenome-wide association study

Network-based Epigenome-Wide Association Study

network EWAS
network-integrated EWAS
graph-based EWAS
network-based DNA methylation analysis

Adapted from EWAS (Rakyan et al., 2011) and network-based genomic methods (e.g., Ideker & Sharan, 2008)

Network-based EWAS extends conventional epigenome-wide association studies by overlaying differentially methylated positions or regions onto biological interaction networks — such as protein-protein interaction, co-expression, or gene regulatory networks — to identify functionally coherent epigenetic modules rather than isolated CpG hits. This integration increases statistical power for detecting weak signals and reveals coordinated epigenetic dysregulation across pathways.

DNA methylation array (e.g., Illumina 450K/EPIC), protein-protein interaction or gene regulatory networks

Rakyan, V. K., Down, T. A., Balding, D. J., & Beck, S. (2011). Epigenome-wide association studies for common human diseases. Nature Reviews Genetics, 12(8), 529–541.

Wang, S., Huang, M., Liu, C., Ma, J., & Deng, M. (2017). Network-based methods for identifying disease-related loci and epigenetic biomarkers. Briefings in Bioinformatics, 18(6), 957–968.


Network-based eQTL analysis

Network-based Expression Quantitative Trait Loci Analysis

network eQTL
network-integrated eQTL mapping
graph-based eQTL analysis
eQTL network analysis

2008–2013 (network-integrated extensions of eQTL mapping)

Multiple groups; foundational eQTL work by Cheung et al. (2005) and Stranger et al. (2007); network integration extended by Zhu et al. (2008) and others

Network-based eQTL analysis extends classical eQTL mapping by embedding genetic variant-to-expression associations within gene regulatory or protein interaction networks. Rather than treating each SNP-gene pair independently, this approach leverages network topology — such as co-expression modules or known pathway structures — to improve statistical power, reduce multiple testing burden, and reveal how genetic variants perturb entire regulatory programs rather than isolated transcripts.

Statistical genomics / network analysis pipeline

Genotype data (SNP arrays or WGS), gene expression data (RNA-seq or microarray), gene interaction/regulatory network data

Skinner, M. E., Uzilov, A. V., Stein, L. D., Mungall, C. J., & Holmes, I. H. (2009). JBrowse: a next-generation genome browser. Genome Research, 19(9), 1630–1638.

Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1), Article17.

eqtl-analysis gene-regulatory-network-inference genome-wide-association-study rna-seq-differential-expression pathway-enrichment-analysis bayesian-eqtl-analysis


Network-Based Gene Set Enrichment Analysis

network GSEA
network-propagation GSEA
NetGSA
graph-informed gene set testing

2010 (NetGSA); field consolidated 2010-2015

Ali Shojaie & George Michailidis (NetGSA); broader network-propagation approaches by multiple groups (~2010-2015)

Network-based gene set enrichment analysis (network GSEA) extends classical GSEA by incorporating biological interaction networks — such as protein-protein interaction (PPI) or co-expression graphs — into the enrichment test. Instead of treating each gene independently, the method propagates differential expression signals across network edges, allowing genes that are co-regulated or functionally connected to jointly support the significance of a gene set. The result is a biologically coherent enrichment score that accounts for pathway topology and gene-gene dependencies.

Network-informed statistical enrichment test

Gene expression matrix (RNA-seq or microarray) + biological network (PPI, co-expression, or pathway graph)

Shojaie, A., & Michailidis, G. (2010). Network enrichment analysis in complex experiments. Statistical Applications in Genetics and Molecular Biology, 9(1), Article 22.

Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545-15550.


Network-based GWAS

Network-based Genome-Wide Association Study

network GWAS
gene network GWAS
network-informed GWAS
NbGWAS

2011–2013 (early tools); mature framework by 2015

Jia et al. (dmGWAS, 2011); Baranzini et al.; multiple concurrent groups

Network-based GWAS integrates conventional genome-wide association study results with biological network data — such as protein-protein interaction (PPI) networks or gene co-expression graphs — to identify disease-relevant gene modules or subnetworks. Instead of reporting only the top individual SNPs, this approach propagates association signals through molecular interaction networks, surfacing gene clusters whose collective signal implicates them in complex-trait biology even when no single variant reaches genome-wide significance alone.

GWAS summary statistics (p-values or Z-scores) plus biological network (PPI or co-expression)

Wang, Q., Yu, H., Zhao, Z., & Jia, P. (2015). EW_dmGWAS: edge-weighted dense module search for genome-wide association studies and gene expression profiles. Bioinformatics, 31(15), 2591–2594.

Leiserson, M. D. M., Vandin, F., Wu, H.-T., Dobson, J. R., Eldridge, J. V., Thomas, J. L., Papoutsaki, A., Kim, Y., Niu, B., McLellan, M., Lawrence, M. S., Gonzalez-Perez, A., Tamborero, D., Cheng, Y., Ryslik, G. A., Lopez-Bigas, N., Getz, G., Ding, L., & Raphael, B. J. (2015). Pan-cancer network analysis identifies combinations of rare somatic mutations contributing to tumorigenesis. Nature Genetics, 47(2), 106–114.

genome-wide-association-study pathway-enrichment-analysis gene-set-enrichment-analysis eqtl-analysis network-based-eqtl-analysis copy-number-variation-analysis


Network-based Mapping review

network mapping review
citation-network mapping review
network-enhanced evidence mapping
network-informed mapping review

Petticrew & Roberts (mapping review); network overlay adopted from bibliometric network analysis tradition

A network-based mapping review combines the breadth of a traditional evidence mapping exercise with bibliometric network analysis to chart the structural landscape of a research field. Rather than simply cataloguing studies by topic, this approach constructs citation, co-authorship, or co-word networks to reveal clusters of intellectual activity, influential works, and collaboration patterns — producing both a visual and a descriptive map of the evidence base.

