Bibliography and reading library
Drafted reading lists · generated from the sources
One page for every reading in the book. The first part is organized for use: each meeting’s anchor and companion, required corrections, and optional extensions, each with an official link and an access note. The last part is the formal reference list, generated from references.bib; every citation anywhere in the book links to its entry there. Readings the group adds during the quarter go in their own section. Systems and benchmarks are catalogued in prior art, because they are software and evaluations rather than readings.
Use official links rather than uploaded copies. Access notes distinguish a publisher record from an identified free full-text route; institutional entitlement is not guaranteed. This book distributes links and bibliographic metadata only, and no journal PDF is redistributed here.
The reading lists below are generated by tools/build_bibliography.py from references.bib and curriculum.json, and tools/validate.py fails if they are out of date. Edit the sources and rerun the script rather than editing the lists by hand.
By meeting
Meeting 1: Is there a scientific method?
Anchor.
Platt (1964). Strong Inference. Science. Publisher/DOI page; full text may require institutional access.
Companion.
Cleland (2001). Historical science, experimental science, and the scientific method. Geology. Publisher/DOI page; full text may require institutional access.
Optional extensions. The session page says in a sentence or two why each is there.
Chamberlin (1965). The Method of Multiple Working Hypotheses. Science. Publisher/DOI page; 1965 Science reprint of the 1890 essay. Full text may require institutional access.
Holmes (1987). Scientific Writing and Scientific Discovery. Isis. Publisher/DOI page; full text may require institutional access.
Gilbert (1896). The Origin of Hypotheses, Illustrated by the Discussion of a Topographic Problem. Science. Free to read at the publisher.
Johnson (1933). Role of Analysis in Scientific Investigation. Geological Society of America Bulletin. Publisher/DOI page; full text may require institutional access.
Frodeman (1995). Geological reasoning: Geology as an interpretive and historical science. Geological Society of America Bulletin. Publisher/DOI page; full text may require institutional access.
Kleinhans et al. (2005). Terra Incognita: Explanation and Reduction in Earth Science. International Studies in the Philosophy of Science. Open access at the publisher.
Elliott and Brook (2007). Revisiting Chamberlin: Multiple Working Hypotheses for the 21st Century. BioScience. Open access at the publisher.
Cleland (2002). Methodological and Epistemic Differences between Historical Science and Experimental Science. Philosophy of Science. Publisher/DOI page; full text may require institutional access.
Bacon (2004). Novum organum. The Oxford Francis Bacon, Vol. 11: The Instauratio magna Part II: Novum organum and Associated Texts. Critical edition (G. Rees & M. Wakely, Eds.), Oxford University Press; original work published 1620. Not open access; public-domain English translations exist.
Whewell (2014). The Philosophy of the Inductive Sciences, Founded upon Their History. Cambridge University Press. Cambridge Library Collection reprint; original work published 1840. Public-domain scans exist.
Mill (2011). A System of Logic, Ratiocinative and Inductive. Cambridge University Press. Cambridge Library Collection reprint; original work published 1843. Public-domain scans exist.
Peirce (1992). Deduction, Induction, and Hypothesis. The Essential Peirce: Selected Philosophical Writings, Volume 1 (1867–1893). Book chapter, Indiana University Press; original work published 1878 in Popular Science Monthly, 13, 470–482, which is public domain.
Gilbert (1886). The Inculcation of Scientific Method by Example, with an Illustration Drawn from the Quaternary Geology of Utah. American Journal of Science. Publisher/DOI page; public-domain text, scans exist.
Duhem (1954). The Aim and Structure of Physical Theory. Princeton University Press. Book; P. P. Wiener’s translation of the 1906 original. Not open access.
Popper (2005). The Logic of Scientific Discovery. Routledge. Routledge Classics edition; original work published 1934, English 1959. Not open access.
Hempel (1945). Studies in the Logic of Confirmation (I.). Mind. Publisher/DOI page; full text may require institutional access.
Quine (1951). Two Dogmas of Empiricism. The Philosophical Review. JSTOR; full text may require institutional access.
