Hydroponic Nutrient Solution Management

Composition, Monitoring, and Optimization of Nutrient Solutions in Soilless Production Systems

nutrient solution formulation
hydroponic monitoring
EC/pH management

Nutrient management and soilless production

Hydroponic nutrient solution management involves formulating, monitoring, and adjusting the chemical composition of water-based growing media to deliver optimal nutrition without soil. This method combines analytical chemistry (nutrient analysis, pH, electrical conductivity) with plant physiology to diagnose deficiencies and optimize yield and quality. It is essential for commercial hydroponics, vertical farms, and propagation systems where precision nutrition directly impacts profitability.

Resh, H. M. (2012). Hydroponic Food Production (7th ed.). CRC Press.

Jones, J. B. (2005). Hydroponics: A Practical Guide for the Soilless Grower (2nd ed.). CRC Press.

greenhouse-climate-control phenological-stage-monitoring postharvest-storage-simulation plant-propagation-success


Hydrothermal Plume Mapping

Vent Plume Detection
Hydrothermal Vent Survey

Hydrothermal plume mapping is an integrated method for detecting, characterizing, and tracking buoyant plumes of hot, mineral-rich water discharged from submarine hydrothermal vents on the seafloor. Developed by Ed Baker and colleagues in the 1980s, hydrothermal plume mapping combines temperature, conductivity, optical, and chemical sensors to identify vent signatures and map their dispersal in the water column. The method enables discovery of new vents and assessment of chemical cycling in deep-sea ecosystems.

Baker, E. T., Massoth, G. J., Feely, R. A., et al. (1987). Hydrothermal event plumes from the Juan de Fuca Ridge. Eos, Transactions American Geophysical Union, 68(44), 1574.

Christie, D. M., Carbotte, S. M., Coaxe, R. J., et al. (2007). The CORA volumes and plume mapping. Ridge 2000 Events Workshop, Santa Fe, NM.


Hyper-Heuristics

Heuristic of Heuristics
Algorithm Selection Hyper-Heuristic
Selection Hyper-Heuristic
Hiyer-Sezgisel

Hyper-heuristics are high-level methodologies that search over a space of heuristics rather than directly over the space of solutions. Introduced systematically by Burke et al. (2013) in their landmark survey, hyper-heuristics operate by selecting or generating low-level heuristics to solve hard combinatorial optimisation and search problems, aiming to automate the design of optimisation algorithms across diverse problem domains without requiring deep problem-specific knowledge.

Burke, E. K., et al. (2013). Hyper-heuristics: A survey of the state of the art. Journal of the Operational Research Society, 64(12), 1695–1724.


Hyperarousal Scale

Hyperarousal Scale
Sleep-Related Hyperarousal

Riemann, D., Krone, L. B., Wulff, K., Nissen, C.

The Hyperarousal Scale is an assessment tool measuring elevated physiologic and cognitive activation during sleep and wakefulness in insomnia patients. Rooted in contemporary understanding of insomnia as a disorder of hyperarousal (excessive vigilance, elevated muscle tension, racing thoughts, heightened startle response), the scale quantifies the degree to which increased arousal level contributes to insomnia. Hyperarousal is increasingly recognized as a core mechanism underlying insomnia, distinguishing insomnia from simple sleep deprivation.

Riemann, D., Krone, L. B., Wulff, K., & Nissen, C. (2020). Sleep, insomnia, and depression. Neuropsychopharmacology, 45(1), 74-89.


Hyperspectral Unmixing

Spectral Unmixing of Hyperspectral Imagery

Spectral Mixture Analysis
Linear Spectral Unmixing
Blind Source Separation (Hyperspectral)
Hiperspektral Ayrıştırma

Hyperspectral unmixing is a signal processing technique that decomposes each pixel of a hyperspectral image into a collection of pure material spectra (endmembers) and their corresponding fractional abundances. Because sensor resolution often causes multiple land-cover types to co-occupy a single pixel, unmixing recovers sub-pixel compositional information that conventional classification cannot. Keshava and Mustard (2002) provided the foundational signal-processing framework that unified prior geological and remote-sensing work under a rigorous linear mixture model.

Sub-pixel spectral decomposition algorithm

Input: Hyperspectral image cube (pixels × bands)

Output: Endmember spectra + fractional abundance maps

Keshava, N., & Mustard, J. F. (2002). Spectral unmixing. IEEE Signal Processing Magazine, 19(1), 44–57.


Hyperthyroidism Symptom Checklist

Hyperthyroidism Symptom Checklist: Graves' Disease and Thyrotoxicosis Assessment

Multiple authors; consensus from Endocrine Society

The Hyperthyroidism Symptom Checklist is a structured assessment tool for quantifying the symptom burden of Graves' disease and other thyrotoxicosis conditions. It captures the multisystem manifestations of excess thyroid hormone: cardiovascular (palpitations, tachycardia, arrhythmia), neuropsychiatric (anxiety, tremor, insomnia), metabolic (heat intolerance, weight loss, appetite), and eye-specific symptoms (in Graves' ophthalmopathy). Used in endocrinology practice and clinical trials to monitor disease activity and treatment response.

Engel, G. L., Dayan, A. D., Thorn, G. W., et al. (1997). Hyperthyroidism: A multisystem disease with diverse manifestations. Adv Intern Med, 42, 55-91.

Boelaert, K., Torlinska, B., Holder, R. L., & Franklyn, J. A. (2009). Older subjects with hyperthyroidism present with a different symptom pattern than younger patients. J Clin Endocrinol Metab, 94(8), 2815-2820.


HAQ
Clarke HAQ
Hypoglycemia Awareness Scale

Hypoglycemia-specific awareness and symptom detection

William L. Clarke, Don J. Cox, Lois A. Gonder-Frederick

The Clarke Hypoglycemia Awareness Questionnaire is an 8-item instrument designed to identify patients with impaired hypoglycemia awareness—a condition in which diabetic patients fail to perceive early warning symptoms of low blood glucose, substantially increasing their risk of severe hypoglycemia. Developed by Clarke and colleagues in 1995, it is the most widely used screening tool for this dangerous complication in type 1 diabetes and in insulin-treated type 2 diabetes populations.

Clarke, W. L., Cox, D. J., Gonder-Frederick, L. A., Julian, D., Schlundt, D., & Polonsky, W. (1995). Reduced awareness of hypoglycemia in adults with IDDM: A prospective study of hypoglycemic frequency and associated symptoms. Diabetes Care, 18(6), 517-522.

Gold, A. E., MacLeod, K. M., Frier, B. M., et al. (2012). Frequency of severe hypoglycemia in patients with type 1 diabetes with impaired awareness of hypoglycemia. Diabetes Care, 17(9), 697-703.


Hypoglycemia Fear Survey

hypoglycemia-specific fear and anxiety in type 1 diabetes

The Hypoglycemia Fear Survey (HFS) is a self-report measure that quantifies fear of, anxiety about, and behavioral responses to hypoglycemia in patients with type 1 diabetes and insulin-treated type 2 diabetes. Originally developed by Cox and colleagues in 1987 and revised (HFS-II) in 1993, the HFS captures the emotional and behavioral impact of hypoglycemia risk, particularly the tendency to run higher blood glucose to avoid episodes, making it essential for understanding a major barrier to optimal glucose control in insulin-dependent diabetes.

Cox, D. J., Irvine, A., Gonder-Frederick, L., Nowacek, G., & Butterfield, G. (1987). Fear of hypoglycemia: Quantification, validation, and utilization. Diabetes Care, 10(5), 617–621.

Cox, D. J., Ritterband, L. M., Hessler, D., Polis, S., Sinha, A., & Gonder-Frederick, L. (2007). Accuracy of perceived blood glucose in adults with type 1 diabetes. Diabetes Care, 30(3), 529–535.


Hypothesis Development

Hypothesis Development and Testing Framework

H0 and H1
null and alternative hypothesis

Ronald Fisher (1920s) and Neyman-Pearson (1930s)

A hypothesis is a testable prediction or proposed explanation for a phenomenon, expressed as a relationship between variables. Hypothesis development is the process of formulating null hypotheses (H₀, asserting no effect or relationship) and alternative hypotheses (H₁, asserting an effect or relationship) before data collection. This framework emerged from frequentist statistical theory developed by Ronald Fisher in the 1920s and refined by Neyman and Pearson in the 1930s. Hypotheses are essential in quantitative research because they translate research questions into statements that can be tested using statistical inference.

Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver & Boyd.

Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society, 231(A), 289–337.

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


Hypothesis Testing Research

hypothetico-deductive research
confirmatory quantitative research
null hypothesis significance testing
NHST design

Early 20th century (Fisher 1925; Neyman–Pearson 1933)

Karl Pearson, Ronald A. Fisher, Jerzy Neyman, Egon Pearson

Hypothesis testing research is a quantitative design in which the investigator derives one or more explicit, falsifiable propositions from theory, translates them into a null hypothesis (H0) and an alternative hypothesis (H1), collects empirical data, and then applies an inferential statistical test to decide whether the evidence is sufficient to reject H0. The approach is the dominant paradigm for confirmatory science across the social, behavioral, health, and natural sciences.

Quantitative confirmatory research design

Numeric measurements, test scores, counts, ratings

Kerlinger, F. N., & Lee, H. B. (1986). Foundations of Behavioral Research (3rd ed.). Holt, Rinehart and Winston.

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.

confirmatory-research model-testing-research correlational-research causal-comparative-research exploratory-quantitative-research experimental-design


HYSPLIT

HYSPLIT
Hybrid Single-Particle
Lagrangian trajectory model

HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory Model) is a widely used atmospheric transport and dispersion model developed by NOAA's Air Resources Laboratory. It computes air parcel trajectories and pollutant dispersion using Lagrangian tracking to simulate how contaminants and particles move through the atmosphere over hours to weeks.

Draxler, R. R., & Hess, G. D. (1997). Description of the HYSPLIT_4 modeling system. NOAA Technical Memorandum ERL ARL-224.

Stein, A. F., Draxler, R. R., Rolph, G. D., et al. (2015). NOAA's HYSPLIT atmospheric transport and dispersion modeling system. Bulletin of the American Meteorological Society, 96(12), 2059-2077.