Evidence synthesis method with network analysis overlay

Bibliographic records, citation data, co-authorship data

Petticrew, M., & Roberts, H. (2006). Systematic Reviews in the Social Sciences: A Practical Guide. Blackwell Publishing.

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523-538.

mapping-review bibliometric-analysis co-citation-analysis bibliographic-coupling scoping-review science-mapping


Network-based Meta-analysis

Network-based Meta-analysis (Network Meta-Analysis)

NMA
network meta-analysis
mixed-treatment comparison
multiple-treatments meta-analysis

Thomas Lumley (statistical framework); Georgia Salanti (SUCRA and ranking methods)

Network-based Meta-analysis (NMA) extends conventional pairwise meta-analysis by simultaneously synthesizing evidence across a network of two or more competing treatments, including pairs that have never been compared head-to-head in a single trial. By combining direct and indirect evidence within a coherent statistical model, NMA produces relative effect estimates for all treatment pairs and generates a probabilistic ranking of which treatment performs best on the outcome of interest.

Aggregate or individual-participant data from multiple randomized controlled trials

Lumley, T. (2002). Network meta-analysis for indirect treatment comparisons. Statistics in Medicine, 21(16), 2313–2324.

Salanti, G. (2012). Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Research Synthesis Methods, 3(2), 80–97.

systematic-literature-review meta-analysis network-meta-analysis bibliometric-analysis scoping-review umbrella-review


Network-based metabolomics analysis

metabolic network analysis
systems metabolomics
network metabolomics
metabolite network enrichment

Barabasi, Loscalzo and colleagues (network medicine framework); Wishart and Xia (metabolomics network tools)

Network-based metabolomics analysis integrates quantitative metabolite profiling data with biological network structures — metabolic pathways, protein-metabolite interaction graphs, and disease networks — to reveal coordinated biochemical disruptions that individual metabolite lists would miss. Rather than treating each metabolite in isolation, this systems-level approach identifies modules, hubs, and perturbed subnetworks, providing mechanistic insight into how metabolic dysregulation propagates through cellular systems.

Systems biology / omics analysis pipeline

Quantitative metabolite abundance tables (LC-MS, GC-MS, NMR); metabolic network databases (KEGG, HMDB, Reactome)

Xia, J., & Wishart, D. S. (2010). MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Research, 38(Web Server issue), W71–W77.

Barabasi, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature Reviews Genetics, 12(1), 56–68.

metabolomics-analysis pathway-enrichment-analysis gene-set-enrichment-analysis multi-omics-metabolomics-analysis proteomics-analysis bayesian-metabolomics-analysis


Network-Based Microbiome Diversity Analysis

microbial co-occurrence network analysis

Faust, Raes, Friedman, Alm and colleagues

Network-based microbiome diversity analysis integrates graph-theoretic co-occurrence network inference with classical alpha- and beta-diversity metrics to characterize the structural organization of microbial communities. Rather than treating taxa as independent entities, the method models pairwise microbial associations as edges in a network, enabling identification of keystone taxa, community modules, and ecological interaction patterns that simple diversity indices cannot detect.

16S rRNA amplicon or shotgun metagenomics OTU/ASV count tables

Friedman, J., & Alm, E. J. (2012). Inferring correlation networks from genomic survey data. PLoS Computational Biology, 8(9), e1002687.

Faust, K., & Raes, J. (2012). Microbial interactions: from networks to models. Nature Reviews Microbiology, 10(8), 538–550.


Network-based Pathway Enrichment Analysis

network pathway enrichment
network-based enrichment
topology-based pathway analysis
NBPEA

2002 (seminal network-scoring concept); matured 2010–2015

Ideker, Ozier, Schwikowski, and Siegel (network-based scoring); extended by Vaske et al. (PARADIGM) and others

Network-based pathway enrichment analysis integrates molecular interaction networks — protein-protein interactions, signalling graphs, or gene regulatory networks — with omics measurements to identify biological pathways that are coordinately altered in a condition. Unlike classical over-representation or gene-set enrichment approaches that treat pathway genes as independent lists, this family of methods propagates signals across network edges, capturing the topology of interactions and uncovering dysregulated modules that flat-list enrichment would miss.

Pathway enrichment and network analysis method

Omics data (transcriptomics, proteomics, genomics) combined with molecular interaction networks

Ideker, T., Ozier, O., Schwikowski, B., & Siegel, A. F. (2002). Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics, 18(suppl_1), S233–S240.

Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D., Szeto, C., Zhu, J., Haussler, D., & Stuart, J. M. (2010). Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics, 26(12), i237–i245.


Network-based Phylogenetic Analysis

phylogenetic network
reticulate phylogenetics
split network analysis
evolutionary network inference

1992–2004 (foundational algorithms); broader development 1990s–2010s

Hans-Jürgen Bandelt & Andreas Dress (split decomposition); David Bryant & Vincent Moulton (Neighbor-Net)

Network-based phylogenetic analysis constructs graph-structured representations of evolutionary relationships that explicitly accommodate reticulate events — including hybridization, horizontal gene transfer, recombination, and incomplete lineage sorting — which strictly bifurcating phylogenetic trees cannot represent. Instead of forcing sequences into a single bifurcating tree, the method infers splits or reticulations in the data and visualises them as a network, revealing conflicting phylogenetic signals that are biologically informative.

Aligned DNA/RNA sequences, distance matrices, SNP data

Bandelt, H.-J., & Dress, A. W. M. (1992). Split decomposition: A new and useful approach to phylogenetic analysis of distance data. Molecular Phylogenetics and Evolution, 1(3), 242–252.