Hanson (1958). Patterns of Discovery: An Inquiry into the Conceptual Foundations of Science. Cambridge University Press. Book; no DOI. Not open access.
Harman (1965). The Inference to the Best Explanation. The Philosophical Review. JSTOR; full text may require institutional access.
Musgrave (1974). Logical versus Historical Theories of Confirmation. The British Journal for the Philosophy of Science. Publisher/DOI page; full text may require institutional access.
Mayo (1996). Error and the Growth of Experimental Knowledge. University of Chicago Press. Book; not open access.
Mayo and Spanos (2006). Severe Testing as a Basic Concept in a Neyman–Pearson Philosophy of Induction. The British Journal for the Philosophy of Science. Publisher/DOI page; full text may require institutional access.
Lipton (2004). Inference to the Best Explanation. Routledge. Book, 2nd edition; not open access.
Davis (2006). Strong Inference: Rationale or Inspiration?. Perspectives in Biology and Medicine. Open copy at eScholarship: https://escholarship.org/uc/item/88f9r3j5
Fudge (2014). Fifty Years of J. R. Platt’s Strong Inference. Journal of Experimental Biology. Publisher/DOI page; full text may require institutional access.
Douglas and Magnus (2013). State of the Field: Why Novel Prediction Matters. Studies in History and Philosophy of Science Part A. Open copy at PhilPapers: https://philpapers.org/archive/DOUSOT.pdf
Okasha (2016). Scientific Inference. Philosophy of Science: A Very Short Introduction. Book chapter, 2nd edition, Oxford University Press; not open access. The one-hour introduction to deduction, induction, and inference to the best explanation.
Lakatos (1970). Falsification and the Methodology of Scientific Research Programmes. Criticism and the Growth of Knowledge. Book chapter (I. Lakatos & A. Musgrave, Eds.), Cambridge University Press; full text may require institutional access.
Windschitl et al. (2008). Beyond the Scientific Method: Model-Based Inquiry as a New Paradigm of Preference for School Science Investigations. Science Education. Free to read at the publisher (bronze). University of Washington authors; the analysis of the school version of the scientific method and what it leaves out.
Meeting 2: Exploratory experimentation is not theory-free
Anchor.
Steinle (1997). Entering New Fields: Exploratory Uses of Experimentation. Philosophy of Science. Publisher/DOI page; full text may require institutional access.
Companion.
Karaca (2013a). The Strong and Weak Senses of Theory-Ladenness of Experimentation: Theory-Driven versus Exploratory Experiments in the History of High-Energy Particle Physics. Science in Context. Publisher/DOI page; full text may require institutional access.
Required correction with the companion.
Karaca (2013b). The Strong and Weak Senses of Theory-Ladenness of Experimentation: Theory-Driven versus Exploratory Experiments in the History of High-Energy Particle Physics – ERRATUM. Science in Context. Publisher/DOI record; institutional access may be needed.
Optional extensions. The session page says in a sentence or two why each is there.
Franklin (2005). Exploratory Experiments. Philosophy of Science. Publisher/DOI page; full text may require institutional access.
Colaço (2018). Rethinking the role of theory in exploratory experimentation. Biology & Philosophy. Publisher/DOI page; full text may require institutional access.
Klahr and Dunbar (1988). Dual Space Search During Scientific Reasoning. Cognitive Science. Publisher/DOI page; full text may require institutional access.
Meeting 3: From coastal traces to earthquake histories
Anchor.
Atwater (1987). Evidence for great Holocene earthquakes along the outer coast of Washington state. Science. USGS publication record with publisher link.
Companion.
Nelson et al. (1996). Identifying coseismic subsidence in tidal-wetland stratigraphic sequences at the Cascadia subduction zone of western North America. Journal of Geophysical Research: Solid Earth. Publisher/DOI page; full text may require institutional access.
Optional extensions. The session page says in a sentence or two why each is there.
Satake et al. (1996). Time and size of a giant earthquake in Cascadia inferred from Japanese tsunami records of January 1700. Nature. Publisher/DOI page; full text may require institutional access.
Yamaguchi et al. (1997). Tree-ring dating the 1700 Cascadia earthquake. Nature. Publisher/DOI page; full text may require institutional access.
