Relevant bibliographies by topics / Discovery tools

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Discovery tools'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Discovery tools"

1

Riley, Tracy L. "Tools for Discovery." Gifted Child Today 20, no. 1 (1997): 30–50. http://dx.doi.org/10.1177/107621759702000108.

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James, Dean, Michael Garrett, and Leah Krevit. "Discovering discovery tools." Library Hi Tech 27, no. 2 (2009): 268–76. http://dx.doi.org/10.1108/07378830910968218.

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Lemberger, Thomas. "Tools of discovery." Molecular Systems Biology 10, no. 1 (2014): 715. http://dx.doi.org/10.1002/msb.201305048.

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Kovács, Zoltán, Tomás Recio, and M. Pilar Vélez. "Automated reasoning tools in GeoGebra discovery." ACM Communications in Computer Algebra 55, no. 2 (2021): 39–43. http://dx.doi.org/10.1145/3493492.3493495.

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We present some current achievements in the software package GeoGebra Discovery that provides several symbolic tools and commands to mechanically discover (and verify symbolically) relationships on planar geometry constructions. Our presentation includes the novel Discover tool and command, the Relation tool and command, and the Compare command. Our proposal successfully makes the cycle 'conjecturing-checking-proving' in elementary geometry even more accessible for general users, focusing not only on educational uses but research as well.
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Sinha, Gunjan. "UK boosts discovery tools." Nature Biotechnology 32, no. 2 (2014): 119. http://dx.doi.org/10.1038/nbt0214-119b.

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MMCCOY, MICHAEL. "DRUG DISCOVERY TOOLS DEBUT." Chemical & Engineering News Archive 80, no. 32 (2002): 8. http://dx.doi.org/10.1021/cen-v080n032.p008.

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Kaur, Navneet, Mymoona Akhter, and Chhavi Singla. "Drug designing: Lifeline for the drug discovery and development process." Research Journal of Chemistry and Environment 26, no. 8 (2022): 173–79. http://dx.doi.org/10.25303/2608rjce1730179.

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Drug discovery and development field has entered into a revolutionary phase with the introduction of Computer Aided Drug Designing (CADD) tools in the designing and development of new drugs. Traditional drug discovery and designing is a tedious, expensive and time-consuming process. Pharmaceutical industries spend billions of dollars to launch a potential drug candidate into the drug market. It takes 15-20 years of research to discover a new drug candidate. The advancements in the Computer Aided Drug Designing techniques have significantly contributed towards lowering the cost and time involve
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Huda Nasution, Afwanil, Edy Surya, and Mariani Mariani. "Development of Discovery Learning Tools." American Journal of Educational Research 8, no. 12 (2020): 938–45. http://dx.doi.org/10.12691/education-8-12-7.

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Zhang, Ru, and Xin Xie. "Tools for GPCR drug discovery." Acta Pharmacologica Sinica 33, no. 3 (2012): 372–84. http://dx.doi.org/10.1038/aps.2011.173.

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Valdés-Pérez, Raúl E. "Discovery tools for science apps." Communications of the ACM 42, no. 11 (1999): 37–41. http://dx.doi.org/10.1145/319382.319389.

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Dissertations / Theses on the topic "Discovery tools"

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Howard, Craig M. "Tools and techniques for knowledge discovery." Thesis, University of East Anglia, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368357.

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Martins, Tânia Cristina Soares. "Exosomes as tools for biomarker discovery." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/21996.

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Mestrado em Biomedicina Molecular<br>Os exossomas são pequenas vesículas extracelulares envolvidas em vários processos fisiológicos e patológicos. O potencial dos exosomas como fontes de biomarcadores para o diagnóstico, prognóstico e mesmo para a terapêutica tem intensificado a investigação nesta área, apoiando o potencial dos exosomas na descoberta de biomarcadores. A centrifugação diferencial é o método mais usado, mas é demorado, requer grandes volumes de amostra e as altas velocidades de centrifugação podem comprometer a integridade dos exossomas. Apesar das várias opções disponíve
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Corbeil, Christopher. "New virtual screening tools for molecular discovery." Thesis, McGill University, 2009. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=40786.

