美国NSF中的“图情”基金有哪些？(5)

End Date:

August 31, 2021 (Estimated)

[本文来自：www.11jj.com]

Awarded Amount to Date: [原创文章：www.11jj.com]

$498,778.00

ABSTRACT
The ability to share data among researchers, citizens, government agencies, andeducators creates potential for new research collaborations with significantreal-world impact. However, cities are often unprepared to usecyberinfrastructure to support research that would impact their citizens andcommunities, and researchers often do not have access to or awareness of thekinds of data and questions that are relevant for communities. This projectdevelops innovative and scalable instructional materials, for both in-personand online courses, to increase data science literacy to meet the publicsector"s emerging needs for experts in computational and data science. Thematerials emphasize the types of data necessary for communities to makeinformed decisions (e.g., administrative data on land use, constituent servicerequests, and crime statistics) and applies them to pressing issues presentedby community partners, providing a real-world context for learning. The projectleverages the University of Michigan School of Information"s CitizenInteraction Design program and the Summer Program in Quantitative Methods ofSocial Research at the Inter-university Consortium for Political and SocialResearch (ICPSR) to train undergraduate students, graduate students, and publicsector researchers in collecting, extracting, cleaning, annotating, andanalyzing data generated and used by government organizations. The projectserves the national interest, as stated by NSF"s mission: to promote theprogress of science; to advance the national health, prosperity and welfare; byenabling cities to conduct research that improves their communities and makingthe instructional materials and data used in the courses available for use byother interested educators, communities, and citizens. 
The project addresses bottlenecks in scientific and engineering researchworkforce development by developing and offering three instructionalactivities: (a) a project-based course in which students work directly withMichigan communities to design cyberinfrastructure tools, and (b) two massive,open online courses (MOOCs) in which students learn the fundamentals of datascience for work in the public sector. These courses provide scalable trainingand education programs to increase cyberinfrastructure-enabled research in thepublic sector that leverages administrative data. Course development occurs incollaboration between educators and community partners in cities throughout theMidwest, in order to ensure diversity of topics and audiences, timeliness,relevance, and direct application. This reliance on real data and immediateapplication to community issues is a novel approach in data scienceinstruction. Despite being offered virtually, the MOOCs retain the pedagogicalbenefits of working on meaningful projects with real stakeholders. All threecourses will be offered and evaluated at least once over the course of theproject, and the course materials will be made publicly available through theUniversity of Michigan?s School of Information, ICPSR, and other forums (e.g.,the University"s institutional repository, Deep Blue) for maximum impact. Thelong-term goals are to broaden engagement between researchers and communitiesto leverage advanced cyberinfrastructure to support public sector STEM researchand to contribute to the infrastructure for online, dynamic, personalizedlessons and certifications. Through partnership with the Midwest Big Data Hub,the project ensures that communities in the Midwest have access to resourcesfor workforce development and opportunities to refine educational materials toserve their specific needs now and in the future.

Echo Chambers in Science? The Impact of AcademicRecommender Systems on the Dissemination of Scientific Knowledge

Program Manager:

Cassidy R. Sugimoto
 SMA SBE Off Of Multidisciplinary Activities
 SBE Direct For Social, Behav & Economic Scie

Start Date:

August 1, 2017

End Date:

July 31, 2019 (Estimated)

Awarded Amount to Date:

$293,372.00

ABSTRACT
Digitization of the scientific literauture has occurred in two phases: first,scientific papers began to be digitized, and second, comprehensive searchengines such as Google Scholar, JSTOR and Web of Science emerged that allowscholars to quickly search for and locate published research. Most scientistsnow access the literature through online search engines and digital libraries,and rare is the scientist who walks to the library and peruses the journalshelves for new papers. These new algorithmic search engines thus providescholars with a new lens into the published scientific literature, and thisproject aims to better understand the implications of these technologicalchanges on the practice of scientific discovery and information dissemination.The project investigates whether these new tools are increasing access to awider range of prior literature and thereby democratizing science, or insteadconcentrating scholars" gaze onto an ever smaller set of "star"papers. If the new tools are truly making the widest range of scientificliterature more accessible, it certainly has implications for the prospects forscientific discovery, since scientists are able to consider and engage with allrelevant prior work, a critical ingredient for high quality scientificactivity. On the other hand, evidence from other contexts suggests that theavailability of massive amounts of information puts new pressure on searchingand filtering processes. If this occurs in science it could mean thatscientists increasingly rely upon more concentrated subset of papers thatappear at the top of search results, thereby creating an echo chamber inscience with unintended effects on scientific careers and potentially negativedownstream impacts on scientific innovation.
This research addresses these critical questions primarily via statisticalanalyses of comprehensive citation data from JSTOR and the Web of Science. Theresearch focuses on the extent of citation concentration within and acrossdisciplines; and the role of journals in an article-based search environment.Citation patterns have changed dramatically in the wake of the digitaltransition, becoming both more concentrated and more vulnerable to cumulativeadvantage processes. The population-level analyses are augmented byobservational data describing how scientists actually interact with informationtechnologies and academic recommender systems in the course of their scientificpractice. This combination of methods links processes at the individual andpopulation levels over time, and emphasizes the downstream impacts of informationretrieval and citation on scientific innovation and career trajectories. Acritical policy related implication is that online recommender systems can biassearch results and thus the visibility of specific scientific findings. This isrelevant to the next generation of scientists using these search environmentsand those who evaluate scientists and their work.

 

 

RUI: Framework: Data - An Open Semantic Data Frameworkfor Data-Driven Discovery

Program Manager:

Amy Walton
 OAC Office of Advanced Cyberinfrastructure (OAC)
 CSE Direct For Computer & Info Scie & Enginr

Start Date:

January 1, 2019

End Date: