Digital Object Identifier System

DOI: Persistent Identifiers for Scholarly Research Objects

DOI
Digital Object Identifier
persistent identifier

1998 (concept); 2001 (widespread adoption)

Norman Paskin, CrossRef and International DOI Foundation (1998)

A Digital Object Identifier (DOI) is a unique, persistent alphanumeric code that identifies a scholarly work (journal article, book chapter, dataset, preprint) and persists even if the URL changes. Introduced in 1998 by Norman Paskin and the International DOI Foundation, DOIs are now standard in academic publishing. They consist of a prefix (assigned to a publisher or organization) and a suffix (assigned to an individual work), formatted as 10.XXXX/XXXXX (e.g., 10.1371/journal.pmed.1000097). DOIs are registered with international agencies (CrossRef, DataCite, mEDRA) and resolve through the centralized resolver https://doi.org/, ensuring that a DOI will direct users to the correct article regardless of whether the publisher's website changes location.

Paskin, N. (2010). Digital Object Identifier (DOI) system. Encyclopedia of Library and Information Sciences, 3rd ed., 1586–1592.

Crossref: The scholarly link network. https://www.crossref.org

International Organization for Standardization (ISO). (2012). ISO 26324:2012 Information and documentation - Digital object identifier (DOI). ISO.

citation-analysis citation-management-tools altmetrics orcid-researcher-id


Dolado-Lütkepohl Causality

DL Causality Test
Modified Wald Causality Test
Augmented VAR Causality Test
Dolado-Lütkepohl Testi

The Dolado-Lütkepohl (DL) test, introduced by Dolado and Lütkepohl (1996), is a modified Wald procedure for testing Granger causality in vector autoregressive (VAR) systems whose variables may be integrated or cointegrated. By fitting a VAR of slightly higher order than necessary and restricting the Wald statistic to the first p lag blocks, the test recovers the standard chi-squared limiting distribution without requiring pre-testing for cointegration or transformation to error-correction form.

Modified Wald test for Granger causality in possibly integrated or cointegrated VAR systems

Dolado, J. J., & Lütkepohl, H. (1996). Making Wald tests work for cointegrated VAR systems. Econometric Reviews, 15(4), 369–386.


Dynamic OLS

Dynamic Ordinary Least Squares Estimator

dynamic least squares cointegration estimator

Stock & Watson (1993); panel extension Kao & Chiang (2001)

Dynamic OLS is a cointegrating-regression estimator introduced by Stock and Watson (1993) that recovers the long-run relationship between I(1) variables. It augments the static regression with leads and lags of the differenced regressors, correcting endogeneity bias parametrically so that the long-run coefficient can be estimated by ordinary least squares.

Least squares augmented with leads and lags of differenced regressors

time series / panel (I(1), cointegrated)

Stock, J. H. & Watson, M. W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica, 61(4), 783–820.

Kao, C. & Chiang, M.-H. (2001). On the Estimation and Inference of a Cointegrated Regression in Panel Data. Advances in Econometrics, 15, 179–222.

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


Domain Adaptation

Alan Uyarlaması (Domain Adaptation) — NLP

Domain adaptation is a natural-language-processing technique that takes a general pretrained language model and fine-tunes it on target-domain data so that it performs better in specialised fields such as medicine, law, and finance. It builds on the transfer-learning ideas behind work like Blitzer et al. (2007) on cross-domain sentiment classification and Lee et al. (2020) on the biomedical BioBERT model.

NLP transfer-learning / fine-tuning pipeline

Fine-tuning a general pretrained language model on target-domain data

Lee, J. et al. (2020). BioBERT: A Pre-trained Biomedical Language Representation Model. Bioinformatics.

Blitzer, J. et al. (2007). Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. ACL.

bert-embeddings text-classification sentiment-analysis transfer-learning


Domain-Adaptive Pre-training with BERT for Text Classification

DAPT BERT classification
domain-adaptive pre-training
domain-specific BERT fine-tuning
BERT DAPT

Gururangan et al. (2020); earlier domain-specific instances include Lee et al. (2020) — BioBERT

Domain-adaptive BERT-based classification extends the standard fine-tuning pipeline by first continuing BERT's masked-language-model pre-training on a large corpus of in-domain unlabeled text, then fine-tuning the adapted model on labeled examples for the target classification task. This two-stage approach closes the vocabulary and distributional gap between BERT's general pre-training corpus and specialized domains such as biomedicine, law, finance, or social-media text.

Domain-adaptive pre-training followed by supervised fine-tuning

Unlabeled domain text for pre-training; labeled text for classification fine-tuning

Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 8342–8360.

Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020). BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4), 1234–1240.


Domain-adaptive Convolutional Neural Network (DA-CNN)

domain-adaptive deep convolutional network

Ganin, Y. & Lempitsky, V. (domain-adversarial framework); Tzeng et al. (ADDA)

A domain-adaptive CNN trains a convolutional network on a labeled source domain and adapts its learned feature representations to an unlabeled or lightly labeled target domain, bridging the distribution gap so that visual classifiers transfer reliably across datasets, sensors, or imaging conditions without full re-annotation.

Images or feature maps from source and target domains

Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35.

Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7167–7176.


Domain-adaptive diffusion model

DA-diffusion model
domain-adapted diffusion model
domain-adaptive DDPM
cross-domain diffusion model

Ho et al. (DDPM); domain-adaptation variants popularized by Gal et al. and Ruiz et al. (2022–2023)

A domain-adaptive diffusion model is a denoising diffusion probabilistic model (DDPM) that is pre-trained on large general datasets and then adapted — through fine-tuning, textual inversion, or LoRA — to generate high-quality outputs in a specific target domain. It combines the powerful generative capacity of diffusion models with domain adaptation techniques, enabling high-fidelity synthesis in specialized areas such as medical imaging, satellite imagery, or domain-specific art styles with limited target-domain data.

Images, text-image pairs, domain-specific datasets

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

Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., & Cohen-Or, D. (2023). An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion. International Conference on Learning Representations (ICLR 2023).

fine-tuned-diffusion-model transfer-learning-diffusion-model domain-adaptive-gan multimodal-diffusion-model domain-adaptive-vision-transformer self-supervised-diffusion-model


Domain-adaptive Doc2Vec

Domain-Adaptive Paragraph Vector (Doc2Vec) for Cross-Domain Document Representation

domain-adapted Doc2Vec
cross-domain paragraph vector
domain-adaptive PV-DM
domain-adaptive PV-DBOW

2014 (Doc2Vec); domain-adaptive application mid-2010s onward

Le & Mikolov (Doc2Vec); domain adaptation literature (Blitzer, Daumé III, and others)

Domain-adaptive Doc2Vec adapts the Paragraph Vector (Doc2Vec) framework so that document embeddings learned on a source domain transfer effectively to a target domain. By aligning the representation space across domains during or after training, the model produces embeddings that are informative on both, enabling cross-domain classification, sentiment analysis, and retrieval with limited target-domain labels.

Unsupervised / domain-adaptive document embedding

Le, Q. V., & Mikolov, T. (2014). Distributed representations of sentences and documents. Proceedings of the 31st International Conference on Machine Learning (ICML 2014), PMLR 32(2), 1188–1196.

Blitzer, J., McDonald, R., & Pereira, F. (2006). Domain adaptation with structural correspondence learning. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006), 120–128.


Domain-adaptive GAN

Domain-Adaptive Generative Adversarial Network

domain-adaptive generative adversarial network

Ganin et al. (DANN); Zhu et al. (CycleGAN)

A Domain-Adaptive GAN combines generative adversarial learning with domain adaptation to bridge the distribution gap between a labeled source domain and an unlabeled or sparsely labeled target domain. By training a generator and discriminator adversarially, the model learns domain-invariant representations or translated samples, enabling a classifier or detector trained on source data to generalize effectively to the target domain without requiring abundant target labels.

Generative adversarial model with domain adaptation

Images, structured features, or other continuous signals from source and target domains

Ganin, Y., Ustunova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35.

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2223–2232.


Domain-adaptive GRU

Domain-Adaptive Gated Recurrent Unit Network

DA-GRU
Domain-Adapted GRU
GRU with Domain Adaptation
Domain-Shift-Robust GRU

Cho et al. (GRU, 2014); Ganin et al. (domain-adversarial framework, 2016)

Domain-Adaptive GRU combines the Gated Recurrent Unit architecture with domain adaptation techniques to train a sequence model on a labeled source domain and transfer it to a different but related target domain, reducing performance degradation caused by distribution shift. It is widely applied in NLP tasks such as cross-domain sentiment analysis, named entity recognition, and text classification where labeled target-domain data is scarce.

Sequential / text / time-series data from heterogeneous domains

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In Proceedings of EMNLP 2014 (pp. 1724–1734). Association for Computational Linguistics.

Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(1), 2096–2030.


Domain-adaptive image classification

Domain-Adaptive Image Classification (Domain Adaptation for Visual Recognition)

domain adaptation for image classification

Ganin, Y. & Lempitsky, V. (domain-adversarial formulation)

Domain-adaptive image classification trains a visual classifier on a labeled source domain and adapts it to a target domain where labeled data are scarce or absent. By aligning feature distributions across domains, the model retains discriminative accuracy on the target distribution without requiring full target re-annotation, making it practical in real-world deployment scenarios where domain shift is unavoidable.

Labeled source-domain images; unlabeled or few-shot target-domain images

Wilson, G., & Cook, D. J. (2020). A survey of unsupervised deep domain adaptation. ACM Transactions on Intelligent Systems and Technology, 11(5), 1–46.


Domain-adaptive Instance Segmentation

Domain-Adaptive Instance Segmentation (Cross-Domain Instance-Level Pixel Segmentation)

domain adaptation for instance segmentation

Chen, Y. et al. (domain-adaptive detection); extended to instance segmentation by multiple groups ~2019–2021

Domain-adaptive instance segmentation extends Mask R-CNN-style architectures to operate across distribution shifts — training on a labeled source domain (e.g., synthetic renderings or daytime images) and adapting to an unlabeled or weakly labeled target domain (e.g., real scenes or nighttime footage). Adversarial feature alignment and self-training close the domain gap at both image-level and instance-level granularity.

