Cyberinfrastructure (CI) is a standard tool in the geosciences, but the creation of successful CI remains difficult, and expensive projects can have significant consequences for scientific communities if they do not result in success. In this paper, we present an effort to solicit feedback on cyberinfrastructure needs from a broad community of geoscientists by means of user scenarios to inform the National Science Foundation’s (NSF) EarthCube program. The method for the user scenarios was semi-structured interviews, a total of 50 of which were collected from a broad range of scientists and analyzed. A wide variety of challenges were identified, with the most commonly articulated challenges being an inability to find data of interest in an online repository, the heterogeneity of data and metadata, the lack of needed software (which in turn drove redundant development of needed software in multiple groups), and insufficient or unstable funding for long-term cyberinfrastructure. While the user scenarios do not provide formal requirements in the software engineering sense, they do provide expressions of user challenges that, in many cases, are sufficiently detailed to inform high-level requirement development.

The information provided in the Intergovernmental Panel on Climate Change (IPCC; http://ipcc.ch ) Assessment Reports (ARs) inform climate change policy development. Within the IPCC the scientific coordination of the ARs is conducted by three Working Groups (WGs) comprising of the Bureaus supported by their Technical Support Units (TSUs). Data management support is provided by the IPCC Data Distribution Centre (DDC; http://ipcc-data.org ), which is overseen by the Task Group on Data Support for Climate Change Assessments (TG-Data; formerly TGICA). The DDC is a federated structure that is currently managed by the Centre for Environmental Data Analysis (CEDA; http://www.ceda.ac.uk/ ), United Kingdom; the World Data Center for Climate (WDCC; http://www.wdc-climate.de ), Germany; and the Center for International Earth Science Information Network (CIESIN; http://www.ciesin.columbia.edu/ ) at Columbia University, U.S. For the IPCC Sixth Assessment cycle (AR6), analyses of climate simulations and observations published in scientific literature will be assessed. The reports will include figures and tables prepared from the underlying digital information. The DDC plays an increasingly important role in facilitating the exchange of data, as well as curating the assessed datasets, scripts and provenance records to facilitate the assessment process and to support the traceability of AR6 results through long-term continuity of data management and curation. These issues, among others, are addressed by the DDC support group ( https://cedadev.github.io/ipcc_ddc ) currently consisting of members from the three TSUs and the three DDC managers.

As a novel recursion neural network, Echo State Networks (ESN) are characterized by strong nonlinear prediction capability and effective and straightforward training algorithms. However, conventional ESN predictions require a large volume of training samples. Meanwhile, the time sequence data are complicated and unstable, resulting in insufficient learning of this network and difficult training. As a result, the accuracies of conventional ESN predictions are limited. Aimed at this issue, a time series prediction model of Grey Wolf optimized ESN has been proposed. Wout of ESN was optimized using the Grey Wolf algorithm and predictions of time series data were achieved using simplified training. The results indicated that the optimized time series prediction method exhibits superior prediction accuracy at a small sample size, compared with conventional prediction methods.

The sharing of research data allows for information reuse and knowledge advancement but its realization is often a challenge and seldom successful in practice. We propose a workflow for the design of a User Support System (USS) aimed at tutoring research groups in data sharing by considering their social and domain backgrounds. Our engagement approach focuses on multidisciplinary geospatial research, particularly when interoperable data sharing is required. Specifically, we first characterize the research community on the basis of the behavior and competences in data management by its groups and then target the needs of the latter with specific facilities. We address for the first time in literature the issue of modeling research groups as targets of the USS and provide a roadmap to standardize USS activities across different communities. We describe the implementation of the workflow in the context of an Italian research project and we assess the impact of the USS in terms of increase in the number of nodes and resources in the project’s data infrastructure, and of fulfilment of the expectations by the research groups.

Research Data Management at Bielefeld University is considered as a cross-cutting task among central facilities and research groups at the faculties. While initially started as project “Bielefeld Data Informium” lasting over seven years (2010–2015), it is now being expanded by setting up a Competence Center for Research Data. The evolution of the institutional RDM is based on the three-pillar principle: 1. Policies, 2. Technical infrastructure and 3. Support structures. The problem of data quality and the issues with reproducibility of research data is addressed in the project Conquaire. It is creating an infrastructure for the processing and versioning of research data which will finally allow publishing of research data in the institutional repository. Conquaire extends the existing RDM infrastructure in three ways: with a Collaborative Platform, Data Quality Checking, and Reproducible Research.

