A Survey of Digital Library Aggregation Services

This report provides an overview of a diverse set of more than thirty digital library aggregation services, organizes them into functional clusters, and then evaluates them more fully from the perspective of an informed user. Most of the services under review rely wholly or partially on the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), although some of them predate its inception and a few use predominantly Z39.50 protocols. In the opening section of this report, each service is annotated with its organizational affiliation, subject coverage, function, audience, status, and size. Critical issues surrounding each of these elements are presented in order to provide the reader with an appreciation of the nuances inherent in seemingly straightforward factual information, such as audience or size. Each service is then grouped into one of five functional clusters: • open access e-print archives and servers; • cross-archive search services and aggregators; • from digital collections to digital library environments; • from peer-reviewed referratories to portal services; • specialized search engines

Iterative Near-Term Ecological Forecasting: Needs, Opportunities, And Challenges

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward

Open, distributed and user-centered: Towards a paradigm shift in innovation policy

Today's innovation policies ignore that innovation is increasingly open, distributed and user-centered. In this paper we introduce the user-centered model as an alternative paradigm of how innovation 'works'. We discuss how it differs from traditional, linear producer-centered model, argue why it is legitimate to develop policies in support of it, and provide specific directions. �

Development of linguistic linked open data resources for collaborative data-intensive research in the language sciences

Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zin

Opportunities From the Digital Revolution: Implications for Researching, Publishing, and Consuming Qualitative Research

In the 1990s, the term 'online' research emerged as a new and vibrant suite of methods, focused on exploitation of sources not collected by traditional social science methods. Today, at least one part of the research life cycle is likely to be carried out 'online,' from data collection through to publishing. In this article, we seek to understand emergent modes of doing and reporting qualitative research 'online.' With a greater freedom now to term oneself a 'researcher,' what opportunities and problems do working with online data sources bring? We explore implications of emerging requirements to submit supporting data for social science journal articles and question whether these demands might disrupt the very nature of and identity of qualitative research. Finally, we examine more recent forms of publishing and communicating research that support outputs where data play an integral role in elucidating context and enhancing the reading experience

Operationalizing Machine Learning: An Interview Study

Organizations rely on machine learning engineers (MLEs) to operationalize ML, i.e., deploy and maintain ML pipelines in production. The process of operationalizing ML, or MLOps, consists of a continual loop of (i) data collection and labeling, (ii) experimentation to improve ML performance, (iii) evaluation throughout a multi-staged deployment process, and (iv) monitoring of performance drops in production. When considered together, these responsibilities seem staggering -- how does anyone do MLOps, what are the unaddressed challenges, and what are the implications for tool builders? We conducted semi-structured ethnographic interviews with 18 MLEs working across many applications, including chatbots, autonomous vehicles, and finance. Our interviews expose three variables that govern success for a production ML deployment: Velocity, Validation, and Versioning. We summarize common practices for successful ML experimentation, deployment, and sustaining production performance. Finally, we discuss interviewees' pain points and anti-patterns, with implications for tool design.Comment: 20 pages, 4 figure

Development of Linguistic Linked Open Data Resources for Collaborative Data-Intensive Research in the Language Sciences

This book is the product of an international workshop dedicated to addressing data accessibility in the linguistics field. It is therefore vital to the book’s mission that its content be open access. Linguistics as a field remains behind many others as far as data management and accessibility strategies. The problem is particularly acute in the subfield of language acquisition, where international linguistic sound files are needed for reference. Linguists' concerns are very much tied to amount of information accumulated by individual researchers over the years that remains fragmented and inaccessible to the larger community. These concerns are shared by other fields, but linguistics to date has seen few efforts at addressing them. This collection, undertaken by a range of leading experts in the field, represents a big step forward. Its international scope and interdisciplinary combination of scholars/librarians/data consultants will provide an important contribution to the field

Tilting at the Windmills: Policy Battles Won, Lost, or Long Since Forgotten

http://deepblue.lib.umich.edu/bitstream/2027.42/114394/1/2015 Tilting at Windmills.pd