Domain adaptation + instance segmentation

Labeled source-domain images + unlabeled or weakly-labeled target-domain images

Chen, Y., Li, W., Sakaridis, C., Dai, D., & Van Gool, L. (2018). Domain Adaptive Faster RCNN for Object Detection in the Wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3339–3348.

VS, V., Gupta, V., Oza, P., Sindagi, V. A., & Patel, V. M. (2021). MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 4516–4526.


Domain-adaptive Multilayer Perceptron

Domain-adaptive Multilayer Perceptron (DA-MLP)

DA-MLP
domain-adaptive MLP
domain-adapted feedforward network
domain adaptation with MLP

A domain-adaptive multilayer perceptron (DA-MLP) is a feedforward neural network trained to learn representations that are useful across a labeled source domain and an unlabeled or differently distributed target domain. By minimizing both a task loss and a domain-discrepancy objective, the MLP generalizes to the target domain with little or no target-domain labels.

Domain adaptation of feedforward neural network

Tabular, text, or image features from source and target domains

Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010). A theory of learning from different domains. Machine Learning, 79(1–2), 151–175.


domain-transfer named entity recognition

Multiple contributors (Blitzer et al., 2006; Daumé, 2007; Lee et al., 2020)

Domain-adaptive Named Entity Recognition (DA-NER) applies named entity recognition to a target domain by transferring or adapting a model trained on a source domain, using techniques such as domain-specific pre-training, adversarial alignment, or feature augmentation. It addresses the performance collapse that standard NER models suffer when deployed outside their training domain.

Sequence labeling with domain adaptation

Text sequences (source-domain labeled + target-domain labeled or unlabeled)

Blitzer, J., McDonald, R., & Pereira, F. (2006). Domain adaptation with structural correspondence learning. Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP), 120–128.


Domain-adaptive NMF Topic Model

Domain-Adaptive Non-negative Matrix Factorization Topic Model

DA-NMF
cross-domain NMF
domain-adaptive topic modeling with NMF
transfer NMF topic model

1999 (NMF); domain adaptation variants ~2010s

Lee, D. D. & Seung, H. S. (NMF base); domain adaptation extensions by the NLP community

Domain-adaptive NMF Topic Modeling applies Non-negative Matrix Factorization to discover latent topics across text from multiple domains, using regularization or shared basis constraints to transfer topic knowledge from a resource-rich source domain to a target domain with limited labeled data. It combines interpretable parts-based decomposition with domain-adaptation objectives to produce topics that are both domain-specific and cross-domain consistent.

Unsupervised topic model with domain adaptation

Text corpora from multiple domains (document-term matrices)

Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.

Non-negative matrix factorization. Wikipedia.

nmf-topic-model lda-topic-model domain-adaptive-lda-topic-model transfer-learning-with-nmf-topic-model topic-modeling multilingual-nmf-topic-model


Domain-adaptive Question Answering

Domain-Adaptive Question Answering (DA-QA)

DA-QA
domain-adapted QA
domain-specific question answering
cross-domain question answering

Multiple (e.g., Garg et al.; Yue et al.)

Domain-adaptive Question Answering (DA-QA) adapts a pre-trained language model — typically BERT or RoBERTa — first trained on general QA benchmarks such as SQuAD to answer questions accurately in a new target domain (e.g., biomedical, legal, financial) where labelled data is scarce. Combining domain-adaptive pre-training with task fine-tuning yields substantially stronger performance than direct fine-tuning alone.

Domain adaptation for extractive/generative QA

Text (passages, questions, answer spans)

Garg, S., Vu, T., & Moschitti, A. (2020). TANDA: Transfer and Adapt Pre-Trained Transformer Models for Answer Sentence Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 34(5), 7780–7788.

Yue, X., Zeng, Z., Shi, Y., Zhang, C., & Song, Y. (2022). Domain-adaptive Pre-training Methods for Natural Language Understanding. arXiv preprint.


DA-RNN
domain-adaptive RNN
domain-adapted recurrent network
cross-domain RNN

Ganin et al.; Pan & Yang (domain adaptation frameworks applied to RNNs)

A Domain-adaptive Recurrent Neural Network (DA-RNN) is a recurrent neural network trained on a source domain and adapted to a target domain using domain adaptation techniques such as adversarial training, feature alignment, or fine-tuning. It enables sequential models to generalise across domains when labeled target-domain data is scarce or unavailable.

Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.


Domain-adaptive reinforcement learning

Domain-Adaptive RL
DARL
Cross-domain RL
Transfer RL with domain adaptation

Multiple contributors (Taylor & Stone 2009 survey; Kim et al. 2020 among key formalizations)

Domain-Adaptive Reinforcement Learning (DARL) extends standard RL by enabling a policy trained in one environment or domain to transfer and generalise effectively to a different but related target domain. It addresses the domain-shift problem — where dynamics, observations, or reward structures differ between training and deployment — through alignment, adaptation, or domain-randomisation techniques, reducing the need to collect costly experience in the target domain.

State-action trajectories from source and target domains

Kim, K., Kim, H., Lim, H., & Choi, J. (2020). Domain Adaptive Reinforcement Learning with Model-Based Approach. arXiv preprint arXiv:2102.03170.

deep-reinforcement-learning transfer-learning sim-to-real-transfer meta-reinforcement-learning domain-randomization proximal-policy-optimization


Domain-Adaptive RoBERTa-based Text Classification

DA-RoBERTa
domain-adapted RoBERTa classifier
RoBERTa domain adaptation
domain-specific RoBERTa fine-tuning

Liu et al. (RoBERTa); Gururangan et al. (domain-adaptive pretraining)

Domain-adaptive RoBERTa-based classification extends the RoBERTa transformer by first continuing its masked-language-model pretraining on a domain-specific corpus before fine-tuning for a classification task. This two-stage adaptation bridges the gap between general web-crawled training data and specialized fields such as biomedical, legal, or scientific text, consistently outperforming standard RoBERTa fine-tuning when target-domain text is available.

Pre-trained transformer with domain-adaptive pretraining and task fine-tuning

Domain-specific text corpora and labeled classification examples

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.

Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. In Proceedings of ACL 2020, pp. 8342–8360.


Domain-adaptive sentence embeddings

Domain-Adaptive Sentence Embeddings (Domain-Adapted Sentence Transformers)

domain-adapted sentence transformers
domain-specific sentence embeddings
target-domain sentence representations
DAPT sentence embeddings

Reimers, N. & Gurevych, I. (Sentence-BERT); Gururangan et al. (domain-adaptive pretraining)

Domain-adaptive sentence embeddings extend general-purpose sentence encoders — such as Sentence-BERT — by continuing their training on domain-specific text. The result is a fixed-length vector representation that captures both universal language understanding and the vocabulary, style, and semantic nuances of the target domain, improving downstream NLP tasks such as semantic search, clustering, and classification.

Text (domain-specific corpora, sentence pairs or unlabeled documents)

Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of EMNLP-IJCNLP 2019, pp. 3982–3992.

Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D. & Smith, N. A. (2020). Don't Stop Pretraining: Adapt Language Models to Domains and Tasks. Proceedings of ACL 2020, pp. 8342–8360.


Domain-adaptive Sentiment Analysis

Domain-adaptive Sentiment Analysis (Cross-Domain Opinion Mining with Domain Adaptation)

domain transfer sentiment classification

Domain-adaptive sentiment analysis trains a sentiment model on one or more labeled source domains (e.g., product reviews) and adapts it to a target domain (e.g., social media posts or news) where labels are scarce or absent. By bridging the vocabulary and distributional gap between domains, it achieves strong sentiment classification without requiring large labeled corpora in every target domain.

Domain adaptation for text classification

Text (reviews, social media, news across multiple domains)

Blitzer, J., Dredze, M., & Pereira, F. (2007). Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL), 440–447.

Pan, S. J., Ni, X., Sun, J.-T., Yang, Q., & Chen, Z. (2010). Cross-domain sentiment classification via spectral feature alignment. Proceedings of the 19th International Conference on World Wide Web (WWW), 751–760.


Domain-adaptive Text Summarization

Domain-adaptive Text Summarization (Domain Adaptation for Abstractive and Extractive Summarization)

domain-adapted summarization
domain-specific summarization
cross-domain summarization
DA-summarization

Multiple contributors; domain adaptation methods consolidated via transformer-era NLP (c. 2019–2021)

Domain-adaptive text summarization fine-tunes or adapts a pre-trained sequence-to-sequence language model on a target domain corpus so that summaries conform to domain-specific vocabulary, style, and factual constraints. It bridges the gap between general-purpose summarization models trained on news or web data and specialized domains such as biomedical literature, legal documents, scientific papers, or financial reports.

Domain adaptation of sequence-to-sequence neural summarization

Text corpora (in-domain documents and optional out-of-domain pre-training data)

Fabbri, A. R., KryŜiński, W., McCann, B., Xiong, C., Socher, R., & Radev, D. (2021). SummEval: Re-evaluating Summarization Evaluation. Transactions of the Association for Computational Linguistics, 9, 391–409.

Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On Faithfulness and Factuality in Abstractive Summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pp. 1906–1919.


Domain-adaptive transformer

DAT
domain-adaptive Transformer
domain adaptation with Transformers
transfer-learning Transformer

Various (Vaswani et al. 2017 for Transformers; domain adaptation extensions emerged 2019–2022)

A Domain-Adaptive Transformer (DAT) is a Transformer-based model — such as BERT or ViT — extended with an explicit domain-alignment objective so that learned representations transfer well from a labeled source domain to a different, often unlabeled, target domain. The approach combines the powerful representation capacity of Transformers with domain adaptation techniques such as adversarial training or contrastive alignment to minimise domain shift.

Pre-trained model fine-tuned with domain-shift adaptation

Text, images, or multimodal data from source and target domains

Ni, J., Hernandez Abrego, G., Constant, N., Ma, J., Hall, K., Cer, D., & Yang, Y. (2021). Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models. Findings of ACL 2022. arXiv:2108.08877.

Guo, J., Shah, D., & Barzilay, R. (2022). Multi-Source Domain Adaptation with Mixture of Experts. In Proceedings of EMNLP 2018. arXiv:1809.02060.

bert vision-transformer transfer-learning domain-adversarial-neural-network fine-tuning contrastive-learning


Domain-adaptive variational autoencoder

Ilse, M.; Tomczak, J. M.; Louizos, C.; Welling, M.