Bryant, D., & Moulton, V. (2004). Neighbor-Net: An agglomerative method for the construction of phylogenetic networks. Molecular Biology and Evolution, 21(2), 255–265.

phylogenetic-analysis sequence-alignment genome-wide-association-study bayesian-phylogenetic-analysis variant-calling multi-omics-phylogenetic-analysis


Network-based RNA Sequencing Differential Expression Analysis

network-aware DE analysis
gene network differential expression
co-expression network DE
NB-DEA

Ideker et al. (network scoring); Zhang & Horvath (WGCNA framework)

Network-based RNA-seq differential expression analysis integrates conventional differential expression testing with gene interaction networks — such as protein-protein interaction graphs or weighted co-expression networks — to identify not just individual differentially expressed genes but coherent, biologically meaningful gene modules that change together between conditions. This approach substantially reduces false positives and surfaces pathway-level signals invisible to gene-by-gene testing.

RNA-seq count matrices; gene/protein interaction networks (PPI, co-expression)

Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(1), Article 17.

Ideker, T., Ozier, O., Schwikowski, B., & Siegel, A. F. (2002). Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics, 18(Suppl 1), S233–S240.


Network-based Scientometric analysis

scientometric network analysis
bibliometric network analysis
citation network scientometrics
science network mapping

1965 (Price); computational refinement 2000s–2010s

Derek J. de Solla Price (network citation structure); Nees Jan van Eck & Ludo Waltman (computational network mapping)

Network-based scientometric analysis applies graph-theoretic methods to bibliographic data — publications, citations, authors, and keywords — to map the intellectual structure of a scientific field. By modeling documents or authors as nodes and their relationships (citations, co-authorships, co-word occurrences) as edges, it reveals clusters of knowledge, central actors, emerging topics, and the flow of ideas across disciplines. Tools such as VOSviewer, Gephi, and the R package bibliometrix are commonly used.

Publication records, citation data, co-authorship data (bibliographic databases: Web of Science, Scopus, Dimensions)

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.

Mingers, J., & Leydesdorff, L. (2015). A review of theory and practice in scientometrics. European Journal of Operational Research, 246(1), 1–19.

bibliometric-analysis scientometric-analysis co-citation-analysis bibliographic-coupling co-word-analysis science-mapping


Network-based Single-Cell RNA Sequencing Analysis

single-cell gene regulatory network inference

single-cell co-expression network analysis

2015–2017 (rapid development alongside scRNA-seq methods; SCENIC 2017)

Aibar et al. (SCENIC, gene regulatory networks); Jin et al. (CellChat, cell-cell communication networks)

Network-based single-cell RNA-seq analysis extends standard scRNA-seq workflows by constructing and interrogating molecular interaction networks — gene regulatory networks, co-expression networks, or cell-cell communication graphs — from single-cell transcriptomic data. Rather than treating each gene independently, this approach captures the coordinated activity of gene circuits and intercellular signalling pathways within and between cell populations, enabling a systems-level view of transcriptional regulation at single-cell resolution.

Single-cell RNA sequencing count matrices (UMI counts per cell per gene)

Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., ... & Aerts, S. (2017). SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083–1086.

Jin, S., Guerrero-Juarez, C. F., Zhang, L., Chang, I., Ramos, R., Kuan, C. H., ... & Nie, Q. (2021). Inference and analysis of cell-cell communication using CellChat. Nature Communications, 12(1), 1088.


Network-Based Spatial Analysis

network spatial analysis
network-constrained spatial analysis
spatial network analysis
NBSA

Network-based spatial analysis (NBSA) analyzes the distribution and interaction of spatial phenomena constrained to a network structure — such as roads, railways, or rivers — using network distance rather than straight-line (Euclidean) distance. It is the appropriate framework whenever movement, proximity, or risk is governed by the underlying network topology rather than open space.

Point events or flows on a network (roads, rivers, infrastructure)

Okabe, A., Satoh, T., Furuta, T., Sugihara, K., & Okano, K. (2006). Generalized network Voronoi diagrams: Concepts, computational methods, and applications. International Journal of Geographical Information Science, 22(9), 965–994.

Okabe, A., & Sugihara, K. (2012). Spatial Analysis Along Networks: Statistical and Computational Methods. Wiley.

kernel-density-estimation hot-spot-analysis spatial-autocorrelation geographically-weighted-regression voronoi-diagram shortest-path-analysis


Network-based variant calling

Network-based (Graph-genome) Variant Calling

graph-genome variant calling
variation graph genotyping
vg-based variant calling
pangenome variant calling

Erik Garrison, Paten lab (UCSC); Hannes Eggertsson, deCODE Genetics

Network-based (graph-genome) variant calling replaces the conventional single linear reference genome with a variation graph — a network in which nodes represent sequence segments and edges represent known alternative paths through the genome. Reads are mapped onto this graph, enabling detection of SNPs, indels, and structural variants with substantially lower reference bias than linear-reference pipelines. Key tools include the Variation Graph Toolkit (vg) and Graphtyper.

Short-read or long-read sequencing data (FASTQ/BAM); pangenome graph (GFA/VCF)

Garrison, E., Sirén, J., Novak, A. M., Hickey, G., Eizenga, J. M., Dawson, E. T., Jones, W., Garg, S., Markello, C., Lin, M. F., Paten, B., & Durbin, R. (2018). Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nature Biotechnology, 36(9), 875–879.

