Archives des mots-clés : research data

Science Metadata Management, Interoperability and Data Citations of the National Institute of Polar Research, Japan

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Authors : M. Kanao, M. Okada, J. Friddel, A. Kadokura

The Polar Data Centre (PDC) of the National Institute of Polar Research (NIPR) has a responsibility to manage polar science data as part of the National Antarctic Data Centre and the Science Committee on Antarctic Research. During the International Polar Year (IPY 2007–2008), a remarkable number of data/metadata involving multi-disciplinary science activities were compiled.

Although the long-term stewardship of the accumulation of metadata falls to the data center of NIPR, the work has been in collaboration with the Global Change Master Directory, the Polar Information Commons, the World Data System and other data science bodies/communities under the International Council for Science.

In addition, links with other data centers, such as the Data Integration and Analysis System Program of the Global Earth Observation System of Systems and the Polar Data Catalogue of Canada were initiated in 2014 using the Open Archives Initiative Protocol for Metadata Harvesting. The metadata compiled by the PDC were recently modified using an automatic attributing system and DataCite through the Japan Link Center.

URL : Science Metadata Management, Interoperability and Data Citations of the National Institute of Polar Research, Japan

DOI : http://doi.org/10.5334/dsj-2018-001

Recommendations to Improve Downloads of Large Earth Observation Data

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Authors : Rahul Ramachandran, Christopher Lynnes, Kathleen Baynes, Kevin Murphy, Jamie Baker, Jamie Kinney, Ariel Gold, Jed Sundwall, Mark Korver, Allison Lieber, William Vambenepe, Matthew Hancher,  Rebecca Moore, Tyler Erickson, Josh Henretig,
Brant Zwiefel, Heather Patrick-Ahlstrom, Matthew J. Smith

With the volume of Earth observation data expanding rapidly, cloud computing is quickly changing the way these data are processed, analyzed, and visualized. Collocating freely available Earth observation data on a cloud computing infrastructure may create opportunities unforeseen by the original data provider for innovation and value-added data re-use, but existing systems at data centers are not designed for supporting requests for large data transfers.

A lack of common methodology necessitates that each data center handle such requests from different cloud vendors differently. Guidelines are needed to support enabling all cloud vendors to utilize a common methodology for bulk-downloading data from data centers, thus preventing the providers from building custom capabilities to meet the needs of individual vendors.

This paper presents recommendations distilled from use cases provided by three cloud vendors (Amazon, Google, and Microsoft) and are based on the vendors’ interactions with data systems at different Federal agencies and organizations.

These specific recommendations range from obvious steps for improving data usability (such as ensuring the use of standard data formats and commonly supported projections) to non-obvious undertakings important for enabling bulk data downloads at scale.

These recommendations can be used to evaluate and improve existing data systems for high-volume data transfers, and their adoption can lead to cloud vendors utilizing a common methodology.

URL : Recommendations to Improve Downloads of Large Earth Observation Data

DOI : http://doi.org/10.5334/dsj-2018-002

 

Understanding Data Retrieval Practices: A Social Informatics Perspective

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Authors : Kathleen Gregory, Helena Cousijn, Paul Groth, Andrea Scharnhorst, Sally Wyatt

Open research data are heralded as having the potential to increase effectiveness, productivity, and reproducibility in science, but little is known about the actual practices involved in data search and retrieval.

The socio-technical problem of locating data for (re)use is often reduced to the technological dimension of designing data search systems. In this article, we explore how a social informatics perspective can help to better analyze the current academic discourse about data retrieval as well as to study user practices and behaviors.

We employ two methods in our analysis – bibliometrics and interviews with data seekers – and conclude with a discussion of the implications of our findings for designing data discovery systems.

URL : https://arxiv.org/abs/1801.04971

DataMed – an open source discovery index for finding biomedical datasets

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Authors : Xiaoling Chen, Anupama E Gururaj, Burak Ozyurt, Ruiling Liu, Ergin Soysal, Trevor Cohen, Firat Tiryaki, Yueling Li, Nansu Zong, Min Jiang, Deevakar Rogith, Mandana Salimi, Hyeon-eui Kim, Philippe Rocca-Serra, Alejandra Gonzalez-Beltran, Claudiu Farcas, Todd Johnson, Ron Margolis, George Alter, Susanna-Assunta Sansone, Ian M Fore, Lucila Ohno-Machado, Jeffrey S Grethe, Hua Xu

Objective

Finding relevant datasets is important for promoting data reuse in the biomedical domain, but it is challenging given the volume and complexity of biomedical data. Here we describe the development of an open source biomedical data discovery system called DataMed, with the goal of promoting the building of additional data indexes in the biomedical domain.

Materials and Methods

DataMed, which can efficiently index and search diverse types of biomedical datasets across repositories, is developed through the National Institutes of Health–funded biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) consortium.

It consists of 2 main components: (1) a data ingestion pipeline that collects and transforms original metadata information to a unified metadata model, called DatA Tag Suite (DATS), and (2) a search engine that finds relevant datasets based on user-entered queries.

In addition to describing its architecture and techniques, we evaluated individual components within DataMed, including the accuracy of the ingestion pipeline, the prevalence of the DATS model across repositories, and the overall performance of the dataset retrieval engine.

Results and Conclusion

Our manual review shows that the ingestion pipeline could achieve an accuracy of 90% and core elements of DATS had varied frequency across repositories. On a manually curated benchmark dataset, the DataMed search engine achieved an inferred average precision of 0.2033 and a precision at 10 (P@10, the number of relevant results in the top 10 search results) of 0.6022, by implementing advanced natural language processing and terminology services.

Currently, we have made the DataMed system publically available as an open source package for the biomedical community.

DOI : https://doi.org/10.1093/jamia/ocx121

 

Attitudes and norms affecting scientists’ data reuse

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Authors : Renata Gonçalves Curty, Kevin Crowston, Alison Specht, Bruce W. Grant, Elizabeth D. Dalton

The value of sharing scientific research data is widely appreciated, but factors that hinder or prompt the reuse of data remain poorly understood. Using the Theory of Reasoned Action, we test the relationship between the beliefs and attitudes of scientists towards data reuse, and their self-reported data reuse behaviour.

To do so, we used existing responses to selected questions from a worldwide survey of scientists developed and administered by the DataONE Usability and Assessment Working Group (thus practicing data reuse ourselves).

Results show that the perceived efficacy and efficiency of data reuse are strong predictors of reuse behaviour, and that the perceived importance of data reuse corresponds to greater reuse. Expressed lack of trust in existing data and perceived norms against data reuse were not found to be major impediments for reuse contrary to our expectations.

We found that reported use of models and remotely-sensed data was associated with greater reuse. The results suggest that data reuse would be encouraged and normalized by demonstration of its value.

We offer some theoretical and practical suggestions that could help to legitimize investment and policies in favor of data sharing.

URL : Attitudes and norms affecting scientists’ data reuse

DOI : https://doi.org/10.1371/journal.pone.0189288

Data-Sprinting: a Public Approach to Digital Research

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Authors : Tommaso Venturini, Anders Munk, Axel Meunier

This chapter is about the politics of interdisciplinarity. Not in the sense of the research politics fostering collaboration across disciplines, but in the stronger sense of transcending disciplinary boundaries to make significant political contributions.

In short: it is about making research public. To address this question, this chapter introduces (through a concrete example in climate debate research) an original research format, that we call data-sprinting.

URL : https://hal.archives-ouvertes.fr/hal-01672288

Evaluating the Effectiveness of Data Management Training: DataONE’s Survey Instrument

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Authors : Chung-Yi Hou, Heather Soyka, Vivian Hutchison, Isis Sema, Chris Allen, Amber Budden

Effective management is a key component for preparing data to be retained for future long term access, use, and reuse by a broader community. Developing the skills to plan and perform data management tasks is important for individuals and institutions.

Teaching data literacy skills may also help to mitigate the impact of data deluge and other effects of being overexposed to and overwhelmed by data.

The process of learning how to manage data effectively for the entire research data lifecycle can be complex. There are often multiple stages involved within a lifecycle for managing data, and each stage may require specific knowledge, expertise, and resources.

Additionally, although a range of organizations offers data management education and training resources, it can often be difficult to assess how effective the resources are for educating users to meet their data management requirements.

In the case of Data Observation Network for Earth (DataONE), DataONE’s extensive collaboration with individuals and organizations has informed the development of multiple educational resources. Through these interactions, DataONE understands that the process of creating and maintaining educational materials that remain responsive to community needs is reliant on careful evaluations.

Therefore, the impetus for a comprehensive, customizable Education EVAluation instrument (EEVA) is grounded in the need for tools to assess and improve current and future training and educational resources for research data management.

In this paper, the authors outline and provide context for the background and motivations that led to creating EEVA for evaluating the effectiveness of data management educational resources. The paper details the process and results of the current version of EEVA.

Finally, the paper highlights the key features, potential uses, and the next steps in order to improve future extensions and revisions of EEVA.

URL : Evaluating the Effectiveness of Data Management Training: DataONE’s Survey Instrument

DOI : https://doi.org/10.2218/ijdc.v12i2.508