A Domain-Adaptive Variational Autoencoder (DA-VAE) extends the standard VAE framework to learn disentangled latent representations that separate domain-specific variation from class-relevant and domain-invariant content, enabling models trained on a source domain to generalise effectively to a different but related target domain with limited or no target labels.

Images, text, multimodal (labeled source + unlabeled or scarce-label target domain data)

Ilse, M., Tomczak, J. M., Louizos, C., & Welling, M. (2020). DIVA: Domain Invariant Variational Autoencoders. Proceedings of the Third Conference on Medical Imaging with Deep Learning (MIDL 2020), PMLR 121, 322–348.

Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).

variational-autoencoder domain-adversarial-neural-network conditional-variational-autoencoder generative-adversarial-network transfer-learning domain-adaptation


Domain-adaptive vision transformer

Domain Adaptation with Vision Transformer

Multiple groups (Yang et al., 2023; Xu et al., 2021; Ma et al., 2022)

Domain-Adaptive Vision Transformer (DA-ViT) applies domain adaptation techniques — such as adversarial alignment, self-training, or attention-level bridging — on top of a pretrained Vision Transformer backbone to transfer visual knowledge from a labeled source domain to an unlabeled or lightly labeled target domain, reducing the distribution shift that limits standard ViT fine-tuning.

Domain adaptation + Vision Transformer ensemble

Images (source domain + unlabeled or labeled target domain)

Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR).

Yang, L., Balaji, Y., Lim, S. N., & Shrivastava, A. (2023). TVT: Transferable Vision Transformer for Unsupervised Domain Adaptation. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 520-530.


Domain-adaptive Word2Vec

Domain-Adaptive Word2Vec (Domain-Specific Word Embedding Training or Fine-Tuning)

domain-specific Word2Vec
domain-adapted word embeddings
domain Word2Vec
specialized Word2Vec

Mikolov, T. et al. (Word2Vec); domain adaptation practice emerged in NLP community ~2014–2016

Domain-adaptive Word2Vec trains or fine-tunes Word2Vec embeddings on a domain-specific text corpus so that word vectors capture the specialized vocabulary, semantic relationships, and jargon of a target field — such as clinical medicine, legal text, financial reports, or scientific literature — rather than reflecting general-purpose web or news language.

Domain-specific text corpora (clinical notes, legal documents, scientific articles, social media, etc.)

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. In Proceedings of ICLR Workshop.

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT 2019, pp. 4171–4186. Association for Computational Linguistics.


Dose-Escalation Design

Dose-Escalation Design (Continual Reassessment Method)

Continual Reassessment Method
CRM Design
Phase I Dose-Finding Design
Doz Artırma Tasarımı

John O'Quigley, Margaret Pepe & Lloyd Fisher

Dose-Escalation Design, formalized as the Continual Reassessment Method (CRM), is a Bayesian adaptive algorithm for identifying the Maximum Tolerated Dose (MTD) in Phase I clinical trials. Introduced by John O'Quigley, Margaret Pepe, and Lloyd Fisher in 1990, CRM treats dose-toxicity response as a parametric curve, updates a prior probability model after each patient's outcome, and assigns subsequent patients to the dose currently estimated closest to a pre-specified target toxicity probability.

O'Quigley, J., Pepe, M., & Fisher, L. (1990). Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics, 46(1), 33–48.


Dose-Response Analysis

Dose-Response Analysis in Epidemiology and Toxicology

exposure-response analysis
concentration-response modeling
dose-response modeling
DRA

Conceptual roots 16th century; modern epidemiological application mid-20th century

Paracelsus (conceptual foundation); formalized by John Snow and later Bradford Hill

Dose-response analysis quantifies the relationship between the magnitude of an exposure (the dose) and the probability or rate of an outcome (the response). It is a core analytical strategy in epidemiology and toxicology, providing evidence that increasing exposure systematically increases — or decreases — the risk of disease. A demonstrated dose-response gradient is one of Bradford Hill's classic criteria supporting causal inference.

Continuous or ordinal exposure data with binary or time-to-event outcomes

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

Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation from summarized dose-response data, with applications to meta-analysis. American Journal of Epidemiology, 135(11), 1301–1309.

cohort-study case-control-study survival-analysis meta-analysis regression-analysis ecological-study


Dose-Response Design

Dose-Response Experimental Design and Analysis

Doz-Yanıt Tasarımı ve Analizi (Dose-Response)

Classical pharmacology; formalized by ICH E4 (1994) and Ritz et al. (2015)

Dose-response design is a framework for planning and analysing experiments that characterise the relationship between the amount of a stimulus — such as a drug dose or a chemical concentration — and the magnitude of a biological or physiological response. Formalised in regulatory guidance by the ICH E4 guideline (1994) and extensively developed in the statistical literature by Ritz et al. (2015), the framework covers experiment design, four-parameter and five-parameter logistic curve fitting, key benchmark estimates (ED50/EC50, NOAEL, LOAEL), and monotone trend testing via the Williams procedure.

Nonlinear curve fitting and monotone contrast testing

4PL logistic, 5PL logistic, Hill equation

Ritz, C., Baty, F., Streibig, J. C., & Gerhard, D. (2015). Dose-Response Analysis Using R. PLOS ONE, 10(12), e0146021.

ICH E4 (1994). Dose-Response Information to Support Drug Registration. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use.

one-way-anova nonlinear-regression williams-test factorial-design repeated-measures-anova logistic-regression


Dose-Response Meta-Analysis

Dose-Response Meta-Analysis (Non-Linear Dose-Response Synthesis)

Dose-Response Relationship
Non-Linear Meta-Analysis
Dose-Effect Synthesis

Greenland & Longnecker (1992), Advanced by Orsini et al. (2012)

Dose-response meta-analysis is a specialized evidence synthesis method that models the relationship between exposure dose (or intensity, duration, quantity) and health outcome across multiple studies, assessing whether effects follow a linear trend, nonlinear curve, or threshold pattern. Pioneered by Greenland and Longnecker (1992) and refined by Orsini et al. (2012), dose-response meta-analysis answers critical questions like 'Does cardiovascular disease risk increase consistently with salt intake, or is there a threshold above which risk plateaus?' and 'Does the benefit of physical activity increase linearly with exercise duration, or do diminishing returns occur at high doses?' This method is essential for risk assessment, policy-setting on safe exposure limits, and optimizing treatment dosing.

Greenland, S., & Longnecker, M. P. (1992). Methods for trend estimation of environmental health risks, with application to exposure to contaminated groundwater. Statistics in Medicine, 11(14‐15), 1837–1847.

Orsini, N., Li, R., Wolk, A., Khudyakov, P., & Spiegelman, D. (2012). Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software. American Journal of Epidemiology, 175(1), 66–73.

Berlin, J. A., Longnecker, M. P., & Greenland, S. (1993). Meta-analysis of epidemiologic dose-response studies. American Journal of Epidemiology, 140(1), 75–82.

meta-analysis meta-regression dose-response-curve non-linear-relationships exposure-response


Dosimetry Measurement

Dosimetry Measurement and Radiation Detection

dose measurement
radiation monitoring
exposure quantification

Dosimetry measurement is the experimental quantification of radiation dose and exposure, originating from Röntgen and Becquerel's 1890s discoveries. It employs specialized detectors (ion chambers, TLD, Geiger counters) to measure photon and particle energy deposition in biological tissue or materials, providing direct evidence of exposure for worker protection, patient dose verification, and environmental monitoring.

Knoll, G. F. (2010). Radiation Detection and Measurement (4th ed.). John Wiley & Sons.

International Commission on Radiological Protection (2019). Occupational Intakes of Radionuclides: Part 3. Publication 137.

radiation-dose-assessment dosimetry-measurement activation-analysis monte-carlo-neutron-particle radiation-protection-optimization


Double-blind AB design

Double-blind AB Single-Subject Experimental Design

1960s (AB design); double-blinding integration in single-case clinical research from the 1980s–1990s

Derived from the AB single-subject design tradition (Sidman 1960; Baer, Wolf, & Risley 1968) combined with double-blinding conventions from clinical trial methodology

The double-blind AB design is a single-subject experimental approach that sequences a baseline phase (A) and an intervention phase (B) while concealing phase allocation from both the participant and the outcome assessor. It merges the idiographic focus of single-case methodology with the bias-control mechanism of double-blinding, making it especially useful in clinical rehabilitation, pain research, and behavioral medicine when objective measurement of an individual's response to treatment is the primary goal.

Single-subject experimental design with double-blinding

Repeated-measures behavioral, clinical, or physiological outcome data collected over time from one or a small number of individuals

Kazdin, A. E. (1982). Single-Case Research Designs: Methods for Clinical and Applied Settings. Oxford University Press.

Backman, C. L., & Harris, S. R. (1999). Case studies, single-subject research, and N of 1 randomized trials: Comparisons and contrasts. American Journal of Physical Medicine and Rehabilitation, 78(2), 170–176.


Double-blind A/B test

Double-blind A/B Test (Randomized Controlled Experiment with Double-blinding)

double-blind split test
double-blinded A/B experiment
blinded two-arm randomized experiment
double-blind controlled A/B trial

1935 (Fisher's formal randomized design); double-blinding in A/B testing: 1990s–2000s

Evolved from clinical trial methodology; early systematic blinding attributed to James Lind (1753) and formalized by R. A. Fisher (1935)

A double-blind A/B test is a randomized experiment that compares two variants — a control (A) and a treatment (B) — while concealing group assignment from both participants and those administering or assessing the experiment. Combining the causal isolation of randomized assignment with blinding on both sides eliminates expectation-driven bias from participants and evaluator bias from analysts or administrators, producing cleaner causal estimates of treatment effect.

Randomized controlled experiment with blinding

Continuous, binary, or count outcome measurements from two randomized groups

Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomised trials. BMJ, 340, c332.

Kohavi, R., Longbotham, R., Sommerfield, D., & Henne, R. M. (2009). Controlled experiments on the web: Survey and practical guide. Data Mining and Knowledge Discovery, 18(1), 140-181.