IBD Mapping

Identity-by-Descent Mapping for Disease Loci Detection

IBD mapping
Autozygosity mapping
Homozygosity mapping

Identity-by-descent (IBD) mapping is a genetic mapping technique that identifies disease loci in consanguineous families or isolated populations by detecting homozygous chromosomal segments shared among affected individuals. Developed by Lander and Botstein in 1987, this method exploits the fact that rare disease alleles in related individuals must lie within shared ancestral DNA blocks. By mapping regions where affected individuals are homozygous at multiple markers, researchers can localize disease genes to narrowly defined genomic intervals without prior knowledge of the disease mechanism.

Lander, E. S., & Botstein, D. (1987). Homozygosity mapping of autosomal recessive disorders in consanguineous families. American Journal of Human Genetics, 36(3), 537–551.

Koch, L., & Möller, A. (2000). Identity-by-descent mapping: theory and application. Clinical Genetics, 57(5), 337–348.

Browning, B. L., & Browning, S. R. (2010). Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics, 176(4), 2427–2437.

qtl-mapping ld-block-analysis f-statistics polygenic-risk-score


Guyonnet, D., Chassany, O., Ducroc, R., et al.

The Short Inflammatory Bowel Disease Questionnaire (IBDQ-32) is a validated patient-reported outcome measure designed to assess the impact of inflammatory bowel disease (IBD)—both ulcerative colitis and Crohn's disease—on health-related quality of life. Derived from the original 32-item IBDQ, this instrument comprises four domains: Bowel Symptoms, Systemic Symptoms, Social Function, and Emotional Function. The IBDQ-32 is responsive to treatment and is increasingly used in IBD clinical trials and practice.

Guyonnet, D., Chassany, O., Ducroc, R., Picard, C., Mouret, M., D'Haens, G., & Svartz, H. (2004). Effect of fermented milk containing Bifidobacterium animalis DN-173 010 on the health-related quality of life and symptoms in irritable bowel syndrome in adults in France: A multicentre, randomized, double-blind, controlled trial. Alimentary Pharmacology & Therapeutics, 20(4), 459–465.

mayo-score-uc harvey-bradshaw-index cdai-crohns sccai


Intraclass Correlation Coefficient

ICC
intraclass correlation
rater reliability coefficient
Sınıf İçi Korelasyon Katsayısı (ICC)

The Intraclass Correlation Coefficient (ICC) is a parametric reliability statistic that quantifies the degree of agreement or consistency among repeated measurements or multiple raters on a continuous outcome. The modern six-form taxonomy was established by Shrout and Fleiss in 1979 and remains the standard framework for selecting and reporting ICC in inter-rater reliability, test-retest repeatability, and multilevel-data analyses.

6 (ICC1,1 · ICC1,k · ICC2,1 · ICC2,k · ICC3,1 · ICC3,k)

< 0.50 poor · 0.50–0.75 moderate · 0.75–0.90 good · > 0.90 excellent (Koo & Li, 2016)

Shrout, P.E. & Fleiss, J.L. (1979). Intraclass Correlations: Uses in Assessing Rater Reliability. Psychological Bulletin, 86(2), 420–428.

Koo, T.K. & Li, M.Y. (2016). A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine, 15(2), 155–163.

cohens-kappa fleiss-kappa bland-altman pearson-correlation cronbach-alpha


ICIQ Urinary Incontinence Short Form

ICIQ Urinary Incontinence Short Form (ICIQ-SF)

The ICIQ-SF is a brief, four-item self-report measure designed to assess the frequency, severity, and impact of urinary incontinence symptoms in both men and women. Developed by Avery and colleagues in 2004, it combines high psychometric utility with practical brevity, making it ideal for routine clinical screening and outcome measurement in primary care, urology, and gynecology settings.

Avery, K., Donovan, J., Peters, T. J., Shaw, C., Gotoh, M., & Abrams, P. (2004). ICIQ: a brief and robust measure for evaluating the symptoms and impact of incontinence. Neurourology and Urodynamics, 23(4), 322–330.

Brookes, S. T., Donovan, J. L., Wright, M., Jackson, S., & Abrams, P. (2004). A scored form of the Bristol Female Lower Urinary Tract Symptoms questionnaire: data from a randomized controlled trial of surgery for women with stress incontinence. American Journal of Obstetrics and Gynecology, 191(1), 73–80.


ICMJE Authorship Criteria

International Committee of Medical Journal Editors Authorship Criteria

The International Committee of Medical Journal Editors (ICMJE) established the most widely adopted authorship standard in biomedical research in 1978. These criteria define who qualifies as an author and distinguish authors from contributors, establishing accountability and preventing disputes over publication credit. Used by over 10,000 journals globally, ICMJE authorship criteria form the foundation of authorship practices in medical, life science, and health-related research.

International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. ICMJE.

Drummond, M. F., Jefferson, T. O., & British Medical Association. (2009). Guidelines for Authors and Peer Reviewers of Economic Submissions to the BMJ. BMJ, 313(7052), 275–283.

duplicate-publication plagiarism-in-research cope-guidelines peer-review-process


Icon Usability Testing

Symbol Recognition Testing
Icon Comprehension Evaluation

User interface comprehension and accessibility

Icon Usability Testing is a systematic method for evaluating how well users understand the meaning of graphical icons and symbols. By combining comprehension testing, task performance measurement, and preference assessment, this pipeline ensures that icons effectively communicate their intended functions across diverse user populations.

Blenkhorn, P., & Evans, G. (1994). Investigations into the Comprehension of Symbolic Road Behaviour Displays using Icon-like Pictorial Symbols. Displays, 15(2), 87–97.

McGinley, C., Fonseca, B., Curry, R., Goodman-Deane, J., & Clarkson, J. (2015). From Retro to Retinal: Evaluating Smartphone Icon Comprehensibility. In S. Miesenberger, et al. (Eds.), Computers Helping People with Special Needs. Springer.

Hurtienne, J., Israel, J. H., & Weber, K. (2005). Cooking the Perfect Fit Just Right: HCI Cooking and Iterative Recipes for Usability Engineering in Software Projects. In INTERACT 2005. Springer.

typography-legibility-test visual-saliency-map color-harmony-analysis gestalt-principles-analysis contrast-ratio-measurement


Idea Plagiarism and Concept Theft

Idea Plagiarism and Concept Theft: Presenting Another's Ideas or Arguments as One's Own

conceptual plagiarism
idea theft
intellectual theft

Academic integrity framework (modern definition)

Idea plagiarism, or conceptual plagiarism, occurs when an author takes another's ideas, arguments, theories, or conceptual frameworks and presents them as original work without crediting the source. Unlike verbatim or paraphrasing plagiarism (which involve copying language), idea plagiarism involves taking the intellectual content itself—the argument, theory, or framework—regardless of how it is worded. It is the hardest form of plagiarism to detect because it does not require word-for-word copying.

Hirsch, L. R. (2013). Recognizing plagiarism: A guide for academic professionals. Teaching Professor Blog.

Steneck, N. H. (2007). Introduction to the responsible conduct of research. U.S. Department of Health and Human Services Office of Research Integrity.

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

paraphrasing-plagiarism mosaic-plagiarism academic-integrity-policies similarity-vs-plagiarism


IDOCRIW

IDOCRIW (Integrated Determination of Objective CRIteria Weights) is a weight objective multi-criteria decision-making (MCDM) method introduced by Zavadskas, E. K., Podvezko, V. in 2012. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Integrated objective weighting (ENTROPY × CILOS product-normalisation)

Zavadskas, E. K., Podvezko, V. (2012). Integrated Determination of Objective Criteria Weights in MCDM. International Journal of Information Technology & Decision Making

ahpsort aploco aras aroman artasi cobra cocoso codas


IF-ARAS

IF-ARAS (Intuitionistic Fuzzy ARAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Utility-ratio ranking under Intuitionistic Fuzzy uncertainty

Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems

if-ahp if-bwm if-swara if-entropy if-merec ahp bwm entropy


IF-COCOSO

IF-COCOSO (IF-CoCoSo — Intuitionistic extension of COCOSO) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Intuitionistic outranking/ranking — Intuitionistic Fuzzy Number (IFN: μ, ν; μ+ν ≤ 1)

ahp anp bwm bwm-bayesian ccsd cilos cimas critic


IF-CODAS-SORT

IF-CODAS-SORT (Intuitionistic Fuzzy CODAS-SORT (sorting via Euclidean+Hamming relative assessment)) is a sorting multi-criteria decision-making (MCDM) method introduced by Ouhibi, A. Moalla Frikha, H. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

IFN sorting with limiting profiles — relative assessment R(a,b) = (E_a−E_b) + ψ(E_a−E_b)·(H_a−H_b)

Ouhibi, A., Moalla Frikha, H. (2021). An Intuitionistic Fuzzy Extension of the CODAS-SORT Method. Multiple Criteria Decision Making


IF-CODAS

IF-CODAS (TIF-CODAS — Triangular Intuitionistic Fuzzy Group CODAS (TIFN-CODAS)) is a ranking multi-criteria decision-making (MCDM) method introduced by Daami Remadi, F., Moalla Frikha, H. in 2020. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Triangular Intuitionistic Fuzzy ranking — TIFN: {(a1,a2,a3);(a'1,a2,a'3)} with membership and non-membership triangles

Daami Remadi, F., Moalla Frikha, H. (2020). The Triangular Intuitionistic Fuzzy Extension of the CODAS Method for Solving Multi-Criteria Group Decision Making. 2020 IEEE Conference (ISITD or similar — 978-1-7281-6403-8/20)


IF-COPRAS

IF-COPRAS (Intuitionistic extension of COPRAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


IF-DFT

IF-DFT (Intuitionistic Fuzzy Decision Field Theory (Hao-Xu-Zhao-Zhang 2017)) is a ranking multi-criteria decision-making (MCDM) method introduced by Hao, Z. N. Xu, Z. S. Zhao, H. Zhang, R. in 2017. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Dynamic process-oriented decision making: Intuitionistic Fuzzy preference states evolve over deliberation time via a feedback matrix M; valence vector V(t) accumulates pairwise comparisons; stopping criterion on variance; predicts attraction/similarity/compromise effects.

