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Pages 111-135

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From page 111...
... APPENDIX A 111 March 17, 2020 (Tuesday) 8:30 am EDT/1:30 pm CET/5:30 am PDT  ART THREE: HUMAN P AND POLICY ISSUES Welcome and Overview of Day Two -- Daniel Atkins 8:45 am EDT/1:45 pm CET/5:45 am PDT Accelerating Discovery: Standards, Governance, and Social Context Session Leaders: Lara Mangravite, Sage Bionetworks, and Rebecca Lawrence, F1000 Research Panelists: Raja Mazumder, George Washington University Beth Plale, National Science Foundation Timothy Gardner, Riffyn, Inc.
From page 113...
... She is the founding director of the Workflows for Data Science (WorDS) Center of Excellence and the WIFIRE Lab.
From page 114...
... Since joining SDSC in 2001, she has been a principal investigator and a technical leader in a wide range of cross-disciplinary projects. With a specialty in scientific workflows, she leads collaborative teams to deliver impactful results through making computational data science work more reusable, programmable, scalable, and reproducible.
From page 115...
... Jamison and Betty Williams Professor of Engineering at the University of Michigan. His research is on data science and developing theory and algorithms for data collection, analysis, and visualization that use statistical machine learning and distributed optimization.
From page 116...
... He is also a professor of biostatistics, a professor of computer science, and is affiliated faculty in the Center for Biomedical Ethics and Society. He co-directs the Health Data Science Center, the Center for Genetic Privacy and Identity in Community Settings -- a National Institutes of Health Center of Excellence in Ethical, Legal, and Social Implications Research, and the Big Biomedical Data Science Ph.D.
From page 117...
... His research is focused on understanding atmosphere dynamics on Earth and other planets; turbulence in atmosphere and oceans; and climate change and climate modeling. Previously, Dr.
From page 119...
... The concept of translational research -- meaning the conversion of basic knowledge into products or processes that meet critical real-world needs -- emerged several decades ago in the biomedical domain. Computational scientists have begun to conceive of priorities within their own discipline, including workflow management systems, as examples of translational research (Deelman et al., 2020)
From page 120...
... Building an advanced computational environment for wildfire monitoring and behavior prediction requires integrating numerous functions, such as collecting various types of data and performing multiple, complex modeling tasks. In particle physics, development of computational tools that allowed for collaborative statistical modeling, in addition to workflow management and computational tools, was critical to confirming the existence of the Higgs boson.
From page 121...
... In both experimental and observational fields, including materials research and astronomy, FAIR data are needed to develop and train ML algorithms, which in turn enable the development of closed loop systems in which the selection of experiments or instrument targeting can be automated. In some fields, such as particle physics, there is considerable experience with collecting and processing large amounts of data, but new approaches to instrument design and data are needed to allow for simulation-based inference based on reuse of data.
From page 122...
... . In addition, funders could increase support for leading domain repositories, for the creation of new repositories, and for the broader data ecosystem.
From page 123...
... Many domain repositories are poorly or inconsistently funded and thus are forced to spend significant staff time on fundraising that could be spent on data services. Support is also needed for related organizations that provide important infrastructure for the data ecosystem, such as Crossref, Datacite, the Research Data Alliance (RDA)
From page 124...
... For example, there has been considerable progress in community efforts to develop standards in areas such as registries (Dockstore, an app store for bioinformatics, 4; WorkflowHub, 5 a registry for describing, sharing, and publishing scientific computational workflows) , services for monitoring and testing (LifeMonitor, 6 OpenEBench 7)
From page 125...
... federal government providing shared resources that have allowed research communities to harness information technologies to significantly advance their work. Examples include the establishment of national supercomputer centers in the 1980s by NSF in partnership with academic institutions, the development of GenBank and other digital data resources in the life sciences by NIH and National Library of Medicine starting in the 1980s, and NSF's advanced cyberinfrastructure program launched in the 125 PREPUBLICATION COPY -- Uncorrected Proofs
From page 126...
... . As discussed in Chapter 2, it is inherently more difficult to fund development and maintenance of production-quality software (workflow engines, automated tools, etc.)
From page 127...
... Given the significant investments that governments and other research funders are making in data-driven science, it makes sense to leverage these investments across borders and domains to the extent possible. Goals of enhanced international collaboration would include 127 PREPUBLICATION COPY -- Uncorrected Proofs
From page 128...
... . RDA was started in 2013 and aims to build "the social and technical infrastructure to enable open sharing and re-use of data." 12 The Research Data Framework 13 was initiated by the National Institute of Standards and Technology in 2019 and is aimed at increasing the supply of trustworthy research data across domains by developing a "strategy for various roles in the research data management ecosystem." Additionally, the FAIR for Research Software Working Group is convened as an RDA Working Group, FORCE11 Working Group, and Research Software Alliance Task Force.
From page 129...
... . At the same time as a privacy-aware public shares personal information in unprecedented ways through social media and other avenues, many also express reservations about its use in research.
From page 130...
... Even areas of research that do not directly work with personal data must consider privacy issues. The goal of making the workflow itself transparent strengthens reproducibility but could impinge on privacy under certain circumstances, for example, by revealing personal information about specific researchers.
From page 131...
... Initiatives have been launched in recent years under the rubric of "responsible AI" and "responsible ML." For example, Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) is a series of workshops aimed at exploring the challenges raised by ML "for ensuring non-discrimination, due process, and understandability in decisionmaking." 14 Organizations such as Google have developed principles for responsible AI, and the Institute for Ethical AI & Machine Learning was established in the United Kingdom to carry out "highly technical research into processes and frameworks that support the responsible development, deployment and operation of machine learning systems" (Google, 2021; Institute for Ethical AI & Machine Learning, 2021)
From page 132...
... The technical, legal, and policy barriers to implementing ARWs are intertwined in the sense that technological development needs to be informed by policy and legal requirements. Possible approaches to addressing these issues are both computational and policy or legal related.
From page 133...
... . As Oren Etzioni, the CEO of the Allen Institute for Artificial Intelligence, told attendees at a NASEM convened workshop in 2018, "systems use data from the past to generate models to predict the future, so if society's past was racist and sexist, the models will carry that bias into the future and also, for technical reasons, exacerbate it" (NASEM, 2018c)
From page 134...
... The authors concluded, "The iREDS approach shifts the paradigm of research ethics training from merely telling researchers what is and is not ethical, to empowering them to incorporate ethical practices into their research workflow." 134 PREPUBLICATION COPY -- Uncorrected Proofs


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