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

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

Lists of clinical codes are the foundation for research undertaken using electronic medical records (EMRs). If clinical code lists are not available, reviewers are unable to determine the validity of research, full study replication is impossible, researchers are unable to make effective comparisons between studies, and the construction of new code lists is subject to much duplication of effort. Despite this, the publication of clinical codes is rarely if ever a requirement for obtaining grants, validating protocols, or publishing research. In a representative sample of 450 EMR primary research articles indexed on PubMed, we found that only 19 (5.1%) were accompanied by a full set of published clinical codes and 32 (8.6%) stated that code lists were available on request. To help address these problems, we have built an online repository where researchers using EMRs can upload and download lists of clinical codes. The repository will enable clinical researchers to better validate EMR studies, build on previous code lists and compare disease definitions across studies. It will also assist health informaticians in replicating database studies, tracking changes in disease definitions or clinical coding practice through time and sharing clinical code information across platforms and data sources as research objects.

/pdf/clinicalcodes-an-online-clinical-codes-repository-to-improve-4ee7l4f8j5.pdf

ClinicalCodes: An Online Clinical Codes Repository to Improve the Validity and Reproducibility of Research Using Electronic Medical Records

Geoscientists now live in a world rich with digital data and methods, and their computational research cannot be fully captured in traditional publications. The Geoscience Paper of the Future (GPF) presents an approach to fully document, share, and cite all their research products including data, software, and computational provenance. This article proposes best practices for GPF authors to make data, software, and methods openly accessible, citable, and well documented. The publication of digital objects empowers scientists to manage their research products as valuable scientific assets in an open and transparent way that enables broader access by other scientists, students, decision makers, and the public. Improving documentation and dissemination of research will accelerate the pace of scientific discovery by improving the ability of others to build upon published work.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/2015EA000136

Toward the Geoscience Paper of the Future: Best practices for documenting and sharing research from data to software to provenance

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.

科研人员在钻研科研问题与分享科研数据的过程中, 需要某种基础设施来确保数据最大程度的获取性、稳定性和可用性。这类基础设施可以统称为科研数据知识库(Research Data Repository, RDR)。自2012 年启动的re3data.org 项目, 主要从事科研数据知识库的登记注册, 以及为科研人员、科研资助组织、图书馆和出版商等提供有关异构科研数据知识库的全景概述。截至2013 年7 月, 已有400 个科研数据知识库向re3data.org 登记, 其中288 个采用re3data.org 的信息图标, 以协助科研人员遴选合适的知识库, 并且存储与重用他们的数据。这篇论文描绘异构RDR 的全景, 表述机构的、学科的、跨学科的以及项目专业的RDR 类型。深入描述re3data.org 的特性, 以及这套注册系统如何协助科研人员分辨适合存储和搜索科研数据的知识库。

Making Research Data Repositories Visible：The re3data.org Registry

Vocabulary for the Registration and Description of Research Data Repositories. Version 1.0

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Jens Gundlach

Papers

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

Making Research Data Repositories Visible：The re3data.org Registry

Vocabulary for the Registration and Description of Research Data Repositories. Version 1.0

Schema for the description of research data repositories : version 2.1

Vocabulary for the Registration and Description of Research Data Repositories. Version 2.0