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In the field of molecular discovery, virtually screening large libraries of compounds proved to be often more cost-efficient than the traditional experimental approaches. In fact, it has now become common practice thanks to the virtual screening tools available to chemists in the pharmaceutical industry, specifically docking. Most docking programs do not account for the dynamics associated with protein-ligand binding whether it is protein flexibility or the inclusion of displaceable water molecules. FITTED1.0 was developed to include these specific two features and has been validated on a test
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Cereto, Massagué Adrià. "Development of tools for in silico drug discovery." Doctoral thesis, Universitat Rovira i Virgili, 2017. http://hdl.handle.net/10803/454678.

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El cribratge virtual és un mètode quimioinformàtic que consisteix en cribrar molècules bioactives de grans bases de dades de molècules petites. Això permet als investigadors d’estalviar-se el cost de provar experimentalment cents o milers de compostos candidats, reduïnt-ne el nombre fins a quantitats manejables. Per a la validació dels mètodes de cribratge virtual calen biblioteques de molècules cimbell. El programari DecoyFinder fou desenvolupat com a aplicació gràfica de fàcil ús per a la construcció de biblioteques de molècules cimbell, i fou posteriorment ampliat amb les troballes de recer
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Jones, Neil Christopher. "Computational tools for high-throughput discovery in biology." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3267820.

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Thesis (Ph. D.)--University of California, San Diego, 2007.<br>Title from first page of PDF file (viewed August 7, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 115-127).
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Islam, R. S. "Novel engineering tools to aid drug discovery processes." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1444794/.

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A major bottleneck in drug discovery is the production of soluble human recombinant protein for functional, biochemical and structural analyses. The level of recombinant protein expression is controlled by a complex relationship between both biological and engineering variables. Due to the inter-play between these variables and standard experimental methods, the identification of the key variables which lead to improved protein expression can sometimes be missed. This thesis presents a framework which underpins the generation of large quantities of soluble recombinant protein in E. coli in a r
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DePasquale, Erica. "Development of Computational Tools for Single-Cell Discovery." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1614021318421845.

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Jenkins, Michael Joseph. "Decisional tools for cost-effective bioprocess design for cell therapies and patient-specific drug discovery tools." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10046409/.

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A specific challenge to the translation of cell therapies and stem-cell derived products is the ability to develop and manufacture such products in a cost-effective, scalable and robust manner. To this end, this thesis investigates the creation and application of a set of computational tools designed to aid bioprocess design decisions for cell therapy and stem-cell derived research products. The decision-support tools comprise advanced bioprocess economics models with databases tailored to cellular products. These are linked to Monte Carlo simulation for uncertainty analysis and techniques to
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Hesping, Eva M. "New inhibitors and tools to advance HDAC drug discovery for malaria." Thesis, Griffith University, 2021. http://hdl.handle.net/10072/403646.

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Malaria is a leading cause of morbidity and mortality, causing more than 400,000 deaths per year. Malaria is caused by parasites of the Plasmodium genus with most deaths due to P. falciparum infection. The control of malaria is complicated by the lack of a widely effective vaccine, the spread of mosquito resistance to insecticides and Plasmodium parasite resistance to available drugs, including the gold standard artemisinin-combination therapies. Thus, there is an urgent requirement for the development of new antimalarials, in particular those with different modes of action to existing drugs t
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Carrascosa, Baena María Carmen 1972. "Next generation of informatics tools for big data analytics in drug discovery." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/586011.

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El paradigma clàssic on un medicament interacciona amb un únic target biològic vinculat a una malaltia es posa en dubte. Actualment es reconeix que un medicament interacciona amb múltiples targets biològics i que aquests targets estan involucrats en multitud de pathways i que s’expressen en una varietat d’òrgans. Amb el creixent reconeixement d’aquesta complexitat, la estratègia reduccionista del procés de descoberta de nous medicaments ha evolucionat cap a estratègies sistèmiques multinivell. Gràcies als avenços tecnològics, hi ha hagut un gran increment de les dades generades en les diverse
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Books on the topic "Discovery tools"

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Roessner, Ute, and Daniel Anthony Dias, eds. Metabolomics Tools for Natural Product Discovery. Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-577-4.

