Source Guide: Interdisciplinary Data Challenges in Scientific Research

Frame the question

The question centers on how scientific fields address data challenges, using the provided sources to compare methodologies, limitations, and future directions. Source [1] (EV research) and source [4] (genomics) both highlight data-intensive domains, while sources [2] (materials science) and [3] (finance) offer contrasting approaches to validation and interpretation.

What the evidence shows

Source [1] emphasizes the need for standardized protocols in EV research, noting challenges in nomenclature and functional studies. Its abstract states: 'Challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies remain hurdles to realising their potential in clinical applications.' Source [4] compares genomics to astronomy, YouTube, and Twitter, asserting that 'genomics is a four-headed beast' requiring advanced computational solutions. Source [2] contrasts with EV research by focusing on machine learning's role in materials science, where 'active learning and surrogate-based optimization' are used to improve design processes. Source [3] introduces a statistical framework for financial studies, arguing that 'most claimed research findings are likely false' due to data mining. These sources collectively demonstrate how data challenges vary across disciplines, from EV metrology to financial validation.

Follow the source trail

The anchor source [1] establishes EV research as a data-intensive field requiring standardized methodologies. Source [2] contrasts with EV research by focusing on machine learning's role in materials science, where 'active learning and surrogate-based optimization' are used to improve design processes. Source [3] introduces a statistical framework for financial studies, arguing that 'most claimed research findings are likely false' due to data mining. Source [4] parallels EV research's data demands by framing genomics as a 'four-headed beast' requiring advanced computational solutions. These sources collectively demonstrate how data challenges vary across disciplines, from EV metrology to financial validation.

Use these sources well

Students can use source [1] to discuss EV research's methodological challenges, citing its emphasis on 'production, separation, and characterisation' of EVs. Source [2] provides a framework for analyzing machine learning applications in materials science, while source [3] offers a statistical critique of financial studies. Source [4] can be paired with [1] to compare data infrastructure needs in EV research and genomics. Avoid overstating correlations; for example, while both [1] and [4] address data volume, their solutions (standardization vs. computational scaling) are distinct. Use source [2] to contrast with [1] by highlighting machine learning's role in predictive modeling versus EV functional studies.

What to search next

Further research could explore: (1) How machine learning techniques in [2] might address EV separation challenges in [1]; (2) The feasibility of applying [3]’s statistical framework to validate EV biomarker studies; (3) Comparative data storage requirements between genomics (source [4]) and EV research (source [1]); (4) The role of active learning (source [2]) in optimizing EV functional studies. These questions would require cross-disciplinary analysis and additional primary sources.

Verbatim source abstracts

[1] Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches — Journal of Extracellular Vesicles, 2024-02-01, doi:10.1002/jev2.12404

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly. [1]

[2] Recent advances and applications of machine learning in solid-state materials science — npj Computational Materials, 2019-08-08, doi:10.1038/s41524-019-0221-0

Abstract One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science. [2]

[3] … and the Cross-Section of Expected Returns — Review of Financial Studies, 2015-10-09, doi:10.1093/rfs/hhv059

Hundreds of papers and factors attempt to explain the cross-section of expected returns. Given this extensive data mining, it does not make sense to use the usual criteria for establishing significance. Which hurdle should be used for current research? Our paper introduces a new multiple testing framework and provides historical cutoffs from the first empirical tests in 1967 to today. A new factor needs to clear a much higher hurdle, with a t-statistic greater than 3.0. We argue that most claimed research findings in financial economics are likely false. (JEL C12, C52, G12) [3]

[4] Big Data: Astronomical or Genomical? — PLOS Biology, 2015-07-07, doi:10.1371/journal.pbio.1002195

Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a "four-headed beast"--it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the "genomical" challenges of the next decade. [4]

Source dossiers

Reference cards for every cited source, using only verified record metadata.