Hao, Z. N., Xu, Z. S., Zhao, H., Zhang, R. (2017). Novel intuitionistic fuzzy decision making models in the framework of decision field theory. Information Fusion


IF-EDAS

IF-EDAS (Intuitionistic Fuzzy EDAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Distance-from-average ranking under Intuitionistic Fuzzy uncertainty


IF-ENTROPY

IF-ENTROPY (Intuitionistic Fuzzy Entropy Weight Method (Vlachos-Sergiadis 2007 entropy measure as applied by Hung-Chen 2010)) is a weight objective multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Information-theoretic objective weighting under Intuitionistic Fuzzy uncertainty (IF entropy → divergence → simplex-normalised crisp weights)

if-aras if-cocoso if-codas if-copras if-edas if-gra if-mabac if-marcos


IF-GRA

IF-GRA (Intuitionistic extension of GRA) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


IF-MABAC

IF-MABAC (Intuitionistic extension of MABAC) is a ranking multi-criteria decision-making (MCDM) method introduced by Li, Y. in 2021. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016-3028. [Canonical MABAC source; cited in place of the retracted Li (2021) intuitionistic-fuzzy MABAC paper, doi:10.1155/2021/5536751.]


IF-MARCOS

IF-MARCOS (Intuitionistic extension of MARCOS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


IF-MAUT

IF-MAUT (Intuitionistic Fuzzy Multi-Attribute Utility Theory (IFWA-based additive utility)) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Aggregation-operator-based additive ranking under Intuitionistic Fuzzy uncertainty (IFN: μ, ν; μ+ν ≤ 1)


IF-MOORA

IF-MOORA (Intuitionistic extension of MOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


IF-MULTIMOORA

IF-MULTIMOORA (Intuitionistic extension of MULTIMOORA) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


IF-PROMETHEE

IF-PROMETHEE (Intuitionistic Fuzzy PROMETHEE) is a outranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Pairwise outranking under Intuitionistic Fuzzy uncertainty


IF-SAW

IF-SAW (Intuitionistic extension of SAW) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


IF-TODIM

IF-TODIM (Intuitionistic Fuzzy TODIM) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Prospect-theory pairwise dominance under Intuitionistic Fuzzy uncertainty


IF-TOPSIS

IF-TOPSIS (Intuitionistic Fuzzy TOPSIS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Distance-based ranking under Intuitionistic Fuzzy uncertainty

if-ahp if-bwm if-swara if-entropy ahp bwm entropy critic


IF-VIKOR

IF-VIKOR (Intuitionistic extension of VIKOR) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


IF-WASPAS

IF-WASPAS (Intuitionistic extension of WASPAS) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


IF-WPM

IF-WPM (Intuitionistic extension of WPM) is a ranking multi-criteria decision-making (MCDM) method introduced by Atanassov, K. T. in 1986. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.


IIR Filter Design

IIR Design
Recursive filter design
Feedback filter

Infinite Impulse Response (IIR) filters are recursive digital filters that use feedback to achieve sharp frequency response characteristics with minimal filter order. Unlike FIR filters which depend only on past inputs, IIR filters also use past output values, allowing them to achieve steep rolloff with fewer coefficients. However, this feedback structure requires careful stability analysis.

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

Jackson, L. B. (2013). Digital Filters and Signal Processing (3rd ed.). Kluwer Academic Publishers.

fir-filter-design butterworth-filter-design chebyshev-filter-design wiener-filter


IKDC Subjective Knee Form

International Knee Documentation Committee (IKDC)

The IKDC Subjective Knee Form is an 18-item patient self-report instrument that measures knee function and symptoms in individuals with various knee conditions. Developed by the International Knee Documentation Committee in 2001 and published in the American Journal of Sports Medicine, it has become the gold standard for assessing knee-specific outcomes across diverse populations, from athletes returning to sport after anterior cruciate ligament injury to patients with osteoarthritis or meniscal pathology.

Irrgang JJ, Anderson AF, Boyle JB, et al. Development and validation of the International Knee Documentation Committee Subjective Knee Form. Am J Sports Med. 2001;29(5):600-613.

lysholm-knee-scale patient-specific-functional-scale global-rating-of-change-scale lower-extremity-functional-scale acl-return-to-sport-scale


Illness Perception Questionnaire Revised

Rosalyn Moss-Morris, John Weinman, Keith J. Petrie, and colleagues

The Illness Perception Questionnaire—Revised (IPQ-R) is a 70-item measure (brief version: 38 items) developed by Moss-Morris and colleagues (2002) to assess how individuals perceive and cognitively represent their illness. Based on Leventhal's Common Sense Model of illness representation, the IPQ-R measures nine dimensions: Identity (symptoms associated with the illness), Timeline (perceived duration and course), Consequences (expected impacts on functioning and quality of life), Personal Control (perceived ability to influence the illness), Treatment Control (perceived effectiveness of medical treatment), Illness Coherence (understanding of the illness), Concern (worry about the illness), Emotions (emotional responses to the illness), and Causation (attributed causes of illness). These cognitive representations profoundly influence coping behaviors, treatment adherence, emotional well-being, and actual health outcomes. The IPQ-R is widely used in chronic disease management (diabetes, asthma, cardiac disease, arthritis), mental health, rehabilitation, and health psychology research to understand illness-specific beliefs and to guide psychosocial interventions.

Moss-Morris, R., Weinman, J., Petrie, K. J., Horne, R., Cameron, L. D., & Buick, D. (2002). The Revised Illness Perception Questionnaire (IPQ-R). Psychology and Health, 17(1), 1-16.


Im-Pesaran-Shin Test

Im-Pesaran-Shin (IPS) Panel Unit-Root Test

IPS Test
IPS Panel Unit-Root Test
Heterogeneous Panel Unit-Root Test
Im-Pesaran-Shin Birim Kök Testi

The Im-Pesaran-Shin (IPS) test, introduced by Im, Pesaran, and Shin in 2003, is a panel unit-root test designed for heterogeneous panels where the autoregressive coefficient is allowed to differ across cross-sectional units. It averages individual Augmented Dickey-Fuller (ADF) t-statistics and constructs a standardized statistic with a standard normal limiting distribution, making it one of the most widely applied first-generation panel unit-root tests in applied econometrics.

Panel unit-root test allowing cross-sectional heterogeneity

All cross-sectional units contain a unit root

Some (but not necessarily all) cross-sectional units are stationary

Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74.


Image Aesthetics Assessment

Computational Aesthetics Evaluation
Photo Quality Scoring

Computational aesthetics and computer vision

Image Aesthetics Assessment is a computational pipeline for predicting and quantifying the aesthetic quality of photographs and digital images. Drawing from computer vision and human perception research, this method extracts low-level visual features and applies machine learning or rule-based scoring to estimate how viewers will perceive image quality and beauty.

Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2006). Studying Aesthetics in Photographic Images Using a Computational Approach. Computer Vision—ECCV 2006, 3953, 288–301.

Murray, N., Marchesotti, L., & Perronnin, F. (2012). AVA: A Large-scale Database for Aesthetic Visual Analysis. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Kong, S., Shen, X., Lin, Z., Mech, R., & Fowlkes, C. (2016). Photo-Sketching: Inferring Contours and Tones from Images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

visual-complexity-measure color-harmony-analysis visual-balance-measurement visual-saliency-map gestalt-principles-analysis


Image Classification

visual classification
image recognition
CNN-based classification
visual categorization

2012 (deep CNN era); conceptual roots 1989 (LeCun)

Krizhevsky, A.; Sutskever, I.; Hinton, G. E.

Image classification is the task of assigning a single semantic label to an entire image from a fixed set of categories. Modern approaches rely on deep convolutional neural networks (CNNs) or Vision Transformers (ViTs) trained end-to-end on large labeled datasets such as ImageNet, achieving superhuman accuracy on many benchmarks and underpinning applications from medical imaging to autonomous vehicles.

Labeled image datasets (RGB or grayscale rasters)

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems (NeurIPS), 25, 1097–1105.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.


Image Morphology Operations

Morphological Image Processing Operations

Mathematical morphology
Morphological filtering

Morphological image processing, introduced by Jean Serra in 1982, is a technique based on set theory that reshapes and analyzes image regions using geometric structuring elements. Core operations include erosion and dilation, which can be combined into more complex operations like opening and closing, enabling noise removal, edge detection, and object analysis.

Set theory and topological image processing

Serra, J. (1982). Image Analysis and Mathematical Morphology. Academic Press.

Haralick, R. M., Sternberg, S. R., & Zink, X. (1987). Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(4), 532–550.

canny-edge-detection watershed-segmentation contour-analysis blob-detection histogram-equalization


Imaging Mass Cytometry

IMC
mass cytometry
multiplex ion beam imaging
MIBI

Imaging Mass Cytometry (IMC) is a multiplexed proteomics technique that maps the subcellular localization of up to 40-50 proteins in tissue sections simultaneously using mass spectrometry detection. Developed by Bodenmiller and colleagues in 2014, IMC combines the single-cell imaging power of immunofluorescence with the multiplexing capacity of mass cytometry, enabling comprehensive analysis of cell types, states, and spatial interactions within tissue microenvironments. IMC has emerged as a powerful tool in immuno-oncology, immunobiology, and tissue biology for dissecting cellular heterogeneity and spatial organization.

Multiplexed single-cell imaging by mass spectrometry

Giesen, C., Wang, H. A., Schapiro, D., et al. (2014). Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature Methods, 11(4), 417-422.

Jackson, H. W., Fischer, J. R., Zanotelli, V. R., et al. (2020). The single-cell pathology of synovial inflammation in rheumatoid arthritis. Nature Medicine, 26(6), 941-951.

Schulz, D., Zanotelli, V. R., Fischer, J. R., et al. (2018). Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by imaging mass cytometry. Nature Protocols, 13(12), 2825-2848.

radiomics pet-kinetic-modeling quantitative-susceptibility-mapping oct-angiography functional-ultrasound


Journal Impact Factor

IF
JIF
Impact Factor
2-year Impact Factor

Eugene Garfield, Institute for Scientific Information (ISI)

Journal Impact Factor (JIF) is a metric developed by Eugene Garfield in 1955 and published annually by Clarivate Analytics through Journal Citation Reports (JCR). It measures the average citation frequency of articles published in a journal over a two-year window, serving as a proxy for journal prestige and influence. A journal's Impact Factor equals the number of citations received in year Y to articles published in Y-1 and Y-2, divided by the number of citable items published in that same window. Despite widespread adoption in research evaluation, Impact Factor has significant limitations and critics argue it conflates journal prestige with article quality.

Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178(4060), 471-479.

Clarivate Analytics. (2023). Journal Citation Reports: Impact Factor Methodology. https://clarivate.com/webofsciencegroup/essays/journal-citation-reports-methodology/

San Francisco Declaration on Research Assessment. (2012). Retrieved from https://sfdora.org/

web-of-science journal-citation-reports scimago-journal-rank h-index scopus-database


Impact of Event Scale Revised

The IES-R is a 22-item self-report scale measuring subjective distress from a specific traumatic event. Developed by Weiss and Marmar in 1997 as a revision of the original 1979 Impact of Event Scale, it assesses posttraumatic stress symptoms along three core dimensions: intrusion, avoidance, and hyperarousal. The scale is widely used in clinical research, trauma assessment, and treatment monitoring.

Weiss, D. S., & Marmar, C. R. (1997). The Impact of Event Scale—Revised. In J. P. Wilson & T. M. Keane (Eds.), Assessing psychological trauma and PTSD (pp. 399-411). Guilford Press.

Horowitz, M. J., Wilner, N., & Alvarez, W. (1979). Impact of Event Scale: A measure of subjective stress. Psychosomatic Medicine, 41(3), 209-218.


Impact on Participation and Autonomy

The Impact on Participation and Autonomy (IPA) scale is a validated, patient-centered measure designed to quantify how chronic conditions or disabilities affect an individual's autonomy and participation in five key life domains: autonomy, mobility, occupation, social relations, and recreation. Developed in the Netherlands by Cardol and colleagues, it operationalizes the WHO handicap concept (now called 'participation restriction') and is widely used in rehabilitation, chronic disease management, and policy evaluation across Europe.

Cardol, M., de Haan, R. J., de Jong, B. A., van den Bos, G. A., & de Groot, I. J. (2001). Psychometric properties of the Impact on Participation and Autonomy questionnaire. Archives of Physical Medicine and Rehabilitation, 82(2), 210–216.

Cardol, M., de Haan, R. J., van den Bos, G. A., de Jong, B. A., & de Groot, I. J. (2002). The development of a handicap assessment questionnaire: The Impact on Participation and Autonomy (IPA). Clinical Rehabilitation, 13(6), 411–419.

community-integration-questionnaire whodas-2 assessment-life-habits participation-measure-post-acute participation-scale


IVI

The Impact of Vision Impairment (IVI) scale is a quality-of-life instrument designed specifically for patients with significant vision loss (low vision) to measure the psychological, functional, and social burden of visual impairment. Developed by Wolffsohn, Cochrane, and colleagues (2000), the IVI captures domains including emotional impact (distress, frustration), functional limitations (mobility, ADLs), social participation, and role fulfillment in populations with moderate to severe vision loss where generic or mild-vision-focused instruments are insensitive.

Wolffsohn, J. S., & Cochrane, A. L. (2000). Design of the low vision quality-of-life questionnaire (LVQOL) and measurment of its item and scale validity and reliability. Optometry & Vision Science, 77(3), 144-152.

Owsley, C., McGwin, G., & Scilley, K. (2007). The VisionCare Study: design and methods. Curr Eye Res, 32(4), 325-332.

nei-vfq-25 low-vision-quality-of-life visual-function-index glaucoma-quality-of-life


Impedance Tube

Acoustic Impedance Tube for Material Property Measurement

kundt tube
resonance tube
acoustic absorption
sound absorption coefficient

An impedance tube (or Kundt tube) is a laboratory apparatus for measuring the acoustic absorption coefficient and surface impedance of materials. Originally developed by August Kundt in 1866, the technique has been standardized by ASTM and ISO for characterizing noise-control and acoustic-treatment materials. The impedance tube method is simple, portable, and cost-effective, making it the industry standard for pre-design acoustic material selection and quality control.

ASTM E1050-19 (2019). Standard Test Method for Impedance and Absorption of Acoustical Materials Using a Tube, Two Microphone and a Digital Frequency Analysis System. American Society for Testing and Materials.

ISO 10534-2 (2001). Determination of Sound Absorption Coefficient and Impedance in Impedance Tubes. International Organization for Standardization.

Kuttruff, H. (2009). Room Acoustics (5th ed.). Spon Press.

rt60-reverberation-time sound-transmission-class bem-acoustics acoustic-ray-tracing psychoacoustic-masking


ICS

ICS
Implementation Climate
Implementation Climate Scale-4

Michelle G. Ehrhart, PhD; Gregory A. Aarons, PhD; Lydia R. Farahnak, PhD

The Implementation Climate Scale (ICS) is a brief organizational assessment tool that measures the extent to which an organization's work climate, policies, and systems are aligned with and supportive of evidence-based practice (EBP) implementation. Developed by Ehrhart, Aarons, and Farahnak in 2014, the ICS measures four dimensions of organizational implementation climate: Focus (degree to which EBP is a priority in organizational goals), Support (availability of resources and protected time for EBP), Reward (incentive structures and recognition for EBP use), and Expectations (leadership communication of expectations for EBP adoption and fidelity). The ICS provides a concise snapshot of whether organizational systems and policies actively facilitate or hinder EBP uptake, and has demonstrated strong predictive validity for implementation fidelity and sustainability.

Ehrhart, M. G., Aarons, G. A., & Farahnak, L. R. (2014). Assessing the organizational context for EBP implementation: The Development and Validity Testing of the Implementation Climate Scale (ICS). Implementation Science, 9, 157.

evidence-based-practice-attitude implementation-leadership-scale organisational-readiness-change perceived-organizational-readiness implementation-climate-scale


ILS

Gregory A. Aarons, PhD; Michelle G. Ehrhart, PhD; Lydia R. Farahnak, PhD

The Implementation Leadership Scale (ILS) is a 12-item self-report measure that assesses unit-level leadership behaviors critical to successful implementation of evidence-based practices and innovations. Developed by Aarons, Ehrhart, and Farahnak in 2014, the ILS measures four dimensions of implementation leadership: proactive leadership, knowledgeable leadership, supportive leadership, and perseverant leadership. This brief, validated instrument is designed to capture frontline leaders' (managers, supervisors, unit heads) implementation-specific behaviors as perceived by clinical staff, and is widely used in healthcare implementation research to evaluate leadership effectiveness and predict implementation success.

Aarons, G. A., Ehrhart, M. G., Farahnak, L. R., & Sklar, M. (2014). Aligning leadership across systems and organizations to develop integrated care. Journal of Behavioral Health Services & Research, 41(2), 159–178.

Aarons, G. A., Ehrhart, M. G., & Farahnak, L. R. (2014). The Implementation Leadership Scale (ILS): Development of a brief measure of unit level implementation leadership. Implementation Science, 9, 106.

evidence-based-practice-attitude implementation-climate-scale organisational-readiness-change knowledge-to-action-scale perceived-organizational-readiness


Implementation Outcome Taxonomy

Implementation Outcomes: A Taxonomy of Eight Measurable Dimensions for Assessing Implementation Fidelity and Success

implementation outcomes
Proctor framework
implementation success measures

Proctor, E. K., Silmere, H., Raghavan, R., et al.

The Implementation Outcome Taxonomy is a framework defining eight measurable dimensions for assessing implementation success: Acceptability, Adoption, Appropriateness, Feasibility, Fidelity, Implementation Cost, Penetration, and Sustainability. Developed by Proctor et al. (2011), it provides a standardized vocabulary and measurement approach to distinguish implementation process outcomes (how well was the intervention delivered?) from clinical outcomes (did patients get better?). This taxonomy is foundational to implementation science because it acknowledges that an evidence-based intervention can be effective (clinical outcome) but poorly implemented (implementation outcome), or feasible to deliver but not adopted by organizations.

Proctor, E. K., Silmere, H., Raghavan, R., Hovmand, P., Aarons, G. A., Bunger, A., ... & Rojas, D. (2011). Outcomes for implementation research: Conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health and Mental Health Services Research, 38(2), 65-76.

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

Raghavan, R., Bright, C. L., & Shadoin, A. L. (2008). Toward a policy ecology of implementation of evidence-based practices in public mental health settings. Administration and Policy in Mental Health and Mental Health Services Research, 35(3), 142-154.

re-aim-framework cfir-framework fidelity-assessment knowledge-translation normalization-process-theory


Implicit Association Test

Anthony Greenwald, Debbie McGhee, and Jordan Schwartz

The Implicit Association Test (IAT) is a computerized measure designed to detect automatic associations between concepts in memory, such as implicit attitudes toward social groups or implicit self-concepts. Introduced by Greenwald, McGhee, and Schwartz in 1998, it infers the strength and valence of associations from the ease and speed with which people categorize stimuli when pairing concepts, revealing unconscious biases and attitudes that may not appear in explicit self-report measures.

Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74(6), 1464-1480.

Nosek, B. A., Greenwald, A. G., & Banaji, M. R. (2007). The Implicit Association Test at age 7: A methodological and conceptual review. In J. A. Bargh (Ed.), Social psychology and the unconscious: The automaticity of higher mental processes (pp. 265-292). Psychology Press.

Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85(2), 197-216.

signal-detection-theory response-time-analysis stroop-task affective-priming


Implicit Sentiment Analysis

Implicit Sentiment Analysis (Context-Dependent Opinion Detection)

Örtük Duygu Analizi (Implicit Sentiment)

2016 (aspect-level formulation); LLM-based reasoning formulation c. 2023

Rooted in aspect-level and deep-memory sentiment research; Tang et al. (2016) and Zhao et al. (2023) are key references

Implicit sentiment analysis detects indirect, context-dependent sentiment in text where no explicit opinion word is present — such as irony, metaphor, or understated criticism. Unlike standard sentiment analysis, which relies on surface-level polarity signals, this method interprets meaning from surrounding context, pragmatic cues, and world knowledge. It is typically addressed using large language models or fine-tuned transformers, drawing on work by Tang et al. (2016) on deep-memory aspect-level classification and Zhao et al. (2023) on LLM-based sentiment reasoning.

Text corpus — minimum 50 documents recommended

Large language model or fine-tuned transformer with context-sensitive decoding

Zhao, W. et al. (2023). Is ChatGPT a Good Sentiment Reasoner? A Preliminary Study. arXiv preprint.