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Yu, Bing, and Marcus Hinchcliffe, eds. In Silico Tools for Gene Discovery. Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-176-5.

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Saxena, Anil Kumar, ed. Biophysical and Computational Tools in Drug Discovery. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85281-8.

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R, Kiesel Paul, ed. Matthew Bender practice guide.: Planning, privileges, tools. LexisNexis/Matthew Bender, 2003.

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Jürgen, Bajorath, ed. Chemoinformatics: Concepts, methods, and tools for drug discovery. Humana Press, 2004.

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Rubenstein, Ken. Drug targets from genomics: Evolving tools for discovery. D&MD Publications, 2005.

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Lederman, Leon M. From quarks to the cosmos: Tools of discovery. Scientific American Library, 1989.

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Das Talukdar, Anupam, Jayanta Kumar Patra, Gitishree Das, and Deepa Nath, eds. Traditional Resources and Tools for Modern Drug Discovery. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4600-2.

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Roessner, Ute, and Daniel Anthony Dias. Metabolomics tools for natural product discovery: Methods and protocols. Humana Press, 2013.

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L, Wang Jason T., Shapiro Bruce A, and Shasha Dennis Elliott, eds. Pattern discovery in biomolecular data: Tools, techniques, and applications. Oxford University, 1999.

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Book chapters on the topic "Discovery tools"

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Kangueane, Pandjassarame. "Tools and Techniques." In Bioinformation Discovery. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95327-4_3.

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Kangueane, Pandjassarame. "Tools and Techniques." In Bioinformation Discovery. Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0519-2_3.

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Schneider, Thomas. "After the Discovery." In Tools of Effective Compliance. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66748-4_18.

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Peseschkian, Nossrat. "Self-Discovery." In Oriental Stories as Tools in Psychotherapy. Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-70678-3_11.

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Asano, Yasuhisa. "Tools for Enzyme Discovery." In Manual of Industrial Microbiology and Biotechnology. ASM Press, 2014. http://dx.doi.org/10.1128/9781555816827.ch30.

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Liebler, Daniel C. "Bioinformatics Tools for Proteomics." In Proteomics for Biological Discovery. John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/0470007745.ch15.

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Cronin, Mark T. D. "Chapter 2. In Silico Tools for Toxicity Prediction." In Drug Discovery. Royal Society of Chemistry, 2011. http://dx.doi.org/10.1039/9781849733045-00009.

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Riley, Jo. "Tools and equipment of discovery." In Teaching Drama With, Without and About Gender. Routledge, 2021. http://dx.doi.org/10.4324/9781003080800-2.

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Shneiderman, Ben. "Inventing Discovery Tools: Combining Information Visualization with Data Mining." In Discovery Science. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45650-3_4.

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Hasan, Ahmad, Kia Teymourian, and Adrian Paschke. "Probabilistic Event Pattern Discovery." In Rule Technologies: Foundations, Tools, and Applications. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21542-6_16.

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Conference papers on the topic "Discovery tools"

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Gawande, Janhvi, and Manjusha Bhange. "Artificial Intelligence in Tools Pharamaceutical Research and Healthcare Drug Discovery." In 2024 International Conference on Healthcare Innovations, Software and Engineering Technologies (HISET). IEEE, 2024. http://dx.doi.org/10.1109/hiset61796.2024.00092.

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Hayashi, Kengo, and Ryuta Arisaka. "Semi-Automatic Discovery of Dependency Relationships Among Properties for Ranking-Based Semantics." In 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2024. https://doi.org/10.1109/ictai62512.2024.00101.

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Tanay, Mogalluru Chidhvilas, Rahul Rathnam, Pamba Vamshi Krishna, Mrinal Devnath, and Narinder Singh Punn. "Enhanced QSAR Modeling for Drug Discovery: Leveraging Advanced Computational Tools and Techniques." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752307.

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Seifert, Mike, Sébastien Chapleau, and Martin Couture. "Dynamics Fem Correlation Using Structural Optimization Tools." In Vertical Flight Society 70th Annual Forum & Technology Display. The Vertical Flight Society, 2014. http://dx.doi.org/10.4050/f-0070-2014-9492.