Bond et al. (2007). What do you think this is? “Conceptual uncertainty” in geoscience interpretation. GSA Today. Publisher/DOI page; full text may require institutional access.
Polson and Curtis (2010). Dynamics of uncertainty in geological interpretation. Journal of the Geological Society. Open access at the publisher.
Baddeley et al. (2004). An introduction to prior information derived from probabilistic judgements: elicitation of knowledge, cognitive bias and herding. Geological Society, London, Special Publications. Publisher/DOI page; full text may require institutional access.
Rudwick (1985). The Great Devonian Controversy: The Shaping of Scientific Knowledge among Gentlemanly Specialists. University of Chicago Press. Book; not open access.
Currie (2018). Rock, Bone, and Ruin: An Optimist’s Guide to the Historical Sciences. MIT Press. Book; not open access.
Meeting 4: Plate tectonics: concept formation and a discriminating test
Anchor.
Wilson (1965). A New Class of Faults and their Bearing on Continental Drift. Nature. Publisher/DOI page; full text may require institutional access.
Companion.
Sykes (1967). Mechanism of earthquakes and nature of faulting on the mid-oceanic ridges. Journal of Geophysical Research. Publisher/DOI page; full text may require institutional access.
Optional extensions. The session page says in a sentence or two why each is there.
Vine and Matthews (1963). Magnetic Anomalies Over Oceanic Ridges. Nature. Publisher/DOI page; full text may require institutional access.
Oreskes (1988). The Rejection of Continental Drift. Historical Studies in the Physical and Biological Sciences. Publisher/DOI page; full text may require institutional access.
Meeting 5: New sensing capability versus trustworthy measurement
Anchor.
Lindsey et al. (2019). Illuminating seafloor faults and ocean dynamics with dark fiber distributed acoustic sensing. Science. Publisher/DOI page; full text may require institutional access.
Companion.
Lindsey et al. (2020). On the Broadband Instrument Response of Fiber-Optic DAS Arrays. Journal of Geophysical Research: Solid Earth. Publisher full-text page.
Optional extensions. The session page says in a sentence or two why each is there.
Mao et al. (2019). High Temporal Resolution Monitoring of Small Variations in Crustal Strain by Dense Seismic Arrays. Geophysical Research Letters. Author-hosted full text: https://www.seismo.helsinki.fi/greg/GRL_Mao_HighResolutionStrainDenseArrays_2019.pdf
Read Sections 2.2–2.3, 3.4, and 4.1–4.3, especially Figure 4. Use this case to examine the inference from ambient-noise velocity change to strain.
Kuhn (1961). The Function of Measurement in Modern Physical Science. Isis. Publisher/DOI page; full text may require institutional access.
Tal (2013). Old and New Problems in Philosophy of Measurement. Philosophy Compass. Publisher/DOI page; full text may require institutional access.
Chang (2004). Measurement, Justification, and Scientific Progress. Inventing Temperature. Publisher/DOI record; institutional access may be needed.
Chapter 5 of Inventing Temperature; optional measurement and progress framing.
Bogen and Woodward (1988). Saving the Phenomena. The Philosophical Review. Publisher/DOI record; institutional access may be needed.
Meeting 6: Explore freely; distinguish exploration from confirmation
Anchor.
Tukey (1962). The Future of Data Analysis. The Annals of Mathematical Statistics. Publisher/DOI page; full text may require institutional access.
Companion.
Nosek et al. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences. Free full text in PubMed Central.
Optional extensions. The session page says in a sentence or two why each is there.
Schorlemmer et al. (2018). The Collaboratory for the Study of Earthquake Predictability: Achievements and Priorities. Seismological Research Letters. Publisher/DOI page; full text may require institutional access.
Rzhetsky et al. (2015). Choosing experiments to accelerate collective discovery. Proceedings of the National Academy of Sciences. Publisher/DOI page; free full text in PubMed Central.
Leonelli (2014). What difference does quantity make? On the epistemology of Big Data in biology. Big Data & Society. Open access (SAGE, CC BY).
Bergen et al. (2019). Machine learning for data-driven discovery in solid Earth geoscience. Science. Publisher/DOI page; full text may require institutional access.
Ioannidis (2005). Why Most Published Research Findings Are False. PLoS Medicine. Open access (PLoS).
Breiman (2001). Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author). Statistical Science. Publisher/DOI record; institutional access may be needed.
Meeting 7: Field and marine research under real constraints
Anchor.
Powell (2007). The Rigours of an Arctic Experiment: The Precarious Authority of Field Practices in the Canadian High Arctic, 1958–1970. Environment and Planning A. Publisher/DOI page; full text may require institutional access.
Companion.
Becker et al. (2019). Fifty Years of Scientific Ocean Drilling. Oceanography. Free publisher article and PDF link.
Optional extensions. The session page says in a sentence or two why each is there.
Kuklick and Kohler (1996). Introduction. Osiris. Publisher/DOI page; full text may require institutional access.
Kastens et al. (2009). How Geoscientists Think and Learn. Eos, Transactions American Geophysical Union. Publisher/DOI page; full text may require institutional access.
Meeting 8: Serendipity, anomaly, and scientific interestingness
Anchor.
Moore (2025). The Serendipity of Discovery: Life of a Geochemist. Annual Review of Marine Science. Open-access publisher article; CC BY 4.0.
Companion.
Yaqub (2018). Serendipity: Towards a taxonomy and a theory. Research Policy. Open-access publisher article.
Optional extensions. The session page says in a sentence or two why each is there.
Kuhn (1962). Historical Structure of Scientific Discovery. Science. Publisher/DOI page; full text may require institutional access.
Fugelsang et al. (2004). Theory and data interactions of the scientific mind: Evidence from the molecular and the cognitive laboratory. Canadian Journal of Experimental Psychology. Publisher/DOI page; full text may require institutional access.
Copeland (2019). On serendipity in science: discovery at the intersection of chance and wisdom. Synthese. Publisher/DOI page; full text may require institutional access.
Rouet-Leduc et al. (2017). Machine Learning Predicts Laboratory Earthquakes. Geophysical Research Letters. Publisher/DOI page; full text may require institutional access.
Schmidhuber (2010). Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010). IEEE Transactions on Autonomous Mental Development. Publisher/DOI page; full text may require institutional access.
Baker (1999). Geosemiosis. Geological Society of America Bulletin. Publisher/DOI page; full text may require institutional access.
Meeting 9: Hypothesis testing when several models fit
Anchor.
Oreskes et al. (1994). Verification, Validation, and Confirmation of Numerical Models in the Earth Sciences. Science. Publisher/DOI page; full text may require institutional access.
Companion.
Beven and Freer (2001). Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology. Publisher/DOI page; full text may require institutional access.
Optional extensions. The session page says in a sentence or two why each is there.
Tarantola (2006). Popper, Bayes and the inverse problem. Nature Physics. Publisher/DOI page; full text may require institutional access.
Kirchner (2006). Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology. Water Resources Research. Publisher/DOI page; full text may require institutional access.
Bakun and Lindh (1985). The Parkfield, California, Earthquake Prediction Experiment. Science. Publisher/DOI page; full text may require institutional access.
Bakun et al. (2005). Implications for prediction and hazard assessment from the 2004 Parkfield earthquake. Nature. Publisher/DOI page; full text may require institutional access.
Bokulich and Oreskes (2017). Models in Geosciences. Springer Handbook of Model-Based Science. Book chapter; Publisher/DOI page; full text may require institutional access.
Schumm (1991). To Interpret the Earth: Ten Ways to Be Wrong. Cambridge University Press. Book; not open access. Open Library record; no DOI registered.
Geller (1997). Earthquake prediction: a critical review. Geophysical Journal International. Publisher/DOI page; full text may require institutional access.
Shmueli (2010). To Explain or to Predict?. Statistical Science. Publisher/DOI record; institutional access may be needed.
Mai et al. (2016). The Earthquake‐Source Inversion Validation (SIV) Project. Seismological Research Letters. Publisher/DOI record; institutional access may be needed.
Meeting 10: Scientific novelty: new to whom, compared with what?