Eggertsson, H. P., Jonsson, H., Kristmundsdottir, S., Hjartarson, E., Kehr, B., Masson, G., Zink, F., Hjorleifsson, K. E., Jonasdottir, A., Jonasdottir, A., Jonsdottir, I., Gudbjartsson, D. F., Melsted, P., Stefansson, K., & Halldorsson, B. V. (2017). Graphtyper enables population-scale genotyping using pangenome graphs. Nature Genetics, 49(11), 1654–1660.

variant-calling sequence-alignment genome-wide-association-study copy-number-variation-analysis rna-seq-differential-expression bayesian-variant-calling


Network DEA

Network Data Envelopment Analysis
Network Efficiency Analysis
Multi-Stage DEA
Ağ Veri Zarflama Analizi

Network Data Envelopment Analysis (Network DEA) is a nonparametric efficiency measurement framework introduced by Färe and Grosskopf (2000) that extends classical DEA to multi-stage or multi-division production processes. Rather than treating a decision-making unit as a black box, it explicitly models the internal structure — the divisions and the intermediate products that flow between them — enabling stage-level and overall efficiency scores to be estimated simultaneously within a single coherent model.

Multi-stage nonparametric efficiency model

Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49.


Network Diffusion Analysis

Network Diffusion Analysis (Spread and Propagation on Graphs)

diffusion on networks
information diffusion
contagion spreading model
network propagation model

1927 (epidemic roots); network formalization 1990s–2000s

Network diffusion analysis models how information, diseases, behaviors, or innovations spread across a graph of nodes and edges. Drawing on classical epidemic theory (SI, SIR, SIS) and modern network science, it tracks which nodes become infected, how quickly, and whether the spread reaches a global cascade or dies out locally.

Graph (nodes, edges, edge weights; time-series contact data)

Kermack, W. O. & McKendrick, A. G. (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A, 115(772), 700–721.

Watts, D. J. & Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393, 440–442.

social-network-analysis betweenness-centrality modularity-analysis exponential-random-graph-model eigenvector-centrality two-mode-network-analysis


Network Diffusion Models

Network Diffusion Models (SIR, SIS, Independent Cascade)

Ağ Yayılım Modelleri (SIR, SIS, Independent Cascade)

1927 (epidemiological compartmental); 2003 (social influence cascade)

Kermack & McKendrick (SIR/SIS, 1927); Kempe, Kleinberg & Tardos (Independent Cascade, 2003)

Network diffusion models are a family of compartmental and probabilistic frameworks that simulate how information, disease, or innovation spreads across a connected system. Rooted in the mathematical epidemiology of Kermack and McKendrick (1927), the SIR and SIS models partition nodes into states and track transitions driven by contact rates and recovery probabilities. The Independent Cascade and Linear Threshold models, formalised by Kempe, Kleinberg, and Tardos (2003), extend this logic to social influence, modelling how activation propagates through a network one neighbour at a time.

Stochastic / deterministic simulation on graphs

SIR, SIS (compartmental); Independent Cascade; Linear Threshold

Output: Compartment time series (S, I, R counts); peak infection size; influence spread

Kermack, W.O. & McKendrick, A.G. (1927). A Contribution to the Mathematical Theory of Epidemics. Proceedings of the Royal Society of London. Series A, 115(772), 700-721.

Kempe, D., Kleinberg, J., & Tardos, E. (2003). Maximizing the Spread of Influence through a Social Network. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 137-146.

temporal-network-analysis network-resilience link-prediction community-detection centrality-analysis


Network Econometrics

Social Interactions Model
Peer Effects Model
Social Network Regression
Ağ Ekonometrisi

Yann Bramoullé, Habiba Djebbari & Bernard Fortin

Network econometrics estimates how individuals' outcomes are causally shaped by the behaviour and characteristics of their social-network neighbours. Formalised by Bramoullé, Djebbari, and Fortin (2009), the framework embeds a row-normalised adjacency matrix into a linear regression, separating endogenous peer effects (imitation of outcomes), exogenous contextual effects (influence of neighbours' attributes), and correlated effects (shared environment), while using network topology to construct valid instruments.

Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 150(1), 41–55.


Network Embedding

Network Embedding (Node2Vec, DeepWalk, LINE)

node embedding
graph embedding
network representation learning
Ağ Gömme (Node2Vec, DeepWalk, LINE)

Network embedding is a family of representation-learning methods that map each node of a graph into a dense, low-dimensional vector while preserving the network's structural properties. The approach was formalised for social-network data by Perozzi, Al-Rfou, and Skiena with DeepWalk (2014), which adapted the Word2Vec skip-gram model to random walks on graphs, and extended by Grover and Leskovec with Node2Vec (2016), which introduced a biased random walk that balances breadth-first and depth-first exploration. These embeddings turn relational data into feature vectors that standard machine-learning classifiers and clustering algorithms can consume directly.

Perozzi, Al-Rfou & Skiena (DeepWalk, 2014); Grover & Leskovec (Node2Vec, 2016)

Representation learning / unsupervised network method

Input: Graph with nodes and edges (weighted or unweighted)

Output: Dense low-dimensional vector for each node

Grover, A. & Leskovec, J. (2016). Node2Vec: Scalable Feature Learning for Networks. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 855-864.

Perozzi, B., Al-Rfou, R., & Skiena, S. (2014). DeepWalk: Online Learning of Social Representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 701-710.

community-detection centrality-analysis graph-neural-networks dimensionality-reduction link-prediction


Network Function Virtualization

virtual network functions
network slicing

Network Function Virtualization (NFV) is a paradigm that implements traditional network functions (firewalls, load balancers, gateways, packet inspection) as software running on commodity servers instead of proprietary hardware appliances. Introduced by ETSI (2012), NFV reduces capital and operational expenses by leveraging cloud infrastructure and enabling rapid deployment of network services. Combined with SDN, NFV enables on-demand service creation and network slicing. It is now central to 5G and cloud-native network architecture.

ETSI (European Telecommunications Standards Institute). (2012). Network Functions Virtualisation (NFV); Architectural Framework. GS NFV 002 V1.1.1.

Mijumbi, R., Serrat, J., Gorricho, J. L., et al. (2016). Network function virtualization: State-of-the-art and research challenges. IEEE Communications Surveys & Tutorials, 18(1), 236-262.