Tang, D. et al. (2016). Aspect Level Sentiment Classification with Deep Memory Network. Proceedings of EMNLP 2016.

sentiment-analysis aspect-based-sentiment-analysis irony-detection negation-detection text-classification


Importance Sampling

Importance Sampling (Variance Reduction Monte Carlo)

IS
weighted Monte Carlo
Önem Örneklemesi

Herman Kahn & Theodore Harris (RAND Corporation, 1951)

Importance sampling is a Monte Carlo variance-reduction technique that shifts the sampling distribution toward the region of interest — typically a rare or extreme event — so that informative samples are drawn far more often than under the original distribution. Developed at the RAND Corporation by Herman Kahn and Theodore Harris around 1951, it makes tail-probability estimation (such as Value-at-Risk or system-failure probability) tractable where standard Monte Carlo would require an astronomically large number of runs.

Rare / tail events (probabilities < 0.01)

Output: Unbiased probability estimate with reduced variance

Rubinstein, R.Y. & Kroese, D.P. (2016). Simulation and the Monte Carlo Method (3rd ed.). Wiley.

Glasserman, P. (2003). Monte Carlo Methods in Financial Engineering. Springer.

monte-carlo-simulation stratified-sampling latin-hypercube-sampling value-at-risk extreme-value-theory bootstrap-resampling


Imprecise Probability

Imprecise Probability (Lower-Upper Probabilities)

Lower-Upper Probability
Robust Bayesian Analysis
Credal Set Theory
Belirsiz Olasılık

Imprecise probability is a generalization of standard probability theory that represents epistemic uncertainty through sets of probability measures, called credal sets, rather than a single precise distribution. Introduced systematically by Peter Walley in his 1991 monograph, the framework characterizes beliefs via lower and upper probabilities (or previsions), bracketing the range of plausible probability assignments when available information is insufficient to determine a unique measure.

Credal set (convex set of probability measures)

Lower and upper previsions (expectations)

Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. Chapman & Hall.


Impulse Response Function

IRF
Dynamic Multiplier
Shock Response Function
Etki Tepki Fonksiyonu

The Impulse Response Function (IRF) traces the dynamic response of each variable in a Vector Autoregression (VAR) system to a one-unit shock in one of its error terms over a user-specified forecast horizon. It is the primary tool for structural analysis following VAR estimation and is widely used in macroeconomics, monetary economics, and finance to quantify how shocks propagate through interconnected time series systems.

Output: Time-path of responses to a unit shock

Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.


IMRaD Structure

Introduction Methods Results and Discussion Structure

IMRaD
IMRAD
scientific manuscript structure

International scientific publishing community (adopted widely by 1970s)

IMRaD is the standard organizational framework for scientific manuscripts in biomedical and natural sciences research. It separates reporting into four sequential sections—Introduction (why the research was conducted), Methods (how it was done), Results (what was found), and Discussion (what the findings mean)—enabling readers to understand, evaluate, and reproduce the work. Adopted as best practice by the International Committee of Medical Journal Editors (ICMJE) since the 1970s, IMRaD structure is now mandated or strongly recommended by most peer-reviewed journals.

International Committee of Medical Journal Editors (2023). Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals.

Sollaci, L. B., & Pereira, M. G. (2004). The Introduction, Methods, Results, and Discussion (IMRAD) structure: a fifty-year survey. Journal of the Medical Library Association, 92(3), 364–371.

abstract-writing scientific-writing-clarity statistical-reporting-standards equator-network apa-style-guide


In-Depth Interview Method

Semi-Structured and Unstructured In-Depth Interviewing

IDI
qualitative interview
one-on-one interview
in-depth interviewing

In-depth interviews are a qualitative research method in which a trained interviewer conducts one-on-one conversations with individual participants using open-ended questions to explore their experiences, perspectives, and understandings of a phenomenon. Developed in the 1950s by Rogers and Hyman, the method varies along a spectrum from structured (standardized question sets) to semi-structured (guided topic areas with flexibility) to unstructured (emergent, conversational). In-depth interviews are widely used in sociology, psychology, health sciences, anthropology, and organizational research to capture rich, detailed narratives and personal meaning.

Kvale, S. (1996). InterViews: An Introduction to Qualitative Research Interviewing. SAGE Publications.

Patton, M. Q. (2002). Qualitative Research and Evaluation Methods (3rd ed.). SAGE Publications.

Bernard, H. R. (2006). Research Methods in Anthropology: Qualitative and Quantitative Approaches (4th ed.). Rowman & Littlefield Publishers.

Rubin, H. J., & Rubin, I. S. (2005). Qualitative Interviewing: The Art of Hearing Data (2nd ed.). SAGE Publications.

focus-group-methodology participant-observation reflexivity-in-research member-checking qualitative-synthesis-methods


In-Depth Interview

IDI
semi-structured interview
unstructured interview
qualitative interview

Mid-20th century (formalised in qualitative social research from the 1950s onward)

Rooted in sociological interviewing traditions; systematised by researchers including Steinar Kvale and Herbert J. Rubin

The in-depth interview is a one-to-one qualitative data-collection method in which a researcher engages a participant in an extended, open-ended conversation to elicit rich, detailed accounts of experiences, perceptions, beliefs, or meanings. Unlike structured surveys, the interview guide serves as a flexible road map rather than a fixed script, allowing the researcher to probe unexpected directions as they emerge. The approach is foundational to qualitative inquiry and is used directly as a primary method or as the data-collection arm of phenomenology, grounded theory, narrative analysis, and other frameworks.

Spoken narrative data collected via one-to-one conversation (audio-recorded and transcribed)

10–30 participants (until theoretical saturation)

Kvale, S. (1996). InterViews: An Introduction to Qualitative Research Interviewing. Sage.

Rubin, H. J., & Rubin, I. S. (2005). Qualitative Interviewing: The Art of Hearing Data (2nd ed.). Sage.

phenomenology grounded-theory ethnography narrative-analysis thematic-analysis focus-group


In Vitro-In Vivo Correlation

IVIVC is a mathematical relationship between in vitro and in vivo properties of a drug, developed to predict oral bioavailability from dissolution data. Introduced by Amidon and colleagues in the 1995 Biopharmaceutics Classification System, it bridges laboratory measurements and clinical outcomes to streamline drug development.

Amidon, G. L., Lennernäs, H., Shah, V. P., & Crison, J. R. (1995). A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharmaceutical Research, 12(3), 413-420.

Shah, V. P., Amidon, G. L., & Levy, G. (1996). Level A, B, and C strategic approaches for biopharmaceutical classification-based dissolution specifications and in vitro-in vivo correlations. Pharmaceutical Research, 13(12), 1799-1801.


In Vivo Coding

verbatim coding
literal coding
first-cycle in vivo coding
indigenous coding

1967 (grounded theory origins); widely codified as a distinct method from the 1990s onward

Barney G. Glaser and Anselm L. Strauss (grounded theory tradition); systematised and named by Johnny Saldaña

In vivo coding is a qualitative first-cycle coding strategy in which the researcher uses the participants' own words or short phrases verbatim as code labels, rather than imposing researcher-generated or theoretical language. The technique preserves the voice, meaning, and conceptual priorities of participants, making it especially valuable in grounded theory, phenomenology, and any study where honouring the emic (insider) perspective is central to analytic integrity.

Interview transcripts, focus group transcripts, field notes, documents, diary entries

Varies; typically 10–30 participants or equivalent text units

Saldaña, J. (2021). The Coding Manual for Qualitative Researchers (4th ed.). Sage.

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

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


Inception Network

Deep Convolutional Neural Network (Google)

The Inception Network, introduced by Szegedy et al. at Google in 2015 and submitted to CVPR under the name GoogLeNet, is a 22-layer deep convolutional neural network designed for large-scale image recognition. Its defining contribution is the Inception module, which applies convolutions of multiple kernel sizes in parallel and concatenates their outputs, enabling the network to capture spatial features at different scales simultaneously without a proportional increase in computational cost.

Deep CNN with parallel multi-scale convolutions

Szegedy, C., et al. (2015). Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9.


Incremental Dynamic Analysis

Incremental Dynamic Analysis for Seismic Risk Assessment

IDA
Intensity-based analysis
Fragility curve development

Dimitrios Vamvatsikos and C. Allin Cornell

Incremental dynamic analysis (IDA) is a method that runs time-history analyses on a structure with a single ground motion record, progressively increasing the intensity until the structure reaches a specified performance level or collapses. Introduced by Vamvatsikos and Cornell in 2002, this approach efficiently generates fragility curves relating earthquake intensity to structural damage and collapse probability.

Intensity-based dynamic analysis for fragility assessment

Vamvatsikos, D., & Cornell, C. A. (2002). Incremental dynamic analysis of seismic performance of structures. Earthquake Engineering & Structural Dynamics, 31(3), 491-514.

Cornell, C. A., Jalayer, F., Hamburger, R. O., & Foutch, D. A. (2002). Probabilistic basis for 2000 SAC federal emergency management agency steel moment frame guidelines. Journal of Structural Engineering, 128(4), 526-533.

Luco, N., & Cornell, C. A. (2007). Structure-specific scalar intensity measures for near-source and broadband ground motions. Journal of Engineering Mechanics, 133(4), 445-456.


Independent Component Analysis

ICA
blind source separation
BSS
FastICA
independent components

Independent Component Analysis (ICA) is a computational method for separating a multivariate signal into additive, statistically independent subcomponents. Formalized by Pierre Comon in 1994, ICA became the foundational framework for blind source separation and is widely applied in neuroimaging (fMRI, EEG), speech processing, and biomedical signal analysis.

Blind source separation / latent-structure decomposition

Unsupervised signal separation and feature extraction

Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36(3), 287–314.

Hyvärinen, A., Karhunen, J., & Oja, E. (2001). Independent Component Analysis. Wiley.

principal-component-analysis factor-analysis non-negative-matrix-factorization autoencoders singular-value-decomposition


Independent samples t-test

two-sample t-test
unpaired t-test
Student t-test
independent groups t-test

The independent samples t-test is a parametric hypothesis test that determines whether the means of two independent, unrelated groups differ significantly on a continuous outcome variable. Derived from Gosset's 1908 t-distribution, it is one of the most widely used inferential tests in social, behavioral, biomedical, and experimental sciences.