Anchor.
Uzzi et al. (2013). Atypical Combinations and Scientific Impact. Science. Publisher/DOI page; full text may require institutional access.
Companion.
Fontana et al. (2020). New and atypical combinations: An assessment of novelty and interdisciplinarity. Research Policy. Publisher/DOI page; full text may require institutional access.
Optional extensions. The session page says in a sentence or two why each is there.
Boudreau et al. (2016). Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance, Novelty, and Resource Allocation in Science. Management Science. Publisher/DOI page; full text may require institutional access.
Wu et al. (2026). NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment. Findings of the Association for Computational Linguistics: ACL 2026. Open ACL Anthology proceedings with PDF and BibTeX.
Shibayama et al. (2021). Measuring novelty in science with word embedding. PLOS ONE. Open access; consult the linked 2026 correction to Table 4.
Machado (2026). Generative AI bias against scientific novelty: a cautionary tale from a small-sample evaluation of research proposals. Scientometrics. Publisher article; the experiment concerns one funder, year, and model configuration.
Si, Yang, et al. (2025). Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers. International Conference on Learning Representations. Open conference proceedings with paper PDF and review links; no DOI invented.
Sourati and Evans (2023). Accelerating science with human-aware artificial intelligence. Nature Human Behaviour. Publisher/DOI page; full text may require institutional access.
Tshitoyan et al. (2019). Unsupervised word embeddings capture latent knowledge from materials science literature. Nature. Publisher/DOI page; full text may require institutional access.
Foster et al. (2015). Tradition and Innovation in Scientists’ Research Strategies. American Sociological Review. Publisher/DOI page; full text may require institutional access.
Swanson (1986). Fish Oil, Raynaud’s Syndrome, and Undiscovered Public Knowledge. Perspectives in Biology and Medicine. Publisher/DOI page; full text may require institutional access.
Fortunato et al. (2018). Science of science. Science. Publisher/DOI record; institutional access may be needed.
Hofstra et al. (2020). The Diversity–Innovation Paradox in Science. Proceedings of the National Academy of Sciences. Free to read at the publisher; preprint at arXiv:1909.02063. Novelty as new concept links in 1.2 million US dissertation abstracts; uptake per link.
Meeting 11: Interdisciplinary integration: breadth, competence, and scientific advance
Anchor.
Nersessian (2022). Interdisciplinarity in the Making: Models and Methods in Frontier Science. MIT Press. Official publisher page links the open-access edition (CC BY-NC-ND).
Meeting 11 assigns the chapter 2 flow-loop case, section 2.1.1; the whole book is background.
Companion.
Shi and Evans (2023). Surprising combinations of research contents and contexts are related to impact and emerge with scientific outsiders from distant disciplines. Nature Communications. Open-access publisher full text.
Optional extensions. The session page says in a sentence or two why each is there.
Hardwig (1991). The Role of Trust in Knowledge. The Journal of Philosophy. Publisher/DOI page; full text may require institutional access.
Wright et al. (2025). What and Whose Knowledge? Measuring Epistemic Diversity in Large Language Models. arXiv preprint arXiv:2510.04226. Open preprint; not peer reviewed at the time of listing.
Rassenfosse et al. (2022). Scientific rewards for biomedical specialization are large and persistent. BMC Biology. Open access (CC BY).
Jones (2009). The Burden of Knowledge and the “Death of the Renaissance Man”: Is Innovation Getting Harder?. Review of Economic Studies. Publisher/DOI page; full text may require institutional access.
Wuchty et al. (2007). The Increasing Dominance of Teams in Production of Knowledge. Science. Publisher/DOI page; full text may require institutional access.
Stichweh (1992). The Sociology of Scientific Disciplines: On the Genesis and Stability of the Disciplinary Structure of Modern Science. Science in Context. Publisher/DOI page; full text may require institutional access.
Teodoridis (2018). Understanding Team Knowledge Production: The Interrelated Roles of Technology and Expertise. Management Science. Publisher/DOI page; full text may require institutional access.
Teodoridis et al. (2019). Creativity at the Knowledge Frontier: The Impact of Specialization in Fast- and Slow-paced Domains. Administrative Science Quarterly. Publisher/DOI page; full text may require institutional access.