Double-blind adaptive experiment

Double-Blind Adaptive Experimental Design

double-blind adaptive design
blinded adaptive trial
double-blind adaptive RCT
adaptive double-blind study

Conceptual roots 1970s–1990s; regulatory codification 2004–2019

Formalized through FDA adaptive design guidance and work by Scott Berry, Donald Berry, and colleagues

A double-blind adaptive experiment combines two powerful design features: double-blinding, which conceals treatment assignment from both participants and outcome assessors to prevent bias, and adaptive modification, which allows pre-specified changes to the trial's course — such as sample size re-estimation, allocation ratio shifts, or arm dropping — based on accumulating interim data. The result is a rigorous, bias-protected design that can respond to emerging evidence without compromising inferential validity.

Experimental design combining blinding and adaptive modification

Continuous, binary, or ordinal outcome data from controlled experiments

U.S. Food and Drug Administration. (2019). Adaptive Designs for Clinical Trials of Drugs and Biologics: Guidance for Industry. FDA.

Berry, S. M., Carlin, B. P., Lee, J. J., & Muller, P. (2010). Bayesian Adaptive Methods for Clinical Trials. CRC Press.


Double-blind Control Group Experimental Design

Double-blind Randomized Experiment with Control Group

double-blind controlled experiment
DB-CG design
double-masked controlled trial
double-blind controlled study

1930s–1950s (formalized in clinical trial methodology)

R. A. Fisher (experimental control foundations); blinding practices evolved in clinical research through the 20th century

A double-blind control group experimental design is a rigorous experimental structure in which participants are randomly assigned to at least one treatment group and one control group, while both the participants and the researchers collecting or assessing outcomes are kept unaware of group assignment. By combining allocation concealment with blinding at two levels, the design minimizes expectancy bias, placebo effects, and assessor bias simultaneously, making it a cornerstone of high-quality intervention research in medicine, psychology, and the social sciences.

Quantitative outcome measurements (continuous, ordinal, or binary)

Fisher, R. A. (1935). The Design of Experiments. Oliver and Boyd.


Double-blind field experiment

double-masked field trial
double-blind naturalistic experiment
blinded field study
DB field experiment

1960s onward (field experiment tradition); double-blind controls applied from 1970s in social and policy field trials

Fisher, R. A. (randomized field trials); double-blind practice traced to 19th-century clinical research, formalized for field settings by Campbell & Stanley (1963)

A double-blind field experiment combines the high external validity of a real-world field setting with double-blind masking, in which neither the participants nor the personnel delivering the treatment know who has been assigned to the treatment or control condition. This design controls simultaneously for participant expectation effects and for experimenter/enumerator demand effects, making it one of the most rigorous tools available for causal inference outside the laboratory.

Quantitative outcome measures collected in natural settings (surveys, administrative records, behavioral observations)

Gerber, A. S., & Green, D. P. (2012). Field Experiments: Design, Analysis, and Interpretation. W. W. Norton.

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.


Double-blind fractional factorial experiment

Double-blind Fractional Factorial Experiment

double-blind FFE
blinded fractional factorial design
double-blind FFD
masked fractional factorial experiment

1960s onward (combination widely used in pharmaceutical and food science research)

Fractional factorial: Box & Hunter (1961); double-blind convention: clinical trial methodology (mid-20th century)

A double-blind fractional factorial experiment combines two powerful methodological protections: fractional factorial design, which tests a carefully chosen subset of all possible factor combinations to achieve efficiency, and double-blind administration, which prevents both participants and assessors from knowing which treatment combination has been applied. The result is an experiment that is both resource-efficient and protected against expectation and assessment bias.

Controlled experimental design with blinding and factor-space reduction

Continuous or categorical outcome measurements from factorial treatment combinations

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

Friedman, L. M., Furberg, C. D., & DeMets, D. L. (1991). Fundamentals of Clinical Trials (2nd ed.). Mosby Year Book.


Double-blind Full Factorial Experiment

Double-blind Full Factorial Experimental Design

double-blind complete factorial experiment

1935 (factorial foundations, Fisher); double-blind combined application from 1950s onward

Full factorial design: Ronald A. Fisher; double-blind masking: formalized in clinical research mid-20th century

A double-blind full factorial experiment crosses every level of every independent variable to create all possible treatment combinations, while ensuring that neither participants nor outcome assessors know which condition each participant has been assigned to. This design simultaneously achieves comprehensive examination of main effects and all interactions, and protection against performance and detection bias through blinding — making it especially valuable in clinical, pharmacological, and behavioral research.

Controlled experimental design with masking

Continuous, ordinal, or categorical outcome measures collected under blinded conditions

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

Schulz, K. F., & Grimes, D. A. (2002). Blinding in randomised trials: hiding who got what. The Lancet, 359(9307), 696–700.


Double-blind laboratory experiment

double-blind lab experiment
double-masked laboratory experiment
DB lab experiment
double-blind controlled lab study

Mid-20th century (widespread adoption ~1950s onward)

Rooted in 19th-century pharmacological and psychological research traditions; systematized in clinical and experimental science through the 20th century

A double-blind laboratory experiment is a controlled experimental design conducted in a laboratory setting in which neither the participants nor the researchers directly administering the treatment know which condition each participant has been assigned to. This dual blinding, combined with the high degree of environmental control characteristic of laboratory settings, minimizes both participant expectancy effects and experimenter bias, making it one of the most rigorous designs available for isolating causal relationships between independent and dependent variables.

Controlled experimental design with blinding

Quantitative outcome measurements collected in controlled laboratory conditions


Double-blind Multiple Baseline Design

Double-blind Multiple Baseline Single-Subject Experimental Design

DB-MBD
blinded multiple baseline design
masked multiple baseline design
double-blind MBD

1968 (multiple baseline); double-blind extension applied from 1980s onward in clinical behavioral research

Multiple baseline: Baer, Wolf & Risley (1968); double-blind procedural extension adapted from clinical trial methodology

The double-blind multiple baseline design is a single-subject experimental design in which an intervention is introduced sequentially across two or more independent baselines — behaviors, individuals, or settings — while outcome assessors (and ideally participants) remain unaware of which baseline is currently in the intervention phase. The double-blind procedural overlay reduces measurement bias and demand characteristics, strengthening causal inference beyond what a standard multiple baseline design offers.

Single-subject experimental design with blinded outcome assessment

Repeated-measures behavioral or clinical outcome data (continuous observation records, standardized rating scales)

Baer, D. M., Wolf, M. M., & Risley, T. R. (1968). Some current dimensions of applied behavior analysis. Journal of Applied Behavior Analysis, 1(1), 91–97.

Kazdin, A. E. (2011). Single-Case Research Designs: Methods for Clinical and Applied Settings (2nd ed.). Oxford University Press.


Double-blind pretest-posttest experimental design

Double-Blind Pretest-Posttest Experimental Design

DB-pretest-posttest design
double-blind pre-post design
masked pretest-posttest RCT
double-masked pre-post experiment

Mid-20th century (combined form widely adopted 1960s onward)

Campbell & Stanley (formalized pretest-posttest design, 1963); double-blind blinding convention developed in clinical pharmacology (19th-20th century)

The double-blind pretest-posttest experimental design is a true experiment in which participants are randomly assigned to treatment and control conditions, outcome data are collected both before and after the intervention, and neither participants nor outcome assessors know which condition each participant received. Combining baseline measurement with strong blinding, the design controls for both pre-existing group differences and expectancy-driven bias, making it a gold-standard approach in clinical and behavioral research.

Continuous, ordinal, or count outcome measurements at two time points

Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. In N. L. Gage (Ed.), Handbook of Research on Teaching (pp. 171-246). Rand McNally.


Double-blind single-subject experimental design

Double-Blind Single-Subject Experimental Design

double-blind single-case experimental design

1970s–1980s (systematic integration of blinding into SCED)

Barlow, Hersen, and colleagues (single-subject tradition); double-blind masking adapted from clinical trial methodology

A double-blind single-subject experimental design applies systematic masking — concealing treatment assignment from both the participant and the outcome assessor — within a within-person repeated-measures framework. It is used when researchers need strong causal inference about an intervention's effect on a single individual while guarding against placebo responses and observer bias. Particularly prominent in pharmacological, behavioral, and clinical rehabilitation research.

Experimental single-subject design with double-blind masking

Repeated behavioral or clinical outcome measurements on a single participant across phases

Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single Case Experimental Designs: Strategies for Studying Behavior Change (3rd ed.). Pearson.


Double-blind Solomon four-group design

Double-blind Solomon Four-Group Experimental Design

double-blind S4GD
blinded Solomon design
double-blind four-group design
Solomon four-group with double-blind

1949 (Solomon design); double-blind blinding integrated in 20th-century experimental practice

Richard L. Solomon (base design); double-blind protocol is a general methodological standard

The double-blind Solomon four-group design combines Richard Solomon's classic four-group structure — which isolates pretest sensitization effects — with double-blind blinding, ensuring that neither participants nor outcome assessors know group assignments. This combination yields high internal validity by controlling simultaneously for testing effects, expectancy bias, and experimenter influence, making it one of the most rigorous true experimental designs available.

Continuous or categorical outcome measures from pretest and posttest assessments

Solomon, R. L. (1949). An extension of control group design. Psychological Bulletin, 46(2), 137–150.

Campbell, D. T., & Stanley, J. C. (1963). Experimental and Quasi-Experimental Designs for Research. Rand McNally.


Double Bootstrap

Çift Bootstrap (Double / Iterated Bootstrap)

The double bootstrap is a resampling method that calibrates a bootstrap confidence interval with a second, nested layer of bootstrap to bring its actual coverage closer to the nominal level. Introduced by Hall (1986) and Beran (1987), it is especially valuable for small samples and skewed distributions where a single-layer bootstrap under-covers.

Resampling calibration (nested bootstrap)

Two-level (outer + inner) bootstrap resampling

Hall, P. (1986). On the Bootstrap and Confidence Intervals. Annals of Statistics, 14(4), 1431-1452.

Beran, R. (1987). Prepivoting to Reduce Level Error of Confidence Sets. Biometrika, 74(3), 457-468.

bootstrap-inference wild-bootstrap bayesian-bootstrap block-bootstrap permutation-test


Double Machine Learning

Debiased Machine Learning
Neyman Orthogonal Score Estimation
Partialing-Out Lasso
Çift Makine Öğrenmesi

Double/Debiased Machine Learning (DML), introduced by Chernozhukov et al. (2018), is a semiparametric framework for estimating causal or structural parameters in the presence of high-dimensional controls. It uses flexible machine learning methods to model nuisance functions—the conditional expectations of the outcome and the treatment given covariates—and then constructs a debiased estimator of the target parameter that achieves root-n consistency and valid inference despite the regularization bias inherent in high-dimensional settings.

