The incorporation of data sharing into the research lifecycle is an important part of modern scholarly debate. In this study, the DataONE Usability and Assessment working group addresses two primary goals: To examine the current state of data sharing and reuse perceptions and practices among research scientists as they compare to the 2009/2010 baseline study, and to examine differences in practices and perceptions across age groups, geographic regions, and subject disciplines. We distributed surveys to a multinational sample of scientific researchers at two different time periods (October 2009 to July 2010 and October 2013 to March 2014) to observe current states of data sharing and to see what, if any, changes have occurred in the past 3–4 years. We also looked at differences across age, geographic, and discipline-based groups as they currently exist in the 2013/2014 survey. Results point to increased acceptance of and willingness to engage in data sharing, as well as an increase in actual data sharing behaviors. However, there is also increased perceived risk associated with data sharing, and specific barriers to data sharing persist. There are also differences across age groups, with younger respondents feeling more favorably toward data sharing and reuse, yet making less of their data available than older respondents. Geographic differences exist as well, which can in part be understood in terms of collectivist and individualist cultural differences. An examination of subject disciplines shows that the constraints and enablers of data sharing and reuse manifest differently across disciplines. Implications of these findings include the continued need to build infrastructure that promotes data sharing while recognizing the needs of different research communities. Moving into the future, organizations such as DataONE will continue to assess, monitor, educate, and provide the infrastructure necessary to support such complex grand science challenges.

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Changes in data sharing and data reuse practices and perceptions among scientists worldwide

There are thousands of data repositories on the Web, providing access to millions of datasets. National and regional governments, scientific publishers and consortia, commercial data providers, and others publish data for fields ranging from social science to life science to high-energy physics to climate science and more. Access to this data is critical to facilitating reproducibility of research results, enabling scientists to build on others' work, and providing data journalists easier access to information and its provenance. In this paper, we discuss Google Dataset Search, a dataset-discovery tool that provides search capabilities over potentially all datasets published on the Web. The approach relies on an open ecosystem, where dataset owners and providers publish semantically enhanced metadata on their own sites. We then aggregate, normalize, and reconcile this metadata, providing a search engine that lets users find datasets in the “long tail” of the Web. In this paper, we discuss both social and technical challenges in building this type of tool, and the lessons that we learned from this experience.

Google Dataset Search: Building a search engine for datasets in an open Web ecosystem

The credibility of scientific claims depends upon the transparency of the research products upon which they are based (e.g., study protocols, data, materials, and analysis scripts). As psychology navigates a period of unprecedented introspection, user-friendly tools and services that support open science have flourished. However, the plethora of decisions and choices involved can be bewildering. Here we provide a practical guide to help researchers navigate the process of preparing and sharing the products of their research (e.g., choosing a repository, preparing their research products for sharing, structuring folders, etc.). Being an open scientist means adopting a few straightforward research management practices, which lead to less error prone, reproducible research workflows. Further, this adoption can be piecemeal – each incremental step towards complete transparency adds positive value. Transparent research practices not only improve the efficiency of individual researchers, they enhance the credibility of the knowledge generated by the scientific community.

A practical guide for transparency in psychological science

Ecology has joined a world of big data. Two complementary frameworks define big data: data that exceed the analytical capacities of individuals or disciplines or the “Four Vs” axes of volume, variety, veracity, and velocity. Variety predominates in ecoinformatics and limits the scalability of ecological science. Volume varies widely. Ecological velocity is low but growing as data throughput and societal needs increase. Ecological big-data systems include in situ and remote sensors, community data resources, biodiversity databases, citizen science, and permanent stations. Technological solutions include the development of open code- and data-sharing platforms, flexible statistical models that can handle heterogeneous data and sources of uncertainty, and cloud-computing delivery of high-velocity computing to large-volume analytics. Cultural solutions include training targeted to early and current scientific workforce and strengthening collaborations among ecologists and data scientists. The broader goal is to maximize the power, scalability, and timeliness of ecological insights and forecasting.

Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions

https://par.nsf.gov/servlets/purl/10081670

Ecological data sharing

Researchers require infrastructures that ensure a maximum of accessibility, stability and reliability to facilitate working with and sharing of research data. Such infrastructures are being increasingly summarized under the term Research Data Repositories (RDR). The project re3data.org–Registry of Research Data Repositories–has begun to index research data repositories in 2012 and offers researchers, funding organizations, libraries and publishers an overview of the heterogeneous research data repository landscape. In July 2013 re3data.org lists 400 research data repositories and counting. 288 of these are described in detail using the re3data.org vocabulary. Information icons help researchers to easily identify an adequate repository for the storage and reuse of their data. This article describes the heterogeneous RDR landscape and presents a typology of institutional, disciplinary, multidisciplinary and project-specific RDR. Further the article outlines the features of re3data.org, and shows how this registry helps to identify appropriate repositories for storage and search of research data.

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Making research data repositories visible: the re3data.org Registry.

This article provides a comprehensive descriptive and statistical analysis of metadata information on 1,381 research data repositories worldwide and across all research disciplines. The analyzed metadata is derived from the re3data database, enabling search and browse functionalities for the global registry of research data repositories. The analysis focuses mainly on institutions that operate research data repositories, types and subjects of research data repositories (RDR), access conditions as well as services provided by the research data repositories. RDR differ in terms of the service levels they offer, languages they support or standards they comply with. These statements are commonly acknowledged by saying the RDR landscape is heterogeneous. As expected, we found a heterogeneous RDR landscape that is mostly influenced by the repositories' disciplinary background for which they offer services.

The Landscape of Research Data Repositories in 2015. A re3data Analysis

The Landscape of Research Data Repositories in 2015

In diesem Beitrag wird der Lehrund Forschungsbereich Informationsmanagement am IBI vorgestellt. Aktuelle und zukünftige Lehrgebiete werden benannt und der Bezug zu Forschungsschwerpunkten herausgestellt. Dazu gehören drittmittelgeförderte Projekte, Forschungsarbeiten sowie Projekte in der Lehre. Die Forschungsschwerpunkte werden darüber hinaus in die Entwicklung der Forschungsgebiete in der Bibliotheksund Informationswissenschaft eingeordnet.

/pdf/die-digitale-forschungswelt-als-gegenstand-der-forschung-i6tad43psj.pdf

„Die digitale Forschungswelt“ als Gegenstand der Forschung. Lehrstuhl Informationsmanagement

Authors: Jessika Rücknagel , Paul Vierkant , Robert Ulrich , Gabriele Kloska , Edeltraud Schnepf , David Fichtmüller , Evelyn Reuter , Angelika Semrau , Maxi Kindling , Heinz Pampel , Michael Witt , Florian Fritze , Stephanie van de Sandt , Jens Klump , HansJürgen Goebelbecker , Michael Skarupianski , Roland Bertelmann , Peter Schirmbacher , Frank Scholze , Claudia Kramer , Claudio Fuchs , Shaked Spier , Agnes Kirchhoff d

Peter Schirmbacher

Papers

Making research data repositories visible: the re3data.org Registry.

The Landscape of Research Data Repositories in 2015. A re3data Analysis

The Landscape of Research Data Repositories in 2015

„Die digitale Forschungswelt“ als Gegenstand der Forschung. Lehrstuhl Informationsmanagement

Metadata Schema for the Description of Research Data Repositories : version 3.0