Leahey et al. (2017). Prominent but Less Productive: The Impact of Interdisciplinarity on Scientists’ Research. Administrative Science Quarterly. Publisher/DOI page; full text may require institutional access.
Hao et al. (2026). Artificial intelligence tools expand scientists’ impact but contract science’s focus. Nature. Publisher/DOI page; full text may require institutional access.
Doshi and Hauser (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances. Open access (Science Advances).
Burke (2020). The Polymath: A Cultural History from Leonardo da Vinci to Susan Sontag. Yale University Press. Publisher and JSTOR records; not open access. Use library reserve procedures for any chapter scan.
Kitcher (1990). The Division of Cognitive Labor. The Journal of Philosophy. Publisher/DOI page; full text may require institutional access.
Yegros-Yegros et al. (2015). Does Interdisciplinary Research Lead to Higher Citation Impact? The Different Effect of Proximal and Distal Interdisciplinarity. PLOS ONE. Open access. Four WoS categories, 2005 articles; inverted-U relations between the three diversity dimensions and citations.
Wang et al. (2015). Interdisciplinarity and Impact: Distinct Effects of Variety, Balance, and Disparity. PLOS ONE. Open access. All WoS articles of 2001; variety, balance, and disparity have different signs and time dependence.
Liu et al. (2021). Understanding the Onset of Hot Streaks across Artistic, Cultural, and Scientific Careers. Nature Communications. Open access. Topic diversity of a career’s outputs from learned representations; hot streaks begin at the transition from exploration to exploitation.
Meeting 12: Assessing scientific advance: novelty, correctness, and impact
Anchor.
Wu et al. (2019). Large teams develop and small teams disrupt science and technology. Nature. Publisher/DOI page; full text may require institutional access.
Companion.
Petersen et al. (2025). The disruption index suffers from citation inflation: Re-analysis of temporal CD trend and relationship with team size reveal discrepancies. Journal of Informetrics. Open-access journal article; use the 2025 version of record.
Optional extensions. The session page says in a sentence or two why each is there.
Dellsén (2016). Scientific progress: Knowledge versus understanding. Studies in History and Philosophy of Science Part A. Publisher/DOI page; full text may require institutional access.
Azoulay et al. (2019). Does Science Advance One Funeral at a Time?. American Economic Review. Publisher offers complimentary full-text PDF.
Wang et al. (2017). Bias against novelty in science: A cautionary tale for users of bibliometric indicators. Research Policy. Publisher/DOI page; full text may require institutional access.
Park et al. (2023). Papers and patents are becoming less disruptive over time. Nature. Publisher/DOI page; full text may require institutional access.
Chu and Evans (2021). Slowed canonical progress in large fields of science. Proceedings of the National Academy of Sciences. Publisher/DOI page; free full text in PubMed Central.
Bloom et al. (2020). Are Ideas Getting Harder to Find?. American Economic Review. Publisher/DOI page; full text may require institutional access.
Azoulay et al. (2011). Incentives and creativity: evidence from the academic life sciences. The RAND Journal of Economics. Publisher/DOI record; institutional access may be needed.
Bird (2007). What Is Scientific Progress?. Noûs. Publisher/DOI record; institutional access may be needed.
Leibel and Bornmann (2024). What Do We Know About the Disruption Index in Scientometrics? An Overview of the Literature. Scientometrics. Open access at the publisher (hybrid). The review of the disruption index’s variants, biases, and validity; DI_5 shows higher convergent validity than DI_1.
Sinatra et al. (2016). Quantifying the Evolution of Individual Scientific Impact. Science. Open copy at the University of Copenhagen research portal. The random-impact rule and the Q model.
Meeting 13: What architecture follows from practice, and what would show it worked?
Anchor.
Boiko et al. (2023). Autonomous chemical research with large language models. Nature. Open-access publisher article.
Companion.
Chen et al. (2025). ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery. International Conference on Learning Representations. Open conference proceedings with paper PDF and review links; no DOI invented.
Optional extensions. The session page says in a sentence or two why each is there.