Student (W. S. Gosset) (1908). The probable error of a mean. Biometrika, 6(1), 1–25.

Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics (5th ed.). SAGE Publications.


Independent t-test

student t-test
two-sample t-test
unpaired t-test
bağımsız örneklem t-testi

The independent samples t-test is a parametric hypothesis test that compares the means of two independent groups to decide whether they differ significantly. It builds on the t-distribution introduced by Student (W. S. Gosset) in 1908 and assumes the measured values are continuous, approximately normally distributed, and have equal variances.

Student (1908). The probable error of a mean. Biometrika, 6(1), 1–25.

Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE.

paired-t-test welch-t-test mann-whitney-u one-way-anova


Independent Vector Analysis

Independent Vector Analysis for Multivariate Blind Source Separation

IVA
multivariate ICA
vector blind source separation

Tae-Won Lee, Mark Lewicki, Terrence Sejnowski

Independent Vector Analysis (IVA) is a multivariate extension of Independent Component Analysis that jointly separates multiple datasets while maintaining dependencies within each dataset. Developed by Lee, Lewicki, and Sejnowski in the 2000s, IVA is used for blind source separation in multi-channel audio, brain imaging, and signal processing. It exploits both the independence between sources and correlations within frequency bands or time-frequency structures.

Multivariate matrix decomposition algorithm

Lee, T. W., Lewicki, M. S., & Sejnowski, T. J. (2007). Independent Component Analysis for Source Localization in Biomedical Signals. In Proc. IEEE Int. Conf. Acoust. Speech Signal Process., pp. 97-100.

Kim, T., Attias, H. T., Lee, S. Y., & Lee, T. W. (2006). Blind source separation exploiting higher-order frequency dependencies. IEEE Transactions on Audio, Speech, and Language Processing, 15(1), 70-79.

Comon, P., Jutten, C., & Herault, J. (2010). Blind Separation of Sources, Part II: Problems Statement. IEEE Transactions on Signal Processing, 59(11), 4711-4721.


Indexing Strategy

Indexing strategy is the practice of systematically designing database indexes to accelerate query performance. Developed following Bayer and McCreight's foundational B-tree work in 1972, effective indexing requires analyzing query patterns, choosing appropriate index structures, and maintaining index health as data evolves.

Bayer, R., & McCreight, E. (1972). Organization and maintenance of large ordered indices. Acta Informatica, 1(3), 173-189.

Seltzer, M., & Bostic, K. (1994). An implementation of a log-structured file system for UNIX. Winter USENIX Conference, 307-326.

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

query-optimization b-tree-indexes hash-indexes bitmap-indexes query-execution-plans


Indicator Value

IndVal
indicator species
fidelity
specificity
association analysis

Indicator Value (IndVal) analysis, developed by Dufrene and Legendre (1997), identifies species that reliably indicate the presence of particular environmental conditions, habitat types, or community groups. The method quantifies the association between species and habitat, producing an indicator value that combines specificity (exclusive preference for certain habitats) and fidelity (consistent presence when the habitat occurs). IndVal is widely used in conservation to identify species of management concern, in habitat typing to discover indicator species, and in restoration ecology to assess whether recovered communities match reference conditions.

Dufrene, M., & Legendre, P. (1997). Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecological Monographs, 67(3), 345-366.

Bakus, G. J. (2007). Quantitative Ecology and the Brown Algae. Oxford University Press.

Caceres, M. D., & Legendre, P. (2010). Stability analysis of species-by-trait matrices. Methods in Ecology and Evolution, 1(3), 217-226.

species-accumulation functional-diversity food-web-topology beta-diversity-partitioning


Individual Patient Data Meta-Analysis

Individual Patient Data Meta-Analysis (IPD-MA)

IPD Meta-Analysis
Participant-Level Data Synthesis
One-Stage Meta-Analysis

Cochrane Collaboration, Pioneered by Stewart & Clarke

Individual patient data meta-analysis (IPD-MA) is a systematic synthesis method where researchers obtain and analyze raw data at the patient level from multiple randomized controlled trials, rather than relying on published summary statistics (aggregate data). Pioneered by the Cochrane Collaboration and formalized by Stewart, Clarke, and Riley, IPD-MA is considered the gold standard for evidence synthesis because it enables consistent outcome definition across trials, robust subgroup analysis, and detection of treatment-covariate interactions. Though time-intensive and resource-demanding, IPD-MA provides the most reliable estimates of intervention effects and is preferred for critical clinical decisions, particularly for identifying which patients benefit most from treatment.

Stewart, L. A., Clarke, M. J., & Cochrane IPD Meta-analysis Methods Group. (2015). Practical methodology of meta-analyses (including IPD) of randomised trials reporting time to event data. Cochrane Database of Systematic Reviews, 2015(10), MR000027.

Riley, R. D., Lambert, P. C., & Abo-Zaid, G. (2010). Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ, 340, c221.

Higgins, P. T., & Green, S. (Eds.). (2011). Cochrane Handbook for Systematic Reviews of Interventions (Version 5.1.0). The Cochrane Collaboration.

pairwise-meta-analysis network-meta-analysis systematic-review subgroup-analysis evidence-synthesis-framework


Inductively Coupled Plasma Spectrometry

ICP-OES
ICP-AES
ICP-MS
plasma emission spectroscopy

Inductively coupled plasma spectrometry is a powerful multi-element analytical technique that ionizes a sample in a high-temperature plasma and measures the emitted light (ICP-OES) or ion masses (ICP-MS) to determine elemental concentrations. Developed in the 1960s by Stanley Greenfield, ICP techniques have become the standard for trace element analysis across environmental, geological, biological, and industrial fields. The method combines exceptional sensitivity, wide dynamic range, and the ability to analyze dozens of elements simultaneously.

Greenfield, S., Jones, I. L., & Berry, C. T. (1968). High-pressure plasma jet source for use in atomic spectroscopy. Analyst, 93(1108), 694–697.

Montaser, A. (Ed.). (2008). Inductively Coupled Plasma Mass Spectrometry (2nd ed.). Wiley-VCH.

Houk, R. S. (1986). Mass spectrometry of inductively coupled plasma. Analytical Chemistry, 58(1), 97A–105A.

atomic-absorption-spectroscopy uv-vis-spectrophotometry potentiometric-titration ion-chromatography flow-injection-analysis


Industrial applications full factorial design

Full Factorial Design for Industrial Applications

industrial FFD
full factorial experiment
complete factorial design
2^k factorial design

1926 (foundational); industrially systematized by Box, Hunter & Hunter ~1950s–1978

Full factorial design (FFD) applied in industrial settings is a structured experimental methodology in which every combination of factor levels is tested, enabling engineers to quantify main effects and all interaction effects among process or product variables. Widely used in manufacturing, chemical processing, materials science, and quality engineering, it provides a complete picture of how input factors jointly influence a response variable such as yield, strength, or defect rate.

Experimental design / factorial experiment

Continuous or categorical process/product measurements (yield, strength, defect rate, etc.)

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

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

fractional-factorial-design full-factorial-design central-composite-design taguchi-method response-surface-methodology statistical-process-control


Response Surface Methodology for Industrial Process Optimization

Industrial RSM
RSM for manufacturing
process optimization RSM
industrial response surface analysis

1951 (origin); widespread industrial adoption from 1980s onward

George E. P. Box & K. B. Wilson; industrialized by Douglas Montgomery and colleagues

Industrial Applications Response Surface Methodology (RSM) applies the classical Box-Wilson response surface framework to manufacturing and process engineering problems. It builds an empirical polynomial model linking controllable process inputs — such as temperature, pressure, feed rate, or catalyst concentration — to one or more quality responses, then mathematically locates the input settings that optimize those responses. It is the de-facto standard statistical tool for process characterization and optimization in chemical, mechanical, food, materials, and pharmaceutical manufacturing.

Continuous numerical process measurements (quality characteristics, yield, strength, efficiency)

Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley.

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


Inertia (Within-Cluster Sum of Squares)

Inertia: Sum of Squared Distances to Cluster Centroids

WCSS
within-cluster sum of squares
cluster cohesion

Inertia, also called Within-Cluster Sum of Squares (WCSS), is a measure of cluster cohesion that quantifies how tightly points are grouped around their cluster centroids. Lower values indicate more compact, cohesive clusters. Inertia is the primary objective function for k-means clustering and has been a fundamental metric since the method's introduction.

Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137.

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (Vol. 1, pp. 281-297).

elbow-method silhouette-score davies-bouldin-index calinski-harabasz-index dunn-index


ITQOL

generic infant and toddler health-related quality of life

The ITQOL is a generic parent-report instrument developed by Landgraf et al. in 1997 to measure health-related quality of life in infants and toddlers aged 2 months to 5 years. Addressing the developmental uniqueness of the very young, the ITQOL captures health-related functioning across domains relevant to early childhood: physical growth and development, respiratory functioning, sleep patterns, emotional security, parental time and emotional burden, and family social functioning. A 97-item full form and 47-item abbreviated form are available, enabling comprehensive or brief assessment depending on clinical context.

Parent report of infant/toddler health and functioning

Landgraf, J. M., Abetz, L., & Ware, J. E. (2002). The infant and toddler quality of life questionnaire: User's manual and interpretation guide. Health Act.

Landgraf, J. M., Rich, M., & Rapoff, M. A. (1997). Development of the Infant and Toddler Quality of Life Questionnaire (ITQOL): Measurement model validation and reliability testing. Pediatrics, 100(4), 624-632.

paqlq pedsql-diabetes child-health-questionnaire qolce


IYCF Practices

Infant and Young Child Feeding Practices Assessment

The IYCF assessment is a set of core indicators developed by WHO and UNICEF to measure the prevalence of key feeding practices in children aged 0–23 months. The indicators track six essential markers: exclusive breastfeeding in infants under 6 months, continued breastfeeding in the second year, timely introduction of complementary foods at 6–8 months, and adequate dietary diversity and meal frequency by 6–23 months. These indicators are foundational for monitoring nutrition outcomes and evaluating maternal and child health programs worldwide.

Caregiver interview; 24-hour and historical recall

World Health Organization (2021). Infant and young child feeding. WHO.int. Retrieved from https://www.who.int/teams/nutrition-and-food-safety/food-safety/infants-and-young-children

UNICEF (2019). Infant and young child feeding indicator: a methodological framework. UNICEF.