In the materials science domain the data-driven science paradigm has become the focus since the beginning of the 2000s. A large number of research groups and communities are building and developing data-driven workflows. However, much of the data and knowledge is stored in different heterogeneous data sources maintained by different groups. This leads to a reduced availability of the data and poor interoperability between systems in this domain. Ontology-based techniques are an important way to reduce these problems and a number of efforts have started. In this paper we investigate efforts in the materials science, and in particular in the nanotechnology domain, and show how such ontologies developed by domain experts, can be improved. We use a phrase-based topic model approach and formal topical concept analysis on unstructured text in this domain to suggest additional concepts and axioms for the ontology that should be validated by a domain expert. We describe the techniques and show the usefulness of the approach through an experiment where we extend two nanotechnology ontologies using approximately 600 titles and abstracts.

Scientific data stewardship is an important part of long-term preservation and the use/reuse of digital research data. It is critical for ensuring trustworthiness of data, products, and services, which is important for decision-making. Recent U.S. federal government directives and scientific organization guidelines have levied specific requirements, increasing the need for a more formal approach to ensuring that stewardship activities support compliance verification and reporting. However, many science data centers lack an integrated, systematic, and holistic framework to support such efforts. The current business- and process-oriented stewardship frameworks are too costly and lengthy for most data centers to implement. They often do not explicitly address the federal stewardship requirements and/or the uniqueness of geospatial data. This work proposes a data-centric conceptual enterprise framework for managing stewardship activities, based on the philosophy behind the Plan-Do-Check-Act (PDCA) cycle, a proven industrial concept. This framework, which includes the application of maturity assessment models, allows for quantitative evaluation of how organizations manage their stewardship activities and supports informed decision-making for continual improvement towards full compliance with federal, agency, and user requirements.

This paper undertakes a detailed examination of the availability and quality of data on public expenditures in agriculture in Africa. We consider the case of Mozambique, a country characterised by low income and low administrative capacity, but also by a policy environment that has turned a focused lens on public funding to agriculture. We explore the extent to which domestic analysts may be able to access and use such data to reliably quantify public resource allocation to the sector, and to unpack the ‘black box’ of what goes into country-level public expenditure statistics. We find that data are, surprisingly, freely available in great abundance. This has encouraging aspects but also pitfalls: On the one hand, data that are often out of public sight are openly accessible for Mozambican researchers to draw upon. But the drawback of high abundance emanates from its manifestation in the form of a proliferation of multiple classification systems used to create a fine disaggregation of public funds data; given Mozambique’s limited public sector capacity, this has meant that each classification system leaves a lot to be desired, making it hard to use any single one to accurately and fully reliably reconstruct the amount of public resources going to agriculture. Making the hard choice to eliminate some of the classification systems, and dedicate this freed-up capacity to be more thorough on the retained ones, would better serve domestic users of such data, as well as the government, which is both a consumer and producer of these data.

This introduction outlines the contents of the special collection “Open Data and Africa”, which documents the goals and aspirations associated with Open Data means in Africa today: what opportunities they offer, what challenges they pose and what the implications follow from the increasing political and institutional support for this concept.

Crystallographic databases have existed as electronic resources for over 50 years, and have provided comprehensive archives of crystal structures of inorganic, organic, metal–organic and biological macromolecular compounds of immense value to a wide range of structural sciences. They thus serve a variety of scientific disciplines, but are all driven by considerations of accuracy, precise characterization, and potential for search, analysis and reuse. They also serve a variety of end-users in academia and industry, and have evolved through different funding and licensing models. The diversity of their operational mechanisms combined with their undisputed value as scientific research tools gives rise to a rich ecosystem. A session at SciDataCon2016 gave an overview of the largest extant crystallographic databases and their current activities and plans for the future. This review summarizes these presentations and considers them alongside other players in the field, demonstrating their variety, versatility and focus on quality and usefulness.