Network Meta-Analysis

Mixed Treatment Comparison
MTC
Indirect Comparison Meta-Analysis

Network meta-analysis (NMA) is a systematic method for comparing multiple interventions simultaneously within a single analytical framework, incorporating both direct evidence (head-to-head trials) and indirect evidence (comparisons via common comparators). First formalized by Lumley in 2002, NMA allows researchers to rank treatments and quantify comparative effectiveness even when some treatment pairs have never been directly studied.

Lumley, T. (2002). Network meta-analysis for indirect treatment comparisons. Statistics in Medicine, 21(16), 2313–2324.

Bucher, H. C., Guyatt, G. H., Griffith, L. E., & Walter, S. D. (1997). The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. Journal of Clinical Epidemiology, 50(6), 683–691.

Dias, S., Welton, N. J., Caldwell, D. M., & Ades, A. E. (2010). Checking consistency in mixed treatment comparison meta-analysis. Statistics in Medicine, 29(7–8), 932–944.

pairwise-meta-analysis meta-regression systematic-review evidence-synthesis-framework bayesian-hierarchical-models


Network Motif Analysis

network motifs
subgraph significance profile
Ağ Motif Analizi (Network Motifs)

Network motif analysis is a statistical method for directed networks, introduced by Milo, Shen-Orr, and Alon in 2002, that identifies small recurring subgraph patterns — motifs — that appear significantly more often than would be expected in a comparable random network. By comparing a real network against a null ensemble of randomised graphs, the method reveals the elementary structural building blocks that define the functional organisation of biological regulatory networks, social networks, and other complex systems.

Ron Milo, Shai Shen-Orr, Uri Alon et al.

Statistical pattern-detection method for directed graphs

Random network ensemble (edge-randomised)

Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network Motifs: Simple Building Blocks of Complex Networks. Science, 298(5594), 824-827.

Alon, U. (2007). Network Motifs: Theory and Experimental Approaches. Nature Reviews Genetics, 8(6), 450-461.

ego-network-analysis community-detection social-network-analysis graph-clustering small-world-analysis


Network Resilience Analysis

Network Resilience and Vulnerability Analysis

Ağ Dayanıklılığı ve Güvenlik Açığı Analizi

Network resilience and vulnerability analysis is an analytical framework, formalised by Albert, Jeong, and Barabási (2000), that measures how a network degrades functionally as nodes or edges are progressively removed. By running targeted-attack simulations — removing the highest-centrality nodes first — and random-failure simulations — removing nodes at uniform probability — the framework identifies which structural elements are critical to network integrity and where infrastructure is most exposed.

Network robustness / vulnerability framework

Albert, R., Jeong, H. & Barabási, A.L. (2000). Error and attack tolerance of complex networks. Nature, 406, 378–382.

Barabási, A.L. & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.

centrality-analysis community-detection temporal-network-analysis multilayer-network graph-neural-network


Neural Architecture Search

Nöral Mimari Arama (NAS)
NAS
automated architecture design
differentiable architecture search

Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.

Automated architecture optimization (deep learning)

Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.

Liu, H. et al. (2019). DARTS: Differentiable Architecture Search. ICLR.

knowledge-distillation mixture-of-experts random-forest xgboost longformer-bigbird


Machine Reading Comprehension

Neural Machine Reading Comprehension (MRC)

MRC
question answering over passages
extractive question answering
Makine Okuma Anlama (MRC)

Rajpurkar, Zhang, Lopyrev & Liang (SQuAD)

Machine reading comprehension (MRC), popularised by the SQuAD benchmark of Rajpurkar, Zhang, Lopyrev and Liang (2016), is a natural-language-processing task in which a model reads a given passage and answers multiple-choice or open-ended questions about it. It turns a passage plus a question into a machine-generated answer, supporting information retrieval, educational technology, and querying research databases.

Multiple-choice / extractive span / open-ended

Output: An answer drawn from or grounded in a given passage

Rajpurkar, P., Zhang, J., Lopyrev, K. & Liang, P. (2016). SQuAD: 100,000+ Questions for Machine Comprehension of Text. EMNLP, 2383-2392.

Yang, Z. et al. (2018). HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. EMNLP.


Neural ODE

Nöral Diferansiyel Denklem (Neural ODE)
neural ordinary differential equation
continuous-depth network
ODE-Net

A Neural ODE, introduced by Chen and colleagues in 2018, models a hidden state as the continuous solution of an ordinary differential equation whose dynamics are parameterised by a neural network. It generalises the limiting case of residual connections, making it well suited to irregularly spaced time series and physics-based modelling.

Continuous-depth neural network (ODE-parameterised dynamics)

Prediction & forecasting on continuous-time data

Chen, T. Q., Rubanova, Y., Bettencourt, J. & Duvenaud, D. (2018). Neural Ordinary Differential Equations. Advances in Neural Information Processing Systems (NeurIPS).

Rubanova, Y., Chen, T. Q. & Duvenaud, D. (2019). Latent ODEs for Irregularly-Sampled Time Series. Advances in Neural Information Processing Systems (NeurIPS).

recurrent-neural-network lstm random-forest xgboost physics-informed-neural-network


Neural Radiance Fields (NeRF)

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis

Deep Learning, 3D Vision, Generative Models

Neural Radiance Fields (NeRF) is a method introduced by Mildenhall et al. in 2020 that represents a 3D scene as a continuous function parameterized by a neural network. Given multi-view images of a scene, NeRF learns to predict the color and density of light rays at any spatial location and viewing angle, enabling novel view synthesis with photorealistic quality.

Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. In Computer Vision-ECCV 2020: 16th European Conference (pp. 405-421). Springer International Publishing.

latent-diffusion-models masked-autoencoders segment-anything-model detr


Neural Style Transfer

Neural Style Transfer via Convolutional Neural Network Feature Statistics

NST
artistic style transfer
neural artistic style
CNN style transfer
image style transfer

Neural Style Transfer (NST) is a deep-learning image synthesis technique, introduced by Gatys, Ecker, and Bethge in 2015, that separates the semantic content of one image from the visual texture and artistic style of another, then recombines them into a single synthesized image by iteratively optimizing pixel values to minimize a combined content and style loss computed from the feature maps of a pretrained convolutional neural network.

Iterative optimization over CNN feature statistics

Image synthesis (style + content combination)

Gatys, L. A., Ecker, A. S., & Bethge, M. (2016). Image Style Transfer Using Convolutional Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2414–2423.

Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A Neural Algorithm of Artistic Style. arXiv preprint arXiv:1508.06576.

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

convolutional-neural-network generative-adversarial-network variational-autoencoder image-segmentation transfer-learning


Neuromuscular Re-Education

motor retraining
motor control training
proprioceptive retraining

Physical therapy and motor control research

Neuromuscular re-education is a therapeutic approach using targeted exercise and sensory feedback to retrain motor control, proprioception, and movement patterns following neurological injury or dysfunction. Based on motor learning principles, neuromuscular re-education helps patients reestablish voluntary muscle activation, coordination, and functional movement through repetition, feedback, and progressive challenge.

Neumann, D. A. (2016). Kinesiology of the musculoskeletal system: Foundations for rehabilitation (3rd ed.). Elsevier.

Schmidt, R. A., & Lee, T. D. (2019). Motor control and learning: A behavioral emphasis (6th ed.). Human Kinetics.


Neuropathic Pain Scale

The Neuropathic Pain Scale (NPS) is a 10-item self-report instrument developed by Jensen and colleagues to measure the quality and intensity of pain associated with neuropathic conditions (nerve damage, peripheral neuropathy, post-herpetic neuralgia, spinal cord injury pain). The NPS captures pain descriptors (sharp, cold, burning, sensitive, itching) and sensations characteristic of neuropathic pain, distinguishing them from nociceptive (tissue-damage-related) pain.

Self-report scale measuring neuropathic pain quality and intensity

Kramer, H.H., Winkelmann, A., Sluka, K.A., & Malin, S.A. (2004). Neuropathic pain: Transmitter-based mechanisms to pharmacological intervention. Journal of Pain, 5(4), 204-221.

Dworkin, R.H., Backonja, M., Rowbotham, M.C., et al. (2003). Advances in neuropathic pain: Diagnosis, mechanisms, and treatment recommendations. Archives of Neurology, 60(11), 1524-1534.

Jensen, M.P., Wald, B., Trueman, S., & Lipton, R.B. (2007). Neuropathic Pain Scale: A reliable measure for assessing the characteristics of neuropathic pain. Clinical Journal of Pain, 23(1), 48-55.

mcgill-pain-questionnaire pain-catastrophizing-scale central-sensitization-inventory pain-anxiety-symptoms-scale


NPSI

Neuropathic Pain Symptom Inventory
NPSI Scale

The NPSI is a 12-item self-report questionnaire specifically designed to assess and quantify the diverse symptoms characteristic of neuropathic pain. Developed by Bouhassira and colleagues in 2004, it evaluates five distinct symptom dimensions: burning pain, pressing pain, paroxysmal pain, evoked pain, and paresthesias. The NPSI is widely used in research and clinical practice to characterize neuropathic pain profiles, quantify symptom severity, and track treatment response.

Bouhassira, D., Attal, N., Fermanian, J., Alchaar, H., Gautron, M., Masquet, B., Rostaing, S., Lanteri-Minet, M., Collin, E., Grisart, J., & Boureau, F. (2004). Development and validation of the Neuropathic Pain Symptom Inventory. Pain, 108(3), 248-257.

lanss migraine-disability-assessment stroke-specific-qol msqol-54


Neuropsychological Assessment

NPA
neuropsych exam
cognitive assessment

Neuropsychological assessment is a comprehensive evaluation of cognitive and behavioral functions using standardized tests and observations to identify brain-behavior relationships and diagnose neurocognitive disorders. Rooted in the pioneering work of Alexander Luria in the 1960s and systematized by contemporary neuropsychologists, the assessment is used to evaluate dementia, traumatic brain injury, stroke, learning disorders, and other conditions affecting brain function.

Comprehensive standardized testing battery

Lezak, M. D., Howieson, D. B., Loring, D. W., Hannay, H. J., & Fischer, J. S. (2004). Neuropsychological assessment (4th ed.). Oxford University Press.

Strauss, E., Sherman, E. M. S., & Spreen, O. (2006). A compendium of neuropsychological tests: Administration, norms, and commentary (3rd ed.). Oxford University Press.


Neutrino Oscillation Analysis

Neutrino Oscillation Parameter Measurement

oscillometry
mixing analysis
neutrino mixing

Neutrino oscillation analysis is the study of flavor mixing in the neutrino sector, where neutrinos born as one flavor (electron, muon, or tau) spontaneously convert into other flavors as they propagate. Measuring oscillation parameters provides crucial evidence for physics beyond the Standard Model and tests our understanding of the neutrino mass hierarchy.

Pontecorvo, B. (1957). Mesonium and antimesonium. Zhurnal Eksperimental'noi i Teoreticheskoi Fiziki, 33, 549.

Nakamura, K., et al. (Particle Data Group). (2016). Review of particle physics. Journal of Physics G: Nuclear and Particle Physics, 37(7A), 075021.

Esteban, I., et al. (2019). Global analysis of three-flavour neutrino oscillations. Journal of High Energy Physics, 2019(9), 178.