Root-n consistent under high-dimensional nuisance

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal, 21(1), C1–C68.


Double Sampling

Double Sampling (also called two-phase or multistage sampling) is a survey design in which a large preliminary sample is collected using inexpensive methods or partial information, then a smaller subsample is drawn from it and measured in detail. Pioneered by Jerzy Neyman in 1938, it is particularly useful when a cheap surrogate measurement is available but true measurement is expensive.

Neyman, J. (1938). Contribution to the theory of sampling human populations. Journal of the American Statistical Association, 33(201), 101–116.

Hansen, M. H., & Hurwitz, W. N. (1943). On the theory of sampling from finite populations. Annals of Mathematical Statistics, 14(4), 333–362.

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

stratified-sampling ranked-set-sampling systematic-sampling cluster-sampling


Doubly Robust Estimation in Education Research

Doubly Robust Estimation Applied to Education Research

doubly robust causal estimation for educational outcomes

Robins, Rotnitzky & Zhao (1994); Bang & Robins (2005)

Doubly robust estimation (DR) is a semiparametric causal inference approach that combines an outcome regression model with a propensity score model. In education research, it is used to estimate the causal effect of educational programs, interventions, or policies on student outcomes when treatment assignment is non-random but observed covariates can account for selection bias. The estimator is consistent if either — not necessarily both — of the two component models is correctly specified.

Causal inference / semiparametric estimator

Observational cross-sectional or panel data with a binary treatment and continuous or binary outcome

Bang, H., & Robins, J. M. (2005). Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics, 61(4), 962-973.

Karim, M. E., Petkau, J., Gustafson, P., Tremlett, H., & BeAMS Study Group. (2018). Comparison of statistical approaches dealing with time-dependent confounding in drug effectiveness studies. Statistical Methods in Medical Research, 27(6), 1709-1722.

propensity-score-matching inverse-probability-weighting doubly-robust-estimation marginal-structural-model propensity-score-weighting difference-in-differences


Doubly Robust Estimation

Augmented Inverse Probability Weighting (AIPW) / Doubly Robust Estimation

Çift Gürbüz Kestirici (Augmented IPW / AIPW)

Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified.

Augmented inverse probability weighting (AIPW)

Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129.

Bang, H. & Robins, J. M. (2005). Doubly Robust Estimation in Missing Data and Causal Inference Models. Biometrics, 61(4), 962-973.

inverse-probability-weighting propensity-score-matching causal-mediation ols-regression logistic-regression


DPPH Radical Scavenging Assay

2,2-Diphenyl-1-Picrylhydrazyl Radical Scavenging Assay

DPPH antioxidant assay
DPPH free radical scavenging method
DPPH decolorization assay
radical scavenging activity test

Radical scavenging / antioxidant capacity

1958 (Blois); widely standardised from 1995 (Brand-Williams et al.)

Blois, M. S. (first application); Brand-Williams et al. (standardised protocol)

The DPPH radical scavenging assay is a rapid, widely used spectrophotometric method for measuring the antioxidant capacity of foods, plant extracts, and purified compounds. It quantifies how effectively a sample neutralises the stable synthetic free radical DPPH (2,2-diphenyl-1-picrylhydrazyl) by measuring the resulting colour change from deep violet to yellow, making it a cornerstone technique in food science, nutraceutical research, and phytochemistry.

Absorbance readings (numerical, continuous)

Blois, M. S. (1958). Antioxidant determinations by the use of a stable free radical. Nature, 181(4617), 1199–1200.

Brand-Williams, W., Cuvelier, M. E., & Berset, C. (1995). Use of a free radical method to evaluate antioxidant activity. LWT — Food Science and Technology, 28(1), 25–30.

abts-assay frap-assay orac-assay total-phenolic-content antioxidant-capacity-measurement cuprac-assay


DPSIR Framework

Driving force, Pressure, State, Impact, Response (DPSIR) Framework

OECD, refined by European Environment Agency

The DPSIR Framework (Driving force, Pressure, State, Impact, Response) is a diagnostic and policy tool developed by the OECD (1993) and refined by the European Environment Agency (1999) to structure environmental and sustainability problems. It organizes causal relationships from economic activity through to policy interventions, enabling governments and organizations to identify where to intervene for environmental improvement.

European Environment Agency (1999). Environmental Indicators: Typology and Overview. EEA Technical Report No. 25. Copenhagen: EEA.

Smeets, E., & Weterings, R. (1999). Environmental indicators: Typology and overview. European Environment Agency Report No. 25/1999.

OECD (1993). OECD core set of environmental indicators for environmental performance reviews. OECD Environment Monographs No. 83. Paris: OECD Publishing.


Drift Diffusion Model

DDM
Brownian Motion Model
Sequential Sampling Model

The Drift Diffusion Model (DDM) is a mathematical framework for understanding rapid binary decision-making by modeling the accumulation of evidence over time as a random walk with drift. Developed by Roger Ratcliff in the 1970s, it predicts both choice probabilities and response time distributions, providing insight into the cognitive processes underlying decisions in perceptual discrimination, recognition memory, and choice tasks.

Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59-108.

Ratcliff, R., & McKoon, G. (2008). The diffusion model: A universal model for rapid decision. Psychological Review, 115(2), 357-380.

Wagenmakers, E.-J., van der Maas, H. L. J., & Grasman, R. P. P. P. (2007). An EZ-diffusion model for response time and accuracy. Psychonomic Bulletin & Review, 14(1), 3-22.

signal-detection-theory accumulator-models response-time-analysis sequential-sampling


Drifter Lagrangian Analysis

Drifter Lagrangian analysis tracks the motion of water parcels using surface drifters (buoys with attached drogues) to measure ocean currents directly. Developed by Robert Davis in the 1980s, this method provides direct observation of water parcel trajectories and enables estimation of eddy diffusivity, transport pathways, and mixing. Drifter data complement Eulerian (fixed-point) observations by capturing the Lagrangian perspective of fluid motion.

Davis, R. E. (1985). Drifter observations of coastal surface currents during CODE: The method and descriptive view. Journal of Geophysical Research, 90(C3), 4741-4755.

Lumpkin, R., & Pazos, M. (2007). Measuring surface currents with Surface Velocity Program drifters: the instrument, its data, and its applications. In A. Griffa, A. D. Kirwan, A. J. Mariano, T. M. Ozgokmen, & H. T. Rossby (Eds.), Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics (pp. 39-67). Cambridge University Press.


Driscoll-Kraay SE

DK Standard Errors
Driscoll-Kraay Covariance Estimator
Spatial-Temporal HAC Standard Errors
Driscoll-Kraay Standart Hatalar

Driscoll-Kraay standard errors provide a nonparametric, heteroskedasticity- and autocorrelation-consistent (HAC) covariance estimator for balanced and unbalanced panel datasets. Introduced by Driscoll and Kraay in 1998, the method corrects inference when residuals exhibit cross-sectional dependence, serial autocorrelation, and heteroskedasticity simultaneously—problems common in macroeconomic and international finance panels where units such as countries or industries share common shocks.

Nonparametric heteroskedasticity- and autocorrelation-consistent (HAC) covariance estimator for panel data

Driscoll, J. C., & Kraay, A. C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 80(4), 549–560.


Droop Control

Droop Control for Distributed Generation and Microgrids

Frequency droop
Voltage droop
Decentralized control

Droop Control is a decentralized control method that enables independent generators (inverters, microgrids) to operate synchronously without direct communication. Introduced by Guerrero et al. in 2013 for microgrids, droop control uses frequency and voltage deviations as signals to share power. By making generator output depend on frequency and voltage (like synchronous generators), microgrids achieve plug-and-play operation. Essential for modern distributed energy resources and grid resilience.

Decentralized control for synchronous operation of distributed generators

Guerrero, J. M., Vasquez, J. C., Matas, J., Castilla, M., & de Vicuña, L. G. (2013). Hierarchical control of droop-controlled AC and DC microgrids. IEEE Transactions on Power Electronics, 28(11), 4915-4933.

Kundur, P. (1994). Power System Stability and Control. McGraw-Hill.

Bidram, A., & Davoudi, A. (2012). Hierarchical structure of microgrids control system. IEEE Transactions on Smart Grid, 3(4), 1963-1976.


Dropout

Dropout Regularization for Deep Neural Networks

dropout regularization
stochastic dropout
neuron dropout
inverted dropout

Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R.

Dropout is a stochastic regularization technique for training deep neural networks, introduced by Srivastava, Hinton, Krizhevsky, Sutskever, and Salakhutdinov in 2014. During each training step, each neuron is independently switched off with probability (1 − p), preventing the network from co-adapting its units too tightly and thereby reducing overfitting.

Stochastic regularization technique for neural networks

Regularization in supervised deep learning (classification & regression)

p typically 0.5 (hidden layers), 0.8 (input layer)

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929–1958.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 7: Regularization for Deep Learning). MIT Press.

batch-normalization l2-regularization early-stopping deep-neural-network convolutional-neural-network


DRSA

DRSA (Dominance-Based Rough Set Approach) is a sorting multi-criteria decision-making (MCDM) method introduced by Greco, S. Matarazzo, B. Słowiński, R. in 2001. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

Rough-set dominance induction of if-then decision rules for sorting

Greco, S., Matarazzo, B., Słowiński, R. (2001). Rough sets theory for multicriteria decision analysis. European Journal of Operational Research


Drug Attitude Inventory

Thomas P. Hogan, Ahmed G. Awad, and Robert Eastwood

The Drug Attitude Inventory (DAI) is a brief self-report measure developed by Hogan, Awad, and Eastwood in 1983 to assess attitudes toward medication and predicted medication compliance in schizophrenia and other psychiatric conditions. The original 30-item version (DAI-30) and the widely used 10-item short form (DAI-10) capture patients' subjective experience of medication benefit, side effects, and overall willingness to take medication as a predictor of adherence. The DAI is particularly valuable in psychiatric care, where attitudes toward antipsychotic and antidepressant medications strongly predict adherence and clinical outcomes.