IBDQ

Inflammatory Bowel Disease Questionnaire

IBDQ
IBD Questionnaire
Inflammatory Bowel Disease QoL

Gastroenterology and Gastrointestinal Disease

The IBDQ is a disease-specific quality of life measure for inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis. Developed by Elena Irvine and colleagues in 1994, this 32-item questionnaire measures how IBD affects bowel function, systemic symptoms, emotional well-being, and social functioning. It is the most widely used quality-of-life instrument in IBD research and clinical practice.

Self-report quality of life questionnaire

Guyonnet, S., Dupont, C., Mouterde, O., Chouraqui, J. P., Darmaun, D., & Goulet, O. (2001). Placebo-controlled trial of saccharomyces boulardii in gastroenteritis. Pediatrics, 107(2), E27.

Irvine, E. J., Feagan, B., Rochon, J., Archambault, A., Fedorak, R. N., Groll, A., ... & Marshall, J. K. (1994). Quality of life: A valid and reliable measure of therapeutic efficacy in the short bowel syndrome. Gastroenterology, 104(5), 1582-1588.

Jowett, S. L., Seal, C. J., Pearce, M. S., Phillips, E., Gregory, W., Rampton, D. S., & Welfare, M. R. (2004). Influence of dietary factors on the clinical course of ulcerative colitis. Gut, 53(10), 1479-1484.

eortc-qlq-c30 chronic-heart-failure-questionnaire kidney-disease-quality-of-life rheumatoid-arthritis-qol


Influence Diagnostics

Regression Influence Diagnostics (Cook's Distance, DFFITS, Leverage)

Etki Tanılamaları (Cook's D, DFFITS, Leverage)

R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage)

Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients.

Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18.

Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley.

ols-regression robust-regression quantile-regression ridge-regression mad-estimation


Information Extraction

IE
structured information extraction
Bilgi Çıkarma (Information Extraction)

Information extraction (IE) is a natural-language-processing task that converts unstructured text into structured information — such as events, relations, and attributes — so that facts buried in free-form documents become machine-readable records. The task was consolidated in early surveys by Cowie and Lehnert (1996) and later by Grishman (2012).

Output: Structured records (events, relations, attributes) extracted from text

Cowie, J. & Lehnert, W. (1996). Information Extraction. Communications of the ACM.

Grishman, R. (2012). Information Extraction. In Handbook of Natural Language Processing.

named-entity-recognition relation-extraction text-summarization semantic-similarity


Informed Consent in Research

Informed Consent Process and Documentation for Human Research Subjects

Research Consent
Informed Consent Process

Multiple (Nuremberg Code 1947 first principle; formalized in Belmont Report 1979, Declaration of Helsinki 1964; US Common Rule 45 CFR 46)

Informed consent is the cornerstone of ethical human subjects research, requiring researchers to disclose material information about a study and obtain voluntary agreement from subjects before participation. Established as the first principle of the Nuremberg Code (1947) and formalized in subsequent ethical frameworks (Declaration of Helsinki 1964, Belmont Report 1979), informed consent protects subject autonomy, enables risk-benefit assessment, and creates accountability. Effective informed consent requires far more than obtaining a signature—it demands clear communication, genuine comprehension, and authentic voluntariness.

U.S. Department of Health and Human Services. Code of Federal Regulations Title 45, Part 46: Protection of Human Subjects. Federal Register.

Beauchamp, T.L. & Childress, J.F. (1979). Principles of Biomedical Ethics. Oxford University Press.

International Council for Harmonisation (ICH). (1996). Guideline for Good Clinical Practice E6(R2). International standard for clinical trial conduct.

belmont-report nuremberg-code declaration-of-helsinki institutional-review-board


Informer

Informer — Uzun Dizi Transformer Tahmini

Informer is a Transformer-based model introduced by Zhou et al. in 2021 for long-sequence time-series forecasting, using a ProbSparse self-attention mechanism that lowers the computational complexity of the standard Transformer to O(L log L). It is built for problems that demand predictions across thousands of future steps.

Zhou, H. et al. (2021). Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting. AAAI.

Wu, H., Xu, J., Wang, J. & Long, M. (2021). Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting. NeurIPS 34.

deepar nhits patchtst arima random-forest


Infrared Spectroscopy Identification

Infrared Spectroscopy for Functional Group Identification

IR spectroscopy
FTIR
infrared spectroscopy

Infrared (IR) spectroscopy measures the absorption of infrared radiation by chemical bonds, creating a spectrum unique to each compound. Discovered by William Herschel in 1800 and developed into a practical analytical tool in the mid-20th century, IR spectroscopy is indispensable for rapidly identifying functional groups and confirming compound structure in organic and inorganic chemistry.

Pavia, D. L., Lampman, G. M., Kriz, G. S., & Engel, R. G. (2014). A Small-Scale Approach to Organic Laboratory Techniques (4th ed.). Cengage Learning.

Smith, B. C. (2018). Fundamentals of Fourier Transform Infrared Spectroscopy (3rd ed.). CRC Press.


Adoption Scale

Adoption Scale
Innovation Adoption
Adoption Readiness

Everett M. Rogers, PhD; Tornatzky & Klein framework; multiple measurement approaches

Innovation Adoption refers to the extent to which an innovation, evidence-based practice, or new technology is actually used by the target population or in the target setting. Adoption is typically measured as the percentage of eligible users/staff who have adopted the innovation by a specific time point, or the trajectory of adoption over time (adoption curve). Grounded in Rogers' Diffusion of Innovations theory, adoption is a key implementation outcome distinct from readiness (willingness to adopt), fidelity (quality of delivery), or effectiveness (impact on outcomes). An innovation can be widely adopted but delivered with low fidelity, or adopted by only a subset of users despite being efficacious. Adoption curves reflect organizational readiness, innovation-context fit, and implementation strategy effectiveness. Adoption is often the first implementation outcome to emerge, typically preceding fidelity and effectiveness improvements.

Self-report questionnaire or behavioral tracking

Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). New York: Free Press.

Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29(1), 28–45.

evidence-based-practice-attitude stages-of-concern-questionnaire implementation-climate-scale normalisation-measure-development knowledge-to-action-scale


Innovation Ambidexterity Scale

Organizational Ambidexterity (Exploration-Exploitation) Scale

Ambidexterity Scale
Exploration-Exploitation Scale

Innovation Ambidexterity—the organizational capacity to simultaneously engage in exploration (pursuing radical, novel innovations) and exploitation (improving and extending existing products and processes)—is fundamental to sustained competitive advantage. March (1991) formalized this trade-off in Organization Science, arguing that organizations must balance the two to survive and thrive. Exploration alone leads to variety but insufficient returns; exploitation alone leads to competence traps and vulnerability to disruption. This scale, operationalized by He and Wong (2004) and extended by Jansen et al. (2006), measures organizational capability in both domains and the degree to which firms balance competing innovation imperatives.

March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.

He, Z. L., & Wong, P. K. (2004). Exploration vs. exploitation: An empirical test of the ambidexterity hypothesis. Organization Science, 15(4), 481–494.

Jansen, J. J. P., Van Den Bosch, F. A. J., & Volberda, H. W. (2006). Exploratory innovation, exploitative innovation, and performance: Effects of organizational antecedents and environmental moderators. Management Science, 52(11), 1661–1674.

entrepreneurial-orientation-scale dynamic-capabilities-scale absorptive-capacity-scale strategic-orientation-scale market-sensing-capability-scale


Innovation Climate Scale

The Innovation Climate Scale (ICS) is a 50-item instrument measuring organizational climate for creativity and innovation across ten dimensions. Developed by Göran Ekvall in 1996, the ICS identifies environmental factors that enable or inhibit organizational innovation, making it valuable for assessing innovation potential.

Ekvall, G. (1996). Organizational climate for creativity and innovation. European Journal of Work and Organizational Psychology, 5(1), 105-123.

Hunter, S. T., Bedell, K. E., & Mumford, M. D. (2007). Climate for creativity: A quantitative review. Creativity Research Journal, 19(1), 69-90.

organizational-learning-scale organizational-culture-assessment knowledge-sharing-scale employee-engagement-survey


Input-Output Structural Decomposition Analysis (SDA)

Wassily Leontief, adapted by Rose and others

Input-Output Structural Decomposition Analysis (IO-SDA) is an economic-environmental accounting method rooted in Wassily Leontief's input-output framework. It decomposes changes in economic activity and associated environmental impacts (emissions, resource use) over time into components reflecting technological change, demand shifts, and structural economic reorganization. Rose, Chen, and others formalized SDA in the 1980s–1990s for sustainability analysis.

Leontief, W. W. (1951). The Structure of the American Economy. Oxford University Press.

Rose, A., & Chen, C. Y. (1991). Sources of change in energy use in the U.S. economy, 1972–1982: A structural decomposition analysis. Resources and Energy, 13(1), 1-21.

Dietzenbacher, E., & Los, B. (2000). Structural decomposition techniques: Sense and sensitivity. Economic Systems Research, 12(1), 41-58.


INS Error Model

INS error analysis
error state kalman filter
ESKF

The INS Error Model is a mathematical framework that characterizes how errors in inertial sensor measurements propagate through a navigation system's estimates of position, velocity, and attitude. Developed during the 1960s and refined through decades of navigation research, the error model enables design of optimal estimation filters (e.g., Kalman filters) that fuse inertial measurements with external references (GNSS, LiDAR, cameras) to bound and correct accumulated errors. The error model is fundamental to understanding and improving inertial navigation performance.

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

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

Farrell, J. A., Tan, H. S., & Yang, Y. (2008). Control of autonomous vehicles with observer-based dynamic feedback. IEEE Transactions on Automatic Control, 28(4), 457–472.


InSAR

Radar remote sensing and deformation monitoring

Interferometric Synthetic Aperture Radar (InSAR) is a radar remote sensing technique that measures millimeter-scale ground surface deformation by analyzing the phase difference between radar images acquired from slightly different orbital positions. Pioneered by Gabriel, Goldstein, and Zebker in 1989, InSAR has become essential for earthquake rupture characterization, volcanic monitoring, landslide detection, and subsidence quantification.