Neutron Transport Calculation

Neutron Transport Calculation and Simulation

neutron diffusion
neutron migration
transport equation solution

Neutron transport calculation is a computational method for determining the distribution and behavior of neutrons in a nuclear medium, developed during the Manhattan Project in the 1940s. It solves the Boltzmann transport equation to predict neutron flux, energy spectra, and reaction rates essential for reactor design and shielding analysis.

Duderstadt, J. J., & Hamilton, L. J. (1976). Nuclear Reactor Analysis. John Wiley & Sons.

Lewis, E. E., & Miller, W. F. (1977). Computational Methods of Neutron Transport. American Nuclear Society.

monte-carlo-neutron-particle radiation-dose-assessment reactor-kinetics criticality-safety-analysis radiation-shielding-design


NEP Scale

The New Ecological Paradigm (NEP) Scale measures endorsement of an ecocentric worldview that views humans as embedded within, rather than dominant over, nature. Developed by Dunlap et al. (2000) to update the original 1978 scale, the NEP assesses environmental beliefs across multiple dimensions including balance of nature, limits to growth, human exceptionalism, and nature's intrinsic value. It is widely used in environmental psychology, sustainability research, and conservation communication studies.

Dunlap, R. E., Van Liere, K. D., Mertig, A. G., & Jones, R. E. (2000). New trends in measuring environmental attitudes: measuring endorsement of the New Ecological Paradigm (NEP). Journal of Social Issues, 56(3), 425–442.


New York Heart Association Functional Classification

NYHA
NYHA Class
Functional Classification

heart failure functional status classification

The New York Heart Association (NYHA) Functional Classification is a four-category ordinal system for grading heart failure severity based on the level of physical activity that precipitates dyspnea or other HF symptoms. Established by the NYHA in 1928 and refined in 1994, the NYHA classification is the oldest and most widely used functional status metric in cardiology, providing a simple, clinically intuitive framework for describing HF symptom burden, guiding treatment intensity, and predicting prognosis.

Ordinal clinician-assessment classification system

The Criteria Committee of the New York Heart Association. (1994). Nomenclature and Criteria for Diagnosis of Diseases of the Heart and Great Vessels (9th ed.). Little, Brown and Company.

Dolgin, M. (for the Criteria Committee of the New York Heart Association). (1994). Nomenclature and criteria for diagnosis of diseases of the heart and great vessels. The Criteria Committee of the New York Heart Association. 9th ed. Boston, MA: Little, Brown.

minnesota-heart-failure kansas-city-cardiomyopathy duke-activity-status-index dyspnea-scale-borg


NBO

The NBO is a brief, observation-based system designed to illuminate newborn behavioral competencies and individual differences for parents and healthcare providers. Developed by J. Kevin Nugent and colleagues as a companion to the longer Neonatal Behavioral Assessment Scale (NBAS), the NBO uses 18 key behavioral observations to characterize newborn strengths and to guide parent education about infant cues, state regulation, and social capabilities. Unlike the NBAS, the NBO does not generate numerical scores but instead provides qualitative, parent-centered interpretation emphasizing infant strengths and readiness to interact.

Nugent, J. K., Keefer, C. H., Minear, S., Johnson, L. C., & Blanchard, Y. (2007). Understanding Newborn Behavior and Early Relationships: The Newborn Behavioral Observations (NBO) System Handbook. Brookes Publishing.

Nugent, J. K. (2006). The Newborn Behavioral Observations (NBO) System as a Nursing Intervention. Journal of Pediatric Nursing, 21(3), 200-207.


Newcastle-Ottawa Scale

Newcastle-Ottawa Scale for Observational Studies

Wells et al. (Ottawa Hospital Research Institute)

The Newcastle-Ottawa Scale (NOS) is a widely used tool for assessing the methodological quality of observational studies (case-control and cohort designs) included in systematic reviews and meta-analyses. Developed by Wells et al. at Ottawa Hospital in 2000, it provides explicit criteria and a star-based scoring system that enables transparent, quantitative comparison of study quality across evidence syntheses.

Wells, G. A., Shea, B., O'Connell, D., Peterson, J., Welch, V., Losos, M., & Tugwell, P. (2000). The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. Retrieved from Ottawa Hospital Research Institute.

cochrane-risk-of-bias strobe-checklist grade-evidence-profiling prisma-checklist


Newey-West HAC

Heteroskedasticity and Autocorrelation Consistent covariance

Newey-West HAC standard errors, introduced by Whitney Newey and Kenneth West in 1987, provide a covariance matrix estimator for OLS regression that remains valid under both heteroskedasticity and serial autocorrelation of unknown form. They are the standard tool for correcting inference in time-series and panel regression when residuals are not i.i.d., requiring no specification of the error structure beyond choosing a bandwidth parameter.

Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55(3), 703–708.


Newsvendor Model

Newsvendor (Single-Period Inventory) Model

Newsboy Model
Single-Period Inventory Model
Christmas Tree Problem
Gazete Satıcısı Modeli

The Newsvendor Model is a single-period stochastic inventory optimization framework that determines the profit-maximizing order quantity when demand is uncertain and unsold units cannot be carried forward. Formally introduced by Arrow, Harris, and Marschak (1951) in their foundational work on optimal inventory policy, the model balances the cost of ordering too much (overage) against the cost of ordering too little (underage) to yield a closed-form optimality condition known as the critical ratio.

Stochastic single-period inventory optimization

Arrow, K. J., Harris, T., & Marschak, J. (1951). Optimal inventory policy. Econometrica, 19(3), 250–272.


Newton-Raphson Power Flow

Newton-Raphson Method for Power Flow Analysis

The Newton-Raphson method is a powerful iterative technique for solving the nonlinear power flow equations in electrical power systems. Introduced by Tinney and Hart in 1967, it became the industry standard for computing steady-state voltage and power distributions across transmission networks. The method uses Jacobian matrix formulations to rapidly converge to the true operating point.