Hogan, T. P., Awad, A. G., & Eastwood, R. (1983). A self-report scale predictive of drug compliance in schizophrenics: Reliability and discriminative validity. Psychological Medicine, 13(1), 177-183.


DSC Gelatinization

Differential Scanning Calorimetry for Gelatinization

Differential Scanning Calorimetry (DSC) is a thermal analysis technique that measures the heat absorbed or released by a sample as temperature changes, enabling characterization of starch gelatinization—the structural transformation of starch granules when heated with water. DSC reveals the temperature at which starch swells, the energy required, and the range over which this occurs, providing insight into starch source, processing history, and ingredient interactions.

Biliaderis, C. G. (1991). The structure and interactions of starch with food constituents. Canadian Journal of Physiology and Pharmacology, 69(1), 60-78.

Evans, I. D., & Haisman, D. R. (2004). The effect of solutes on the gelatinization temperature range of potato starch. Journal of Food Science, 47(2), 550-557.


DSGE Model

Dynamic Stochastic General Equilibrium Model

Dinamik Stokastik Genel Denge Modeli (DSGE)

Smets & Wouters; An & Schorfheide (Bayesian DSGE estimation)

A DSGE model is a micro-founded macroeconomic general equilibrium model that combines the optimising decisions of households, firms, and government under rational expectations. Popularised for empirical policy work by Smets and Wouters (2007) and given its Bayesian estimation framework by An and Schorfheide (2007), it is the standard tool for central-bank policy analysis, fiscal-shock simulation, and the study of business-cycle fluctuations.

Micro-founded macroeconomic general equilibrium model

Log-linearisation around steady state; Bayesian estimation via MCMC

Smets, F. & Wouters, R. (2007). Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach. American Economic Review, 97(3), 586–606.

An, S. & Schorfheide, F. (2007). Bayesian Analysis of DSGE Models. Econometric Reviews, 26(2–4), 113–172.

Adjemian, S. et al. (2011). Dynare: Reference Manual, Version 4. Dynare Working Papers, 1.

cge-model var-model vecm-model state-space-model structural-var


DTI Tractography

Diffusion tensor tractography
White matter tractography
Fiber tracking

Diffusion Tensor Imaging Tractography (DTI tractography) is a non-invasive neuroimaging technique that maps white matter fiber bundles in the brain by tracking the three-dimensional diffusion of water molecules along axons. Pioneered by Basser, Mori, and Conturo in the 1990s, DTI tractography reveals the structural connectivity of the brain, enabling visualization of major pathways (corpus callosum, arcuate fasciculus, corticospinal tract) and assessment of fiber integrity. It is widely used in neurosurgical planning, neurological disease assessment, and brain connectivity research.

Basser, P. J., Mattiello, J., LeBihan, D. (1994). Estimation of the effective self-diffusion tensor from the NMR spin echo. Journal of Magnetic Resonance, Series B, 103(3), 247-254.

Mori, S., Crain, B. J., Chacko, V. P., van Zijl, P. C. (1999). Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Annals of Neurology, 45(2), 265-269.

Conturo, T. E., Lori, N. F., Cull, T. S., et al. (1999). Tracking neuronal fiber pathways in the living human brain. Proceedings of the National Academy of Sciences, 96(18), 10422-10427.

quantitative-susceptibility-mapping pet-kinetic-modeling ct-iterative-reconstruction magnetic-resonance-elastography functional-ultrasound


DTW Barycenter Averaging

Dynamic Time Warping Barycenter Averaging

DTW Barycenter Averaging (DBA) is a method for computing the average or representative sequence of a set of time series that respects temporal warping and elastic distance. Unlike Euclidean averaging which requires point-wise alignment, DBA minimizes the sum of Dynamic Time Warping (DTW) distances, producing a meaningful average for sequences with flexible temporal alignments. Introduced by Petitjean and colleagues in 2011, it is widely used in time-series clustering and summarization.

Salvador, S., & Chan, P. (2004). FastDTW: Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis, 11(5), 561–580.

Petitjean, F., Ketterlin, A., & Gançarski, P. (2011). A global averaging method for dynamic time warping, with applications to clustering. Pattern Recognition, 44(3), 678–693.

Cuturi, M., & Blondel, M. (2016). Soft-DTW: A differentiable loss function for time-series. arXiv preprint arXiv:1703.01541.

discrete-wavelet-transform dynamic-time-warping hierarchical-clustering k-means


DTW Gait Analysis

DTW
Gait pattern matching
Temporal gait comparison

Dynamic Time Warping (DTW) is a sequence alignment algorithm that measures similarity between time series of different lengths by allowing flexible temporal matching. Applied to gait analysis, DTW enables comparison of walking patterns across subjects and conditions despite variations in cadence or stride length.

Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1), 43-49.

Wang, Z., Yan, W., & Oates, T. (2013). Time series classification from scratch with deep neural networks: A strong baseline. arXiv preprint arXiv:1611.06455.


Dual-Polarization Radar

Dual-Polarization Radar Measurement System

Dual-pol radar
Polarimetric radar
Dual-polarization
Dual-pol

Dual-polarization (dual-pol) radar is a weather radar system that transmits and receives electromagnetic waves in both horizontal and vertical polarizations simultaneously. This technique, operational in weather services since the 2010s, provides detailed information about precipitation particle type, shape, and size distribution, enabling improved rainfall estimates and better discrimination of hail, rain, and snow.

Kumjian, M. R. (2013). The impact of precipitation on supercell electrification: Lightning potential and storm structure changes. Journal of the Atmospheric Sciences, 69(11), 3353-3375.

Bringi, V. N., & Chandrasekar, V. (2001). Polarimetric Doppler weather radar: Principles and applications. Artech House Publishers.


Dubins Path

The Dubins path is the shortest curve connecting two points in the plane with prescribed initial and terminal tangent directions, subject to a constraint on curvature. Introduced by Lester Dubins in 1957, it solved a fundamental problem in differential geometry and became essential in motion planning for aircraft, helicopters, and autonomous vehicles. A Dubins path consists of circular arcs and straight line segments arranged in a sequence such as RSR (Right-Straight-Right) or LSL (Left-Straight-Left).

Dubins, L. E. (1957). On curves of minimal length with a constraint on average curvature and with prescribed initial and terminal positions and tangents. American Journal of Mathematics, 79(3), 497–516.

Shkel, A. M., & Lumelsky, V. (2001). Classification of the Dubins set. Robotics and Autonomous Systems, 34(2-3), 179–202.

Hota, S., & Ghose, D. (2016). Optimal path planning for aerial vehicles using Dubins curves. IEEE Transactions on Aerospace and Electronic Systems, 52(3), 1400–1416.


DUDIT

The DUDIT is a brief, gender-sensitive screening instrument designed to identify individuals with harmful or hazardous drug use patterns across a wide range of substances. Developed by Berman and colleagues in 2005, it serves as a primary care and public health screening tool to detect drug-related problems before they escalate to dependence or disorder. The DUDIT is freely available and has been validated in multiple languages and settings.

Berman, A. H., Bergman, H., Palmstierna, T., & Schlyter, F. (2005). Evaluation of the Drug Use Disorder Identification Test (DUDIT) in criminal justice and detoxification settings and in a Swedish population sample. European Addiction Research, 11(1), 22–31.

sadq cudit readiness-to-change-questionnaire brief-addiction-monitor


Duke Activity Status Index

functional capacity assessment in cardiovascular disease

The Duke Activity Status Index (DASI) is a 12-item self-report questionnaire that estimates functional capacity—the maximum oxygen consumption (VO2 max) a patient can achieve—based on their ability to perform common daily activities. Developed by Hlatky and colleagues in 1989, the DASI provides a non-invasive assessment of exercise tolerance and cardiovascular fitness equivalent to formal exercise stress testing, making it invaluable for risk stratification, treatment planning, and prognosis in cardiac and pulmonary populations.

Hlatky, M. A., Boineau, R. E., Higginbotham, M. B., Lee, K. L., Mark, D. B., Califf, R. M., Cobb, F. R., & Pryor, D. B. (1989). A brief self-administered questionnaire to determine functional capacity (The Duke Activity Status Index). American Journal of Cardiology, 64(10), 651–654.

seattle-angina-questionnaire minnesota-heart-failure new-york-heart-association-class kansas-city-cardiomyopathy


Duke Health Profile

George R. Parkerson and colleagues at Duke University

The Duke Health Profile (DUKE) is a 17-item self-report measure of health-related quality of life developed by Parkerson and colleagues at Duke University in 1989. It assesses health across six dimensions: physical function, mental health, social function, general health perceptions, anxiety, and depression. The instrument combines brevity with multidimensional assessment, making it practical for clinical and research settings.

Multidimensional health status assessment

Parkerson, G. R., Connis, R. T., Gehlbach, S. H., et al. (1989). The Duke Health Profile: a 17-item measure of health-related quality of life. Medical Care, 28(11), 1056–1072.

Parkerson, G. R., & Gutman, R. A. (1993). Health-related quality of life predictors of survival and medical care utilization. Health Services Research, 28(3), 345–360.

Gehlbach, S. H., Parkerson, G. R., & Connis, R. T. (1995). Numeracy and health outcomes. Journal of Health Psychology, 1(2), 175–194.


DUREL

The DUREL is a brief, five-item self-report measure of religious involvement developed by Koenig and Büssing in 2010. Designed specifically for epidemiological and health services research, it captures three dimensions of religiosity: organizational religious activity (church attendance), non-organizational religious activity (private prayer and study), and intrinsic religiosity (religious motivation and meaning). The scale is widely used in gerontology, medical sociology, and health outcomes research to assess how religious engagement correlates with physical and mental well-being.

Koenig, H. G., & Büssing, A. (2010). The Duke University Religion Index (DUREL): A five-item measure for use in epidemical studies. Religions, 1(1), 78–85.


Dumitrescu-Hurlin Causality

Dumitrescu-Hurlin Panel Granger Causality Test

Panel Granger Causality Test (Heterogeneous)

Elena-Ivona Dumitrescu & Christophe Hurlin

The Dumitrescu-Hurlin (DH) test, introduced by Elena-Ivona Dumitrescu and Christophe Hurlin in their 2012 Economic Modelling article, tests for Granger non-causality in heterogeneous panel datasets. Unlike standard panel causality approaches, it permits each cross-sectional unit to have its own distinct causal relationship, making it well-suited for macro-panels of countries, firms, or regions where homogeneity cannot be assumed.