[1] Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

• Authors: Joshua A Welsh, Deborah C. I. Goberdhan, Lorraine O’Driscoll, Edit I. Buzás, Cherie Blenkiron, Benedetta Bussolati, Houjian Cai, Dolores Di Vizio, Tom A. P. Driedonks, Uta Erdbrügger, Juan Manuel Falcón‐Pérez, Qing‐Ling Fu, Andrew F. Hill, Metka Lenassi, Sai Kiang Lim, Mỹ G. Mahoney, Sujata Mohanty, Andreas Möller, Rienk Nieuwland, Takahiro Ochiya, Susmita Sahoo, Ana Cláudia Torrecilhas, Lei Zheng, Andries Zijlstra, Sarah Abuelreich, Reem Bagabas, Paolo Bergese, Esther Bridges, Marco Brucale, Dylan Burger, Randy P. Carney, Emanuele Cocucci, Rossella Crescitelli, Edveena Hanser, Adrian L. Harris, Norman J. Haughey, An Hendrix, Alexander R. Ivanov, Tijana Jovanović‐Talisman, Nicole A. Kruh‐Garcia, Vroniqa Ku'ulei‐Lyn Faustino, Diego Kyburz, Cecilia Lässer, Kathleen M. Lennon, Jan Lötvall, Adam L. Maddox, Elena S. Martens‐Uzunova, Rachel R. Mizenko, Lauren A. Newman, Andrea Ridolfi, Eva Rohde, Tatu Rojalin, Andrew Rowland, András Saftics, Ursula S. Sandau, Julie A. Saugstad, Faezeh Shekari, Simon Swift, Dmitry Ter‐Ovanesyan, Juan Pablo Tosar, Zivile Useckaite, Francesco Valle, Zoltán Varga, Edwin van der Pol, Martijn J. C. van Herwijnen, Marca H. M. Wauben, Ann M. Wehman, S Williams, Andrea Zendrini, Alan Zimmerman, Clotilde Théry, Kenneth W. Witwer
• Venue: Journal of Extracellular Vesicles
• Published: 2024-02-01
• DOI: 10.1002/jev2.12404
• Citation count: 4,087
• Institutions: National Institutes of Health; College Board; PHV Dialysezentrum; Craft Engineering Associates (United States); National Cancer Institute; John Radcliffe Hospital; University of Oxford; Trinity College Dublin
• Topics: Extracellular vesicles in disease, MicroRNA in disease regulation, Cell Adhesion Molecules Research, Extracellular vesicles, Extracellular vesicle, Computer science, Nanotechnology, Snapshot (computer storage), Synthetic biology, Computational biology
• License/access: cc-by (open access)
• Record: https://doi.org/10.1002/jev2.12404
• Abstract (verbatim): "Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly." [1]

[2] Recent advances and applications of machine learning in solid-state materials science

• Authors: Jonathan Schmidt, Mário R. G. Marques, Silvana Botti, Miguel A. L. Marques
• Venue: npj Computational Materials
• Published: 2019-08-08
• DOI: 10.1038/s41524-019-0221-0
• Citation count: 2,496
• Institutions: Martin Luther University Halle-Wittenberg; Friedrich Schiller University Jena
• Topics: Machine Learning in Materials Science, X-ray Diffraction in Crystallography, Computational Drug Discovery Methods, Interpretability, Toolbox, Machine learning, Artificial intelligence, Computer science, Process (computing), Property (philosophy)
• License/access: cc-by (open access)
• Record: https://doi.org/10.1038/s41524-019-0221-0
• Abstract (verbatim): "Abstract One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science." [2]

[3] … and the Cross-Section of Expected Returns

• Authors: Campbell R. Harvey, Yan Liu, Caroline Zhu
• Venue: Review of Financial Studies
• Published: 2015-10-09
• DOI: 10.1093/rfs/hhv059
• Citation count: 2,116
• Institutions: National Bureau of Economic Research; Mitchell Institute; Texas A&M University; University of Oklahoma
• Topics: Financial Markets and Investment Strategies, Stochastic processes and financial applications, Financial Risk and Volatility Modeling, Section (typography), Cross section (physics), Economics, Physics, Business
• License/access: open access — license unspecified (open access)
• Record: https://doi.org/10.1093/rfs/hhv059
• Abstract (verbatim): "Hundreds of papers and factors attempt to explain the cross-section of expected returns. Given this extensive data mining, it does not make sense to use the usual criteria for establishing significance. Which hurdle should be used for current research? Our paper introduces a new multiple testing framework and provides historical cutoffs from the first empirical tests in 1967 to today. A new factor needs to clear a much higher hurdle, with a t-statistic greater than 3.0. We argue that most claimed research findings in financial economics are likely false. (JEL C12, C52, G12)" [3]

[4] Big Data: Astronomical or Genomical?

• Authors: Zachary Stephens, Skylar Y. Lee, Faraz Faghri, Roy H. Campbell, ChengXiang Zhai, Miles Efron, Ravishankar K. Iyer, Michael C. Schatz, Saurabh Sinha, Gene E. Robinson
• Venue: PLOS Biology
• Published: 2015-07-07
• DOI: 10.1371/journal.pbio.1002195
• Citation count: 1,433
• Institutions: University of Illinois Urbana-Champaign; Cold Spring Harbor Laboratory
• Topics: Genetics, Bioinformatics, and Biomedical Research, Genetic Associations and Epidemiology, Genomics and Phylogenetic Studies, Big data, Genomics, Data science, Biology, Computational genomics, Computer science, Genome
• License/access: cc-by (open access)
• Record: https://doi.org/10.1371/journal.pbio.1002195
• Abstract (verbatim): "Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a "four-headed beast"--it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the "genomical" challenges of the next decade." [4]