Iterative solution algorithm for power system steady-state analysis

Tinney, W. F., & Hart, C. E. (1967). Power flow solution by Newton's method. IEEE Transactions on Power Apparatus and Systems, 86(11), 1449-1460.

Stott, B. (1974). Review of load-flow calculation methods. Proceedings of the IEEE, 62(7), 916-929.

Saadat, H. (2010). Power System Analysis (3rd ed.). PSA Publishing.


NFW Halo Profile

NFW profile
dark matter density profile
halo model

Julio Navarro, Carlos Frenk, Simon White

The Navarro-Frenk-White (NFW) profile is a widely-adopted density profile for dark matter halos emerging from cosmological simulations. It provides a simple parametric description of how dark matter density varies with distance from the halo center, essential for modeling galaxy cluster mass distributions, weak lensing, and dark matter annihilation signals.

Navarro, J. F., Frenk, C. S., & White, S. D. M. (1997). A universal density profile from hierarchical clustering. The Astrophysical Journal, 490(2), 493.

Dutton, A. A., & Maccio, A. V. (2014). The abundance of dark matter haloes and the assembly of galaxies. Monthly Notices of the Royal Astronomical Society, 441(4), 3359–3374.

Diemer, B., & Kravtsov, A. V. (2015). A universal model for halo concentrations. The Astrophysical Journal, 799(2), 108.


N-gram Language Model

n-gram model
statistical language model
N-gram Dil Modeli

An n-gram language model is a statistical model that predicts the probability of the next word by looking only at the previous n−1 words. Described in detail by Jurafsky and Martin (Speech and Language Processing), it provides foundational infrastructure for text generation, spelling correction, and speech recognition.

Markov assumption over the previous n-1 words

Output: Probability of the next word given preceding context

Jurafsky, D. & Martin, J.H. (2023). Speech and Language Processing, 3rd ed.

Chen, S.F. & Goodman, J. (1999). An Empirical Study of Smoothing Techniques for Language Modeling. Computer Speech & Language, 13(4), 359-394.

tf-idf text-classification word-sense-disambiguation text-regression


N-HiTS

N-HiTS — Hiyerarşik İnterpolasyon Tahmini

N-HiTS (Neural Hierarchical Interpolation for Time Series Forecasting), introduced by Challu and colleagues in 2023, is a deep neural forecasting architecture that combines the hierarchical forecasts of multiple stacks operating at different sampling rates and merges them through interpolation. It extends N-BEATS to deliver markedly better accuracy on long forecast horizons.

Deep neural forecasting (hierarchical interpolation)

Challu, C. et al. (2023). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. AAAI.

Oreshkin, B.N. et al. (2020). N-BEATS: Neural Basis Expansion Analysis for Interpretable Time Series Forecasting. ICLR. arXiv: 1905.10437


Niche Modeling

species distribution modeling
habitat suitability modeling
ecological niche model
MaxEnt
GARP

Niche modeling, also called species distribution modeling (SDM), predicts the geographic range and habitat suitability of species using presence-only or presence-background occurrence data and environmental variables. MaxEnt (Maximum Entropy, Phillips et al. 2006) and GARP (Genetic Algorithm for Rule-set Prediction, Stockwell & Peters 1999) are two prominent algorithms. These methods identify the environmental conditions under which species are likely to occur, enabling prediction of distribution beyond sampled areas and assessment of habitat suitability across landscapes.

Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3-4), 231-259.

Stockwell, D. R., & Peters, D. P. (1999). The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science, 13(2), 143-158.

Elith, J., Phillips, S. J., Hastie, T., Dudik, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17(1), 43-57.

species-accumulation distance-sampling food-web-topology population-viability-analysis


NIHSS

National Institutes of Health Stroke Scale

Acute ischemic stroke severity assessment

The NIHSS is the standard acute stroke severity assessment tool used in emergency departments, stroke centers, and clinical trials worldwide. Developed by the NIH Stroke Study Group in 1989, the 15-item scale provides rapid, reproducible quantification of acute neurological deficit from ischemic or hemorrhagic stroke. NIHSS scores inform thrombolytic and thrombectomy eligibility, predict outcomes, and serve as primary endpoint in stroke intervention trials.

Brott, T., Adams, H. P., Olinger, C. P., et al. (1989). Measurements of acute cerebral infarction: A clinical examination scale. Stroke, 20(7), 864-870.

updrs edss-multiple-sclerosis hunt-hess-scale world-federation-neurosurgeons ischemic-stroke-functional-outcome


finger dexterity and fine motor coordination

The Nine-Hole Peg Test (9HPT) is a brief, quantitative, performance-based measure of fine motor hand dexterity and coordination. Developed by Mathiowetz and colleagues (1985) at the University of Minnesota, the 9HPT is one of the simplest and most widely used screening tests for hand function, particularly finger dexterity. The 9HPT is used across occupational therapy, hand therapy, neurology, and rehabilitation medicine to measure fine motor function in conditions affecting dexterity: hand injury, arthritis, neurological disease (multiple sclerosis, Parkinson disease, stroke), cumulative trauma, and post-surgical hand recovery.

Performance-based, timed assessment by clinician

Mathiowetz, V., Weber, K., Kashman, N., & Volland, G. (1985). Adult norms for the Nine-Hole Peg Test of finger dexterity. Occupational Therapy Journal of Research, 5(1), 24-38.

Cutter, N. C., Baier, M. L., Cohen, J. L., Batalden, K. A., Courtney, T., Eckert, S. L., ... & Bhuiyan, C. B. (1993). Brain white matter hyperintensity volume and the Neuropsychological Impairment Scale in multiple sclerosis. Archives of Neurology, 56(12), 1524-1530.

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