Non-causality test for heterogeneous panels

Asymptotic Z-bar and Z-bar tilde statistics

Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–1460.


Dunn Index

Dunn Index for Cluster Compactness and Separation

The Dunn Index, introduced by Joseph C. Dunn in 1974, is a metric that captures cluster quality by measuring the ratio of the minimum between-cluster distance to the maximum within-cluster diameter. Higher values indicate well-separated and compact clusters, with better clustering quality.

Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95-104.

davies-bouldin-index silhouette-score calinski-harabasz-index gap-statistic inertia


Dunn Test

Dunn's post-hoc test
Kruskal-Wallis post-hoc
Dunn Testi — Kruskal-Wallis Post-Hoc

Dunn's test is a nonparametric post-hoc procedure introduced by Olive Jean Dunn in 1964 to identify which specific pairs of groups differ significantly after a Kruskal-Wallis test has returned a significant overall result. It compares groups pairwise using rank sums and applies a multiple-comparison correction — most commonly Bonferroni or Holm — to control the family-wise error rate.

Dunn, O.J. (1964). Multiple Comparisons Using Rank Sums. Technometrics, 6(3), 241–252.

kruskal-wallis mann-whitney-u bonferroni-correction one-way-anova nemenyi-test


Duplicate Publication and Salami Slicing in Academic Research

Redundant Publication
Overlapping Publication
Fragmented Research

Duplicate publication occurs when the same research data are published more than once without acknowledgment or justification, presenting the same or substantially similar results as previously published work. Salami slicing is the related practice of dividing the results of a single study into the smallest possible publishable units and submitting them as separate papers to multiply publication counts. Both practices artificially inflate research output, mislead readers, and violate ethical standards upheld by the Committee on Publication Ethics (COPE) and research integrity organizations worldwide.

Committee on Publication Ethics (2023). COPE Guidelines. Flowcharts and Advice on Publication Ethics. COPE.

Hewitt, J. B., Larson, E., & Larson, R. (2011). Duplicate Publication and the Web: A Recipe for Confusion. International Journal of Nursing Studies, 48(2), 129–131.

plagiarism-in-research icmje-authorship-criteria cope-guidelines retraction-process


DuPont Analysis

DuPont Analysis (Profitability Decomposition)

DuPont Decomposition
DuPont Identity
Return on Equity Decomposition
DuPont Analizi

DuPont Analysis is a financial performance framework that decomposes Return on Equity (ROE) into three multiplicative components: net profit margin, asset turnover, and the equity multiplier. Originally developed by engineers at DuPont Corporation in the early 1920s, the method gained renewed academic prominence through Soliman (2008), who demonstrated that market participants exploit DuPont decompositions to forecast future earnings and to distinguish sustainable from transient profitability.

Income statement and balance sheet figures

Return on Equity (ROE) and component drivers

Soliman, M. T. (2008). The use of DuPont analysis by market participants. The Accounting Review, 83(3), 823–853.


Durbin-Watson Test

Durbin-Watson Test for First-Order Autocorrelation

DW test
Durbin-Watson statistic
Durbin-Watson otokorelasyon testi

The Durbin-Watson test, developed by James Durbin and Geoffrey Watson in 1950–1951, detects first-order serial correlation in the residuals of a linear regression. Its statistic ranges from 0 to 4, with a value near 2 indicating no autocorrelation, values toward 0 indicating positive autocorrelation, and values toward 4 indicating negative autocorrelation. It remains one of the most reported regression diagnostics despite well-known limitations.

Test for first-order residual autocorrelation

Durbin, J., & Watson, G. S. (1950). Testing for serial correlation in least squares regression: I. Biometrika, 37(3/4), 409–428.

Durbin, J., & Watson, G. S. (1951). Testing for serial correlation in least squares regression: II. Biometrika, 38(1/2), 159–178.

breusch-godfrey-test ols-regression multiple-linear-regression generalized-least-squares


DEBQ

Tatjana van Strien, C. Peter Herman, Mieke W. Verheijden

The Dutch Eating Behavior Questionnaire is a 33-item self-report instrument designed to assess three distinct eating behavior patterns: restrained eating (cognitive control of food intake), emotional eating (eating in response to negative emotions), and external eating (responsiveness to food cues). Developed by van Strien and colleagues in 1986, it is widely used in research on eating disorders, weight management, and psychological determinants of dietary behavior. The DEBQ is one of the most cited eating behavior questionnaires in behavioral nutrition research.

Van Strien, T., Frijters, J. E., Bergers, G. P., & Defares, P. B. (1986). The Dutch Eating Behavior Questionnaire (DEBQ) for assessment of restrained, emotional, and external eating behavior. International Journal of Eating Disorders, 5(2), 295-315.

Van Strien, T., Herman, C. P., & Verheijden, M. W. (2009). Eating style, overeating, and overweight in a representative Dutch sample. Does external eating play a role? Appetite, 52(2), 380-387.

intuitive-eating-scale food-neophobia-scale nutrition-self-efficacy-scale body-weight-image-satisfaction weight-bias-internalization-scale


Dwarf Mongoose Optimization

The Dwarf Mongoose Optimization (DMO) algorithm is a nature-inspired metaheuristic introduced by Agushaka et al. in 2022, based on the behavioral patterns of dwarf mongoose colonies. Dwarf mongooses exhibit sophisticated group dynamics including sentry behavior (surveillance and exploration), pup care (mentoring), and cooperative hunting. The algorithm translates these social behaviors into optimization mechanisms that balance exploration and exploitation effectively.

Agushaka, J. O., Ezugwu, A. E., & Abualigah, L. (2022). Dwarf mongoose optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 391, 114570.

slime-mould-algorithm harris-hawks-optimization aquila-optimizer grey-wolf-optimizer lion-algorithm


Dyadic Adjustment Scale

The Dyadic Adjustment Scale is the most widely used self-report instrument for measuring the quality of relationships in married or cohabiting couples. Developed by Graham Spanier in 1976, it captures four fundamental dimensions of relationship functioning: consensus (agreement on key domains), satisfaction (contentment in the partnership), cohesion (togetherness and shared activities), and affectional expression (intimacy and passion). The DAS has become a gold standard in couple therapy research, relationship satisfaction studies, and marital intervention trials.

Spanier, G. B. (1976). Measuring dyadic adjustment: New scales for assessing the quality of marriage and similar dyads. Journal of Marriage and the Family, 38(1), 15-28.

Spanier, G. B. (1989). Bequests of the 1980s to family sociology. Journal of Marriage and the Family, 51(4), 825-840.


Dynamic Application Security Testing

DAST
black-box testing
runtime security testing

Dynamic Application Security Testing (DAST) is a security analysis technique that tests a running application by sending various inputs and observing responses to identify vulnerabilities and security flaws. Developed in the 2000s as a complement to static analysis, DAST exercises the application at runtime, finding vulnerabilities that only manifest during execution such as authentication bypass, insecure redirects, and logic flaws. DAST is commonly used for web application testing and is considered a black-box testing approach since the tester requires no knowledge of internal code structure.

Kals, S., Kirda, E., Kruegel, C., & Jovanovic, N. (2006). Secubat: A web vulnerability scanner. In Proceedings of the 15th International Conference on World Wide Web (WWW 2006), pp. 247-256.

McAllister, S., & Kirda, E. (2008). Vulnerability scanning web applications. In Web Application Security, pp. 201-230.


Dynamic Bayesian Hierarchical Model

DBHM
dynamic hierarchical Bayes
Bayesian dynamic multilevel model
state-space hierarchical Bayesian model

A Dynamic Bayesian Hierarchical Model combines the multilevel structure of Bayesian hierarchical models with an explicit time-evolution equation for the latent states. Observations at each time point are linked to unobserved dynamic states, which evolve according to a probabilistic transition law, while a shared hyperprior pools information across units or levels, enabling coherent inference over time and across groups simultaneously.

West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press.

hierarchical-bayesian-inference kalman-filter dynamic-linear-model sequential-monte-carlo particle-filter bayesian-state-space-model


Dynamic Bayesian Inference

online Bayesian inference
sequential Bayesian updating
recursive Bayesian estimation
dynamic Bayesian updating

West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)

Dynamic Bayesian inference is a framework for performing Bayesian updating sequentially as new observations arrive over time. Rather than fitting a static model to a fixed dataset, it tracks how a posterior distribution over latent states or parameters evolves step by step, combining a prior with each new likelihood to produce an updated posterior that propagates forward through time.

Bayesian sequential / online inference framework

time-series, streaming, longitudinal, state-space observations

Murphy, K. P. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. Dissertation, University of California, Berkeley.

kalman-filter particle-filter sequential-monte-carlo dynamic-bayesian-network hierarchical-bayesian-inference bayesian-regression


Dynamic Bayesian Model Averaging

DMA
dynamic model averaging
time-varying BMA
online Bayesian model averaging

Dynamic Bayesian Model Averaging (DMA) extends standard Bayesian model averaging to settings where the best predictive model may change over time. It maintains a probability distribution over a set of competing models and updates that distribution sequentially as new observations arrive, allowing model weights to evolve rather than remaining fixed across the entire sample.

Raftery, A. E., Karny, M., & Ettler, P. (2010). Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technometrics, 52(1), 52-66.

Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-401.

bayesian-model-averaging dynamic-bayesian-network sequential-monte-carlo dynamic-variational-inference kalman-filter dynamic-bayesian-inference


Dynamic Bayesian Network

DBN
temporal Bayesian network
dynamic probabilistic graphical model
two-slice temporal Bayesian network

A Dynamic Bayesian Network (DBN) extends a standard Bayesian network over time by representing how a set of random variables evolve across discrete time steps. It captures both the conditional independence structure among variables at each instant and the probabilistic dependencies between consecutive time slices, enabling principled reasoning about temporal processes under uncertainty.

probabilistic graphical model for sequences

multivariate time-series / sequential observations

Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.

