Uncertainty About the Long-Term: Digital Libraries, Astronomy Data, and Open Source Software

Digital library developers make critical design and implementation decisions in the face of uncertainties about the future. We present a qualitative case study of the Large Synoptic Survey Telescope (LSST), a major astronomy project that will collect and make available large-scale datasets. LSST developers make decisions now, while facing uncertainties about its period of operations (2022-2032). Uncertainties we identify include topics researchers will seek to address, tools and expertise, and availability of other infrastructures to exploit LSST observations. LSST is using an open source approach to developing and releasing its data management software. We evaluate benefits and burdens of this approach as a strategy for addressing uncertainty. Benefits include: enabling software to adapt to researchers’ changing needs; embedding LSST standards and tools in community practices; and promoting interoperability with other infrastructures. Burdens include: open source community management; documentation requirements; and trade-offs between software speed and accessibility.Alfred P. Sloan Foundation (#20113194, #201514001)Ope

Variables As Currency: Linking Meta-Analysis Research and Data Paths in Sciences

Meta-analyses are studies that bring together data or results from multiple independent studies to produce new and over-arching findings. Current data curation systems only partially support meta-analytic research. Some important meta-analytic tasks, such as the selection of relevant studies for review and the integration of research datasets or findings, are not well supported in current data curation systems. To design tools and services that more fully support meta-analyses, we need a better understanding of meta-analytic research. This includes an understanding of both the practices of researchers who perform the analyses and the characteristics of the individual studies that are brought together. In this study, we make an initial contribution to filling this gap by developing a conceptual framework linking meta-analyses with data paths represented in published articles selected for the analysis. The framework focuses on key variables that represent primary/secondary datasets or derived socio-ecological data, contexts of use, and the data transformations that are applied. We introduce the notion of using variables and their relevant information (e.g., metadata and variable relationships) as a type of currency to facilitate synthesis of findings across individual studies and leverage larger bodies of relevant source data produced in small science research. Handling variables in this manner provides an equalizing factor between data from otherwise disparate data-producing communities. We conclude with implications for exploring data integration and synthesis issues as well as system development

Music Information Retrieval: An Inspirational Guide to Transfer from Related Disciplines

The emerging field of Music Information Retrieval (MIR) has been influenced by neighboring domains in signal processing and machine learning, including automatic speech recognition, image processing and text information retrieval. In this contribution, we start with concrete examples for methodology transfer between speech and music processing, oriented on the building blocks of pattern recognition: preprocessing, feature extraction, and classification/decoding. We then assume a higher level viewpoint when describing sources of mutual inspiration derived from text and image information retrieval. We conclude that dealing with the peculiarities of music in MIR research has contributed to advancing the state-of-the-art in other fields, and that many future challenges in MIR are strikingly similar to those that other research areas have been facing

Artexte metadata conversion to EPrints: adaptation of digital repository software to visual and media arts documentation

Purpose: The following research questions structured our analysis: Does an open access institutional repository model respond to the needs of a non-academic documentation centre? Is EPrints software a good match to support the needs of the existing metadata describing Artexte's collection? What are the customizations required to accommodate existing Artexte metadata using EPrints? Methods: We exported the existing metadata schema and sample data in Artexte’s three databases, performed a manual evaluation of metadata quality and compared the 49 Artexte fields to those available within the EPrints schema. Results: We identify the metadata elements that mapped by default without the need for customization or modification and those which would need to be added to EPrints using configuration files. We also identify the custom software development to accommodate Artexte metadata using EPrints: the bilingual controlled vocabulary demands an extension of the EPrints subject taxonomy model with thesaurus semantic relationships. Conclusions: Comparing Artexte and EPrints metadata schemas, we found that 15 out of 49 fields mapped by default without need for modification, 25 fields would need to be added to EPrints configuration files and 1 field will be removed during the migration. With only 8 fields requiring some special attention, we conclude that EPrints is suitable to the needs of Artexte's bibliographic data management

Integrating Technology, Curriculum, and Online Resources: A Multilevel Model Study of Impacts on Science Teachers and Students

This scale-up study investigated the impact of a teacher technology tool (Curriculum Customization Service, CCS), curriculum, and online resources on earth science teachers’ attitudes, beliefs, and practices and on students’ achievement and engagement with science learning. Participants included 73 teachers and over 2,000 ninth-grade students within five public school districts in the western U.S. To assess the impact on teachers, changes between pre- and postsurveys were examined. Results suggest that the CCS tool appeared to significantly increase both teachers’ awareness of other earth science teachers’ practices and teachers’ frequency of using interactive resources in their lesson planning and classroom teaching. A standard multiple regression model was developed. In addition to “District,” “Training condition”(whether or not teachers received CCS training) appeared to predict teachers’ attitudes, beliefs, and practices. Teachers who received CCS training tended to have lower postsurvey scores than their peers who had no CCS training. Overall, usage of the CCS tool tended to be low, and there were differences among school districts. To assess the impact on students, changes were examined between pre- and postsurveys of (1) knowledge assessment and (2) students’ engagement with science learning. Students showed pre- to postsurvey improvements in knowledge assessment, with small to medium effect sizes. A nesting effect (students clustered within teachers) in the Earth’s Dynamic Geosphere (EDG) knowledge assessment was identified and addressed by fitting a two-level hierarchical linear model (HLM). In addition, significant school district differences existed for student post-knowledge assessment scores. On the student engagement questionnaire, students tended to be neutral or to slightly disagree that science learning was important in terms of using science in daily life, stimulating their thinking, discovering science concepts, and satisfying their own curiosity. Students did not appear to change their self-reported engagement level after the intervention. Additionally, three multiple regression models were developed. Factors from the district, teacher, and student levels were identified to predict student post-knowledge assessments and their engagement with science learning. The results provide information to both the research community and practitioners

Models and algorithms for automatic detection of language evolution : towards finding and interpreting of content in long-term archives

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