Messick (1995). Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist. Publisher/DOI page; full text may require institutional access.
Langley (1981). Data-Driven Discovery of Physical Laws. Cognitive Science. Publisher/DOI page; full text may require institutional access.
Kulkarni and Simon (1988). The Processes of Scientific Discovery: The Strategy of Experimentation. Cognitive Science. Publisher/DOI page; full text may require institutional access.
King et al. (2009). The Automation of Science. Science. Publisher/DOI page; full text may require institutional access.
Schmidt and Lipson (2009). Distilling Free-Form Natural Laws from Experimental Data. Science. Publisher/DOI page; full text may require institutional access.
Wang et al. (2023). Scientific discovery in the age of artificial intelligence. Nature. Publisher/DOI page; full text may require institutional access.
Kitano (2021). Nobel Turing Challenge: creating the engine for scientific discovery. npj Systems Biology and Applications. Open access (Nature Portfolio, CC BY).
Gottweis et al. (2026). Accelerating scientific discovery with Co-Scientist. Nature. Publisher/DOI page; full text may require institutional access.
Szymanski et al. (2023). An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature. Publisher/DOI page; full text may require institutional access.
Leeman et al. (2024). Challenges in High-Throughput Inorganic Materials Prediction and Autonomous Synthesis. PRX Energy. Open access (APS, CC BY).
Kapoor and Narayanan (2023). Leakage and the reproducibility crisis in machine-learning-based science. Patterns. Open access (Cell Press, CC BY).
Rainforth et al. (2024). Modern Bayesian Experimental Design. Statistical Science. Publisher/DOI page; full text may require institutional access.
Lehman and Stanley (2011). Abandoning Objectives: Evolution Through the Search for Novelty Alone. Evolutionary Computation. Publisher/DOI page; full text may require institutional access.
Gil et al. (2016). Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance. Earth and Space Science. Open access at the publisher.
Gil et al. (2018). Intelligent systems for geosciences: an essential research agenda. Communications of the ACM. Open access at the publisher.
Messeri and Crockett (2024). Artificial intelligence and illusions of understanding in scientific research. Nature. Publisher/DOI page; full text may require institutional access.
Flake and Fried (2020). Measurement Schmeasurement: Questionable Measurement Practices and How to Avoid Them. Advances in Methods and Practices in Psychological Science. Publisher/DOI page; full text may require institutional access.
Si, Hashimoto, et al. (2025). The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas. arXiv 2506.20803. Open preprint, version 1. Separate from the ideation study si2025.
Woollam et al. (2022). SeisBench—A Toolbox for Machine Learning in Seismology. Seismological Research Letters. Publisher/DOI record; institutional access may be needed.
Dekoninck et al. (2024). Evading Data Contamination Detection for Language Models is (too) Easy. arXiv 2402.02823. Open preprint; detector limitations, not a test of the models used by this group.
Simon (1973). Does Scientific Discovery Have a Logic?. Philosophy of Science. Publisher/DOI page; full text may require institutional access.
Cited by the glossary and the corpus study
Measurement literature cited by the glossary and the corpus study rather than by a meeting.
Kell and Oliver (2004). Here is the evidence, now what is the hypothesis? The complementary roles of inductive and hypothesis-driven science in the post-genomic era. BioEssays. Publisher/DOI page; full text may require institutional access.
Glass and Hall (2008). A Brief History of the Hypothesis. Cell. Free to read at the publisher.
Elliott et al. (2016). Conceptions of Good Science in Our Data-Rich World. BioScience. Open access at the publisher.
Funk and Owen-Smith (2017). A Dynamic Network Measure of Technological Change. Management Science. Publisher/DOI page; full text may require institutional access.
Wang et al. (2013). Quantifying Long-Term Scientific Impact. Science. Publisher/DOI page; a preprint is on arXiv (1306.3293).
Ke et al. (2015). Defining and identifying Sleeping Beauties in science. Proceedings of the National Academy of Sciences. Publisher/DOI page; free full text in PubMed Central.
Stirling (2007). A general framework for analysing diversity in science, technology and society. Journal of The Royal Society Interface. Publisher/DOI page; free full text in PubMed Central.
Wagner et al. (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics. Publisher/DOI page; full text may require institutional access.
Porter and Rafols (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics. Publisher/DOI page; full text may require institutional access.
Bornmann and Daniel (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation. Publisher/DOI page; an author copy is in the Max Planck repository.
Radicchi et al. (2008). Universality of citation distributions: Toward an objective measure of scientific impact. Proceedings of the National Academy of Sciences. Publisher/DOI page; free full text in PubMed Central.
Waltman (2016). A review of the literature on citation impact indicators. Journal of Informetrics. Publisher/DOI page; full text may require institutional access.
Cash et al. (2003). Knowledge systems for sustainable development. Proceedings of the National Academy of Sciences. Publisher/DOI page; free full text in PubMed Central.
Jordan et al. (2011). Operational earthquake forecasting: State of knowledge and guidelines for utilization. Annals of Geophysics. Open access at the publisher.
Stokes (1997). Pasteur’s Quadrant: Basic Science and Technological Innovation. Brookings Institution Press. Book; not open access. The source of the term use-inspired research.
Shapiro and Campillo (2004). Emergence of broadband Rayleigh waves from correlations of the ambient seismic noise. Geophysical Research Letters. Publisher/DOI page; full text may require institutional access.
Shapiro et al. (2005). High-Resolution Surface-Wave Tomography from Ambient Seismic Noise. Science. Publisher/DOI page; full text may require institutional access.
Swanson et al. (2025). The Virtual Lab of AI agents designs new SARS-CoV-2 nanobodies. Nature. Publisher/DOI page; full text may require institutional access.
Romera-Paredes et al. (2024). Mathematical discoveries from program search with large language models. Nature. Open access at the publisher.
M. Bran et al. (2024). Augmenting large language models with chemistry tools. Nature Machine Intelligence. Publisher/DOI page; full text may require institutional access.
Shao et al. (2024). Assisting in Writing Wikipedia-like Articles From Scratch with Large Language Models. Proceedings of NAACL 2024 (Long Papers). Open access (ACL Anthology).
Jansen et al. (2024). DiscoveryWorld: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents. Advances in Neural Information Processing Systems 37, Datasets and Benchmarks Track. Open conference proceedings.
Sozou et al. (2017). Computational Scientific Discovery. Springer Handbook of Model-Based Science. Book chapter; Publisher/DOI page; full text may require institutional access.
Darden (1991). Theory Change in Science: Strategies from Mendelian Genetics. Oxford University Press. Book; not open access.
Langley et al. (1987). Scientific Discovery: Computational Explorations of the Creative Processes. MIT Press. Book; not open access.
Thagard (1988). Computational Philosophy of Science. MIT Press. Book; not open access.
Klahr and Simon (1999). Studies of scientific discovery: Complementary approaches and convergent findings. Psychological Bulletin. Publisher/DOI page; full text may require institutional access.
Hacking (1992). ‘Style’ for historians and philosophers. Studies in History and Philosophy of Science Part A. Publisher/DOI record; institutional access may be needed.
Suggested by the group
Readings the group adds during the quarter, beyond the assigned pairs and the optional extensions. Anything here is optional. A suggestion is not an endorsement, and nothing in this section has been through the reading audit that the assigned pairs went through.
Add an entry by opening a pull request that does three things: append the record to references.bib, add the matching entry to the references object in curriculum.json, and add a bullet below citing the key, with one sentence on what the paper claims and one on which meeting it bears on. Verify the DOI first. See how to contribute for the mechanics.
None yet. The first entry sets the format for the rest.
- **@yourkey2026.** [Paper title](https://doi.org/10.0000/example). Journal or venue. Access note.
What it claims, and which meeting it bears on. *Suggested by Name, date.*If a suggestion turns out to be load-bearing, it can move into a session page as an optional extension, or displace an assigned paper in a later revision of the curriculum. That means adding its key to the meeting’s optional list in curriculum.json and citing it on the session page, because tools/validate.py checks that the two agree. Raise it as an issue first so the change is discussed before the syllabus moves.