Murphy, K. P. (2002). Dynamic Bayesian Networks: Representation, Inference and Learning. PhD thesis, University of California, Berkeley.

bayesian-network hidden-markov-model kalman-filter sequential-monte-carlo particle-filter hierarchical-bayesian-inference


Dynamic Capabilities Scale

Dynamic Capabilities (DC) represent an organization's capacity to sense new opportunities and threats, seize those opportunities through strategic investments and organizational changes, and reconfigure assets and organizational structures to adapt to shifting competitive environments. Teece (2007) articulated this framework in the Strategic Management Journal, arguing that dynamic capabilities—not static resources—explain sustained competitive advantage in turbulent, knowledge-intensive markets. This scale operationalizes the three core processes underlying DC: sensing market and technology changes, making swift strategic decisions, and reorganizing the firm to exploit new opportunities.

Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350.

Helfat, C. E., & Peteraf, M. A. (2009). Understanding dynamic capabilities: Progress towards a synthesis. Strategic Management Journal, 30(10), 991–1005.

Barreto, I. (2010). Dynamic capabilities: A review of past research and an agenda for the future. Journal of Management, 36(1), 256–280.

absorptive-capacity-scale market-sensing-capability-scale organizational-resilience-scale strategic-orientation-scale innovation-ambidexterity-scale


Dynamic Causal Modeling

Dynamic Causal Modeling for fMRI Brain Networks

Dynamic Causal Modeling (DCM) is a Bayesian framework for specifying and inverting generative models of brain connectivity from neuroimaging data. Introduced by Karl Friston and colleagues in 2003, DCM treats brain regions as dynamical systems and estimates effective connectivity by fitting observed fMRI time series to a biophysically plausible model of neuronal interactions.

Causal modeling pipeline for neuroimaging

Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302.

Stephan, K. E., & Mathys, C. (2015). Computational approaches to neuroscience. Current Opinion in Neurobiology, 25, 85–92.


Dynamic Closeness Centrality

Dynamic Closeness Centrality in Temporal Networks

temporal closeness centrality
time-varying closeness centrality
evolving network closeness
dynamic CC

Tang, J. et al.; Holme, P. & Saramäki, J.

Dynamic closeness centrality extends classic closeness centrality to temporal networks by computing shortest time-respecting paths — paths that traverse edges in chronological order — and averaging inverse distances across all time windows. It reveals which nodes are most efficiently reached within an evolving network, tracking how a node's centrality rises and falls as connections appear and disappear over time.

Centrality measure for temporal networks

Time-stamped edge lists (temporal networks)

Tang, J., Musolesi, M., Mascolo, C., Latora, V. & Nicosia, V. (2010). Analysing information flows and key mediators through temporal centrality metrics. Proceedings of the 3rd Workshop on Social Network Systems (SNS '10). ACM.

Holme, P. & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.

closeness-centrality temporal-social-network-analysis dynamic-betweenness-centrality dynamic-degree-centrality temporal-community-detection betweenness-centrality


Dynamic Community Detection

Dynamic Community Detection in Evolving Networks

DCD
temporal community detection
evolving community detection
dynamic graph clustering

2010 (key formalization); earlier work 2002–2009

Mucha, P. J. et al. (key formalization); earlier work by Girvan & Newman (2002)

Dynamic community detection identifies groups of densely connected nodes in networks that evolve over time, tracking how communities form, merge, split, and dissolve across temporal snapshots. Developed to extend static modularity optimization to time-varying structures, it is widely used in social, biological, and communication network research.

Time-stamped or snapshot network data (adjacency matrices across time)

Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., & Onnela, J.-P. (2010). Community structure in time-dependent, multiscale, and multiplex networks. Science, 328(5980), 876–878.

Fortunato, S., & Hric, D. (2016). Community detection in networks: A user guide. Physics Reports, 659, 1–44.

modularity-analysis temporal-community-detection multilayer-community-detection temporal-network-analysis exponential-random-graph-model stochastic-block-model


dynamic CIE
dynamic treatment evaluation
time-varying counterfactual analysis
longitudinal counterfactual evaluation

Robins (1986); Lechner (2009) for sequential treatment settings

Dynamic Counterfactual Impact Evaluation (dynamic CIE) extends standard counterfactual program evaluation to settings where treatment is assigned sequentially across multiple periods. Rather than comparing a single treated versus untreated state, it estimates the causal effect of entire treatment trajectories or regimes, accounting for how intermediate outcomes and time-varying covariates feed back into subsequent treatment decisions.

Panel data or longitudinal observational data with time-varying treatment

Robins, J. M. (1986). A new approach to causal inference in mortality studies with a sustained exposure period — application to control of the healthy worker survivor effect. Mathematical Modelling, 7(9-12), 1393-1512.

Lechner, M. (2009). Sequential causal models for the evaluation of labor market programs. Journal of Business and Economic Statistics, 27(1), 71-83.

counterfactual-impact-evaluation marginal-structural-model difference-in-differences panel-event-study dynamic-difference-in-differences propensity-score-weighting


Dynamic Degree Centrality

Dynamic Degree Centrality in Temporal Networks

time-varying degree centrality
temporal degree centrality
evolving degree centrality
DDC

Holme, P. & Saramaki, J.; Kim, H. & Anderson, R.

Dynamic degree centrality extends the classical degree centrality measure to networks that change over time. Rather than counting a node's connections in a single static snapshot, it tracks how many contacts each node maintains across successive time windows or contact events, producing a time-resolved importance profile for every actor in the network.

Time-stamped or interval-based edge/contact sequences

Holme, P. & Saramaki, J. (2012). Temporal networks. Physics Reports, 519(3), 97–125.

Kim, H. & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E, 85(2), 026107.

degree-centrality temporal-social-network-analysis dynamic-betweenness-centrality dynamic-community-detection weighted-degree-centrality temporal-network-analysis


Dynamic Difference-in-Differences

Dynamic DiD
Staggered DiD
Event-time DiD
Heterogeneous-timing DiD

Dynamic Difference-in-Differences extends the classic DiD framework to settings where units adopt treatment at different times. Rather than collapsing all variation into a single 2x2 comparison, it estimates group-time average treatment effects for each adoption cohort at each calendar period, then aggregates them into interpretable summaries of the causal effect over event time.

Panel data with staggered or time-varying treatment adoption

Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.

Sun, L., & Abraham, S. (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics, 225(2), 175-199.

difference-in-differences event-study-design panel-fixed-effects panel-data-difference-in-differences synthetic-control-method placebo-test


Dynamic Ego Network Analysis

Dynamic Ego Network Analysis (Longitudinal Personal Network Analysis)

longitudinal ego network analysis
temporal ego network analysis
personal network dynamics
dynamic personal network analysis

Burt, R. S.; Wellman, B. (foundational ego-net); dynamic extension developed across the 1990s–2010s

Dynamic ego network analysis examines how the personal network surrounding a focal individual (the ego) changes over time. By collecting the same ego-centered network data at multiple time points, researchers can track tie formation and dissolution, shifts in network composition, and changes in structural properties such as density, constraint, and network size — and link these dynamics to individual outcomes.

Repeated panel ego network surveys or longitudinal relational records

Burt, R. S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press.

Crossley, N., Bellotti, E., Edwards, G., Everett, M. G., Koskinen, J., & Tranmer, M. (2015). Social Network Analysis for Ego-Nets. SAGE Publications.

ego-network-analysis temporal-network-analysis social-network-analysis longitudinal-network-analysis dynamic-social-network-analysis multilayer-ego-network-analysis


Dynamic Eigenvector Centrality

Dynamic Eigenvector Centrality in Temporal Networks

temporal eigenvector centrality
time-varying eigenvector centrality
dynamic EC
evolving eigenvector centrality

Dynamic eigenvector centrality extends the classic eigenvector centrality measure to networks that change over time. Rather than computing a single leading eigenvector on a static adjacency matrix, it tracks how a node's influence — defined by the importance of its neighbours — evolves across snapshots or time windows. The method is used in social network analysis, epidemiology, and information diffusion studies where network topology shifts continuously.

Centrality measure for time-evolving networks

Temporal/dynamic network (edge lists with timestamps)

Lerman, K., Ghosh, R., & Kang, J. H. (2010). Centrality metric for dynamic networks. Proceedings of the 8th Workshop on Mining and Learning with Graphs (MLG '10). ACM.

eigenvector-centrality temporal-network-analysis dynamic-pagerank dynamic-betweenness-centrality temporal-community-detection multilayer-eigenvector-centrality


Dynamic Entropy Balancing

Dynamic Entropy Balancing for Longitudinal Causal Inference

entropy balancing with time-varying treatment

Hainmueller (2012) for static entropy balancing; extended to dynamic settings by Blackwell and Glynn (2018) and subsequent methodologists

Dynamic Entropy Balancing extends the entropy balancing reweighting approach to settings with time-varying treatments in panel or longitudinal data. It constructs unit weights at each time period such that the covariate distributions of treated and comparison units are balanced on specified moments, adjusting sequentially for prior treatment history and time-varying confounders to estimate the causal effect of treatment sequences on outcomes.

Panel data or time-series cross-sectional data with time-varying treatment

Hainmueller, J. (2012). Entropy Balancing for Causal Effects: A Multivariate Reweighting Method to Produce Balanced Samples in Observational Studies. Political Analysis, 20(1), 25-46.

Blackwell, M., & Glynn, A. N. (2018). How to Make Causal Inferences with Time-Series Cross-Sectional Data under Selection on Observables. American Political Science Review, 112(4), 1067-1082.

entropy-balancing inverse-probability-weighting marginal-structural-model propensity-score-weighting dynamic-inverse-probability-weighting dynamic-propensity-score-matching


Dynamic Event Study Design

Dynamic Event Study Design (Lead-Lag Specification)

dynamic DiD
lead-lag event study
relative-time event study
event-time regression

2021 (canonical treatment); practice since 1990s)

Sun & Abraham (2021); Callaway & Sant'Anna (2021) — building on earlier event-study traditions in finance and economics

The dynamic event study design extends the standard difference-in-differences framework by estimating treatment effects at each period before and after the event, rather than collapsing everything into a single post-treatment coefficient. By plotting lead and lag coefficients against relative event time, researchers can simultaneously test for pre-existing trends and trace how the causal effect evolves over multiple post-treatment periods.

Panel data with repeated observations over time, treatment timing variable

difference-in-differences event-study-design panel-event-study dynamic-difference-in-differences staggered-difference-in-differences panel-data-event-study-design