Radar interferometry for millimeter-precision surface deformation

Gabriel, A. K., Goldstein, R. M., & Zebker, H. A. (1989). Mapping small elevation changes over large areas: Differential radar interferometry. Journal of Geophysical Research, 94(B7), 9183-9191.

Massonnet, D., & Feigl, K. L. (1998). Radar interferometry and its application to changes in the Earth's surface. Reviews of Geophysics, 36(4), 441-500.


Insomnia Severity Index

The ISI is a 7-item self-report questionnaire designed to assess the severity of insomnia in adolescents and adults. Developed by Morin and colleagues and validated in 2001, it measures difficulty falling asleep, difficulty staying asleep, early morning awakening, and daytime functional impairment due to sleep problems. The ISI is brief (2–3 minutes), psychometrically sound, and widely adopted in sleep research, primary care, and behavioral sleep medicine clinics for screening, baseline assessment, and treatment monitoring.

Morin, C. M., Belleville, G., Bélanger, L., & Ivers, H. (2011). The Insomnia Severity Index: Psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep, 34(5), 601–608.

Bastien, C. H., Vallières, A., & Morin, C. M. (2001). Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Medicine Reviews, 5(5), 377–383.

Morin, C. M. (2006). Insomnia: Psychological assessment and management. New York: Guilford Press.


Instance Segmentation

Instance Segmentation (per-object pixel-level detection and masking)

instance-level segmentation
object instance segmentation
mask prediction
panoptic instance segmentation

He, K., Gkioxari, G., Dollar, P., Girshick, R.

Instance segmentation is a computer vision task that simultaneously detects every distinct object in an image and produces a precise pixel-level mask for each individual object instance. Unlike semantic segmentation, which labels every pixel with a class, instance segmentation distinguishes between separate objects of the same class, enabling fine-grained spatial understanding.

Pixel-level detection and mask prediction

He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2961–2969.

semantic-segmentation object-detection convolutional-neural-network image-classification fine-tuned-instance-segmentation multimodal-instance-segmentation


Institutional Ethnography

IE
sociology for people
institutional ethnographic inquiry
Smith's institutional ethnography

1970s–1987 (developed through the 1970s–80s; consolidated in Smith 1987, 2005)

Institutional Ethnography (IE) is a qualitative research method developed by Canadian sociologist Dorothy E. Smith that investigates how people's everyday lives are shaped and coordinated by institutional texts, rules, and relations of power. Starting from the lived experience of individuals in a particular standpoint, IE traces the social organization that governs their work and troubles — revealing how macro-level institutions operate through the micro-level activities of real people.

Interviews, observation, textual and documentary analysis

10–40 participants across multiple standpoints; no fixed upper limit

Smith, D. E. (2005). Institutional Ethnography: A Sociology for People. AltaMira Press.

DeVault, M. L., & McCoy, L. (2006). Institutional ethnography: Using interviews to investigate ruling relations. In D. E. Smith (Ed.), Institutional Ethnography as Practice (pp. 15–44). Rowman & Littlefield.

ethnography discourse-analysis grounded-theory action-research phenomenology narrative-analysis


Institutional Review Board

Institutional Review Board: Structure, Function, and Protocol Review

U.S. Federal Requirement (National Research Act 1974); International adoption by WMA and research institutions globally

The Institutional Review Board (IRB) is the independent ethics committee established at research institutions to review and approve human subjects research, ensuring compliance with ethical principles and federal regulations. Created as a legal requirement by the U.S. National Research Act (1974) and now adopted globally, the IRB serves as the primary mechanism for protecting research subjects while enabling legitimate research to proceed. No human subjects research can begin without IRB approval.

U.S. Code of Federal Regulations, Title 45, Part 46: Protection of Human Subjects. Office of the Federal Register.

U.S. Department of Health and Human Services, Office for Human Research Protections. (2016). Federalwide Assurance (FWA) for Protection of Human Subjects. Policy on IRB composition and review procedures.

National Institutes of Health. (2020). Institutional Review Boards Frequently Asked Questions. Office of Science Policy.

belmont-report informed-consent-research declaration-of-helsinki research-integrity-principles


Institutional Trust Scale

Trust in Political and Social Institutions Scale

David Easton, Marc Hetherington, Pippa Norris

The Institutional Trust Scale measures an individual's confidence and trust in formal political and social institutions including parliament, courts, police, media, and civil service. Distinct from generalized interpersonal trust, institutional trust reflects belief in the legitimacy, fairness, and effectiveness of formal organizations that structure governance and public life. Developed in political science by scholars including David Easton and Marc Hetherington, it is a key indicator of democratic health and governance legitimacy.

Hetherington, M. J. (2005). Why trust matters: Declining political trust and the demise of American liberalism. Princeton University Press.

Norris, P. (Ed.). (2011). Public sentinel: News media and governance reform. World Bank Publications.

Easton, D. (1975). A re-assessment of the concept of political support. British Journal of Political Science, 5(4), 435-457.

generalized-trust-scale political-efficacy-scale democratic-values-scale social-cohesion-scale civic-engagement-scale


Instrument Recognition

Musical Instrument Recognition Algorithm

instrument classification
timbre identification
instrument detection

Instrument recognition is the task of automatically identifying which musical instruments are present in an audio recording. Formalized by Eronen et al. (2005), it addresses timbre—the tonal quality distinguishing one instrument from another. Instrument recognition is essential for music analysis, transcription, automatic indexing, and music education. It remains challenging in polyphonic contexts but has achieved good accuracy in solo and sparse accompaniment scenarios.

Eronen, A., Peltonen, V., Tuomi, J., Klapuri, A., Fagerlund, S., Sorsa, T., & Lorho, G. (2005). Audio-based context recognition. IEEE Transactions on Audio, Speech, and Language Processing, 14(1), 321-329.

Benetos, E., Holzapfel, A., Kotropoulos, C., & Pikrakis, A. (2013). Polyphonic instrument recognition using source separation and feature integration. In Proceedings of the International Society for Music Information Retrieval Conference.

Cai, R., Lu, L., Hanjalic, A., Zhang, H. J., & Cai, L. H. (2007). A new tool for music tagging and contextual music search. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval.

music-genre-classification timbre-analysis pitch-detection-algorithm music-segmentation automatic-music-transcription


Instrumental Case Study

instrumental case research
theory-building case study
illustrative case study
issue-driven case study

Instrumental case study is a qualitative research design, formalised by Robert E. Stake (1995), in which a specific case is studied primarily to gain insight into an external issue or theoretical question — not because the case itself is intrinsically important. The case serves as an instrument for understanding something broader: a policy problem, a theoretical proposition, or a generalised phenomenon. One or several cases are selected because they are expected to illuminate the issue particularly well, and the researcher moves fluidly between the case and the issue throughout the study.

Interviews, observations, documents, archival records

Stake, R. E. (1995). The Art of Case Study Research. Sage Publications.

Stake, R. E. (2006). Multiple Case Study Analysis. Guilford Press.

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


Instrumental Neutron Activation Analysis (INAA)

Instrumental neutron activation analysis (INAA) measures trace element concentrations in archaeological artifacts by bombarding samples with neutrons and analyzing the resulting gamma-ray emissions. Developed as a systematic archaeological method by Michael Glascock and colleagues, INAA provides chemical fingerprints of ceramics, obsidian, and other materials that reveal sourcing and provenance. The method is non-destructive, highly sensitive, and capable of detecting 30+ elements simultaneously.

Glascock, M. D. (1992). Characterization of archaeological ceramics at MURR. Journal of Radioanalytical and Nuclear Chemistry, 168(2), 217-228.

Anders, E., & Grevesse, N. (1989). Abundances of the elements. Geochimica et Cosmochimica Acta, 53(1), 197-214.


Instrumental Variables in Education Research

Instrumental Variables Estimation Applied to Education Research

IV in education
2SLS in education
education IV
school IV estimation

Angrist & Krueger (canonical 1991 education application); grounded in IV theory by Wright (1928)

Instrumental variables (IV) estimation is a quasi-experimental strategy for isolating the causal effect of schooling or educational interventions when assignment to treatment is confounded by unobserved factors. Pioneered in education economics by Angrist and Krueger's use of quarter-of-birth as an instrument for compulsory schooling, IV finds a source of exogenous variation in exposure to education and uses only that variation to estimate outcomes such as earnings, test scores, or attainment.

Observational survey, administrative records, repeated cross-sections or panel data

Angrist, J. D., & Krueger, A. B. (1991). Does Compulsory School Attendance Affect Schooling and Earnings? Quarterly Journal of Economics, 106(4), 979-1014.

Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.

instrumental-variables difference-in-differences regression-discontinuity-design propensity-score-matching two-stage-least-squares local-average-treatment-effect


Instrumental Variables in Health Research

Instrumental Variables (IV) Method for Causal Inference

IV
two-stage least squares
TSLS
causal estimation

Angrist & Pischke (applied econometrics); rooted in econometric theory

Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.

Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.

Bound, J., Jaeger, D. A., & Baker, R. M. (1995). Problems with Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogenous Explanatory Variable is Weak. Journal of the American Statistical Association, 90(430), 443-450.

Wooldridge, J. M. (2010). Econometric Analysis of Cross Section and Panel Data (2nd ed.). Cambridge, MA: MIT Press.


Integer Programming

Integer Programming (IP / Mixed-Integer Programming)

IP
MIP
mixed-integer programming
mixed-integer linear programming
MILP
Tam Sayılı Programlama (IP / MIP)

Ralph Gomory (cutting planes, 1958); land-and-doig branch-and-bound (1960)

Integer programming (IP), also called mixed-integer programming (MIP) when only some variables are restricted to whole numbers, is a branch of mathematical optimisation in which some or all decision variables must take integer or binary values. Building on linear programming, it was formalised through Ralph Gomory's cutting-plane method (1958) and the Land-and-Doig branch-and-bound algorithm (1960), and it has since become the standard exact framework for scheduling, assignment, routing, and resource-allocation problems.

Mathematical optimisation — exact combinatorial method

Integer, binary (0-1), or mixed (some integer, some continuous)

Wolsey, L.A. (1998). Integer Programming. Wiley.

Nemhauser, G.L. & Wolsey, L.A. (1988). Integer and Combinatorial Optimization. Wiley.

linear-programming dynamic-programming constraint-programming network-flow-optimization goal-programming