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Dynamics finite element (FE) models are the principal tools for prediction of rotorcraft airframe oscillatory loads and vibrations. The accuracy and reliability of rotorcraft system-level dynamics FE models is established through correlation with ground vibration test (GVT) data. The traditional method of correlation involves manual iteration that relies heavily on the knowledge, intuition, and persistence of experienced engineers. A new, more efficient FEM correlation process has been developed to adapt to the increased pace of rotorcraft development programs. The process uses sensitivity-bas
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Pullwer, Björn, and Anke Dittmar. "Prototypes as Tools for Discovery." In ECCE '15: European Conference on Cognitive Ergonomics 2015. ACM, 2015. http://dx.doi.org/10.1145/2788412.2788422.

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Tin Kam Ho. "Interactive tools for pattern discovery." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1334282.

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Rehm, Dieter. "DATABASES: INDISPENSABLE TOOLS FOR RESEARCH AND DEVELOPMENT." In Data For Discovery. Begellhouse, 2023. http://dx.doi.org/10.1615/1-56700-002-9.370.

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Pérez, Laura Icela González, María Soledad Ramírez Montoya, and Francisco J. García-Peñalvo. "Discovery tools for open access repositories." In TEEM'16: 4th International Conference on Technological Ecosystems for Enhancing Multiculturality. ACM, 2016. http://dx.doi.org/10.1145/3012430.3012532.

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Zhang, Sheng, Ryan Jacobs, Sayan Ghosh, Ambarish Kulkarni, and Liping Wang. "Automated Data-Driven Physics Discovery of Turbine Component Damage." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-83372.

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Abstract We propose an automated physics discovery algorithm for turbine component damage modeling. Our algorithm utilizes operational data of a mechanical component and discovers an interpretable symbolic formula that describes the physics. We illustrate our algorithm through two numerical examples and demonstrate that the discovered formulas can predict the future damage accurately. Our framework is flexible and easily applicable to all areas of science and engineering. With cutting-edge machine learning tools, researchers can simply input the experimental data and then the physics formulas
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Potena, Domenico, and Claudia Diamantini. "Description of Knowledge Discovery Tools in KDTML." In Signal Processing with Special Track on Biomedical Engineering (CCSP). IEEE, 2005. http://dx.doi.org/10.1109/ccsp.2005.4977177.

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Reports on the topic "Discovery tools"

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Rudin, Sven Peter. Computational Tools for Materials Discovery/Design. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1545730.

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Massey, Joe, Neil Majithia, and Elena Simperl. Generative AI tools for data discovery and use. Open Data Institute, 2024. https://doi.org/10.61557/odzd4015.

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Simakov, Evgenya. Accelerators as tools for discovery, innovation, and everyday life [Slides]. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1807810.

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Belkacem, Ali, Cynthia Friend, Yimei Zhu, et al. Basic Research Needs for Innovation and Discovery of Transformative Experimental Tools. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1616508.

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Kumar, Vipin, Yongdae Kim, Jaideep Srivastava, et al. Situational Awareness Analysis Tools for Aiding Discovery of Security Events and Patterns. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada439552.

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Patton, Robert M., Christopher T. Symons, Bryan L. Gorman, and Jim N. Treadwell. Knowledge Discovery, Knowledge Management and Enterprise-Wide Information Technology Tools Final Report. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1042913.

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Leger, Dorian. Pre-registration: Landscape Review of AI-Powered Research Tools for Literature Review and Discovery. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.e7ni6r9s.

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Sunil, Smrithi. A Brief History of GFP. Asimov Press, 2025. https://doi.org/10.62211/57yu-31kp.

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Lawrence P. Wackett and Lynda B.M. Ellis. Functional Analysis and Discovery of Microbial Genes Transforming Metallic and Organic Pollutants: Database and Experimental Tools. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/834986.

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Lawley, C. J. M., and B A Kjarsgaard. Bottom-up mineral exploration: ore-element upgrading in the upper mantle and tools for its discovery. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2020. http://dx.doi.org/10.4095/323674.

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