Is DIA proteomics data FAIR? Current data sharing practices,available bioinformatics infrastructure and recommendations for the future

Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in, for example, instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements.     

Caries-preventive effect of glass ionomer and resin-based fissure sealants on permanent teeth: An update of systematic review evidence

Background

The 1980s marked the occasion when Geographical Information System (GIS) technology was broadly introduced into the geo-spatial community through the establishment of a strong GIS industry. This technology quickly disseminated across many countries, and has now become established as an important research, planning and commercial tool for a wider community that includes organisations in the public and private health sectors. The broad acceptance of GIS technology and the nature of its functionality have meant that numerous datasets have been created over the past three decades. Most of these datasets have been created independently, and without any structured documentation systems in place. However, search and retrieval systems can only work if there is a mechanism for datasets existence to be discovered and this is where proper metadata creation and management can greatly help. This situation must be addressed through support mechanisms such as Web-based portal technologies, metadata editor tools, automation, metadata standards and guidelines and collaborative efforts with relevant individuals and organisations. Engagement with data developers or administrators should also include a strategy of identifying the benefits associated with metadata creation and publication.

Findings

The establishment of numerous Spatial Data Infrastructures (SDIs), and other Internet resources, is a testament to the recognition of the importance of supporting good data management and sharing practices across the geographic information community. These resources extend to health informatics in support of research, public services and teaching and learning. This paper identifies many of these resources available to the UK academic health informatics community. It also reveals the reluctance of many spatial data creators across the wider UK academic community to use these resources to create and publish metadata, or deposit their data in repositories for sharing. The Go-Geo! service is introduced as an SDI developed to provide UK academia with the necessary resources to address the concerns surrounding metadata creation and data sharing. The Go-Geo! portal, Geodoc metadata editor tool, ShareGeo spatial data repository, and a range of other support resources, are described in detail.

Conclusions

This paper describes a variety of resources available for the health research and public health sector to use for managing and sharing their data. The Go-Geo! service is one resource which offers an SDI for the eclectic range of disciplines using GIS in UK academia, including health informatics. The benefits of data management and sharing are immense, and in these times of cost restraints, these resources can be seen as solutions to find cost savings which can be reinvested in more research.     

FAIR research data management as community approach in bioengineering

Research data management (RDM) requires standards, policies, and guidelines. Findable, accessible, interoperable, and reusable (FAIR) data management is critical for sustainable research. Therefore, collaborative approaches for managing FAIR-structured data are becoming increasingly important for long-term, sustainable RDM. However, they are rather hesitantly applied in bioengineering. One of the reasons may be found in the interdisciplinary character of the research field. In addition, bioengineering as application of principles of biology and tools of process engineering, often have to meet different criteria. In consequence, RDM is complicated by the fact that researchers from different scientific institutions must meet the criteria of their home institution, which can lead to additional conflicts. Therefore, centrally provided general repositories implementing a collaborative approach that enables data storage from the outset In a biotechnology research network with over 20 tandem projects, it was demonstrated how FAIR-RDM can be implemented through a collaborative approach and the use of a data structure. In addition, the importance of a structure within a repository was demonstrated to keep biotechnology research data available throughout the entire data lifecycle. Furthermore, the biotechnology research network highlighted the importance of a structure within a repository to keep research data available throughout the entire data lifecycle.     

Knowledge sharing and collaboration in translational research, and the DC-THERA Directory

Biomedical research relies increasingly on large collections of data sets and knowledge whose generation, representation and analysis often require large collaborative and interdisciplinary efforts. This dimension of 'big data' research calls for the development of computational tools to manage such a vast amount of data, as well as tools that can improve communication and access to information from collaborating researchers and from the wider community. Whenever research projects have a defined temporal scope, an additional issue of data management arises, namely how the knowledge generated within the project can be made available beyond its boundaries and life-time. DC-THERA is a European 'Network of Excellence' (NoE) that spawned a very large collaborative and interdisciplinary research community, focusing on the development of novel immunotherapies derived from fundamental research in dendritic cell immunobiology. In this article we introduce the DC-THERA Directory, which is an information system designed to support knowledge management for this research community and beyond. We present how the use of metadata and Semantic Web technologies can effectively help to organize the knowledge generated by modern collaborative research, how these technologies can enable effective data management solutions during and beyond the project lifecycle, and how resources such as the DC-THERA Directory fit into the larger context of e-science.     

Ten simple rules for making a vocabulary FAIR

We present ten simple rules that support converting a legacy vocabulary—a list of terms available in a print-based glossary or in a table not accessible using web standards—into a FAIR vocabulary. Various pathways may be followed to publish the FAIR vocabulary, but we emphasise particularly the goal of providing a globally unique resolvable identifier for each term or concept. A standard representation of the concept should be returned when the individual web identifier is resolved, using SKOS or OWL serialised in an RDF-based representation for machine-interchange and in a web-page for human consumption. Guidelines for vocabulary and term metadata are provided, as well as development and maintenance considerations. The rules are arranged as a stepwise recipe for creating a FAIR vocabulary based on the legacy vocabulary. By following these rules you can achieve the outcome of converting a legacy vocabulary into a standalone FAIR vocabulary, which can be used for unambiguous data annotation. In turn, this increases data interoperability and enables data integration.     

Satellite remote sensing,biodiversity research and conservation of the future

Assessing and predicting ecosystem responses to global environmental change and its impacts on human well-being are high priority targets for the scientific community. The potential for synergies between remote sensing science and ecology, especially satellite remote sensing and conservation biology, has been highlighted by many in the past. Yet, the two research communities have only recently begun to coordinate their agendas. Such synchronization is the key to improving the potential for satellite data effectively to support future environmental management decision-making processes. With this themed issue, we aim to illustrate how integrating remote sensing into ecological research promotes a better understanding of the mechanisms shaping current changes in biodiversity patterns and improves conservation efforts. Added benefits include fostering innovation, generating new research directions in both disciplines and the development of new satellite remote sensing products.     

FAIR degree assessment in agriculture datasets using the F-UJI tool

For the agricultural scientific community, data sharing is crucial both for the advancement of the discipline and ability to meet global challenges, such as the target no. 2, i.e., “Zero Hunger,” of the Sustainable Development Goals (SDG 2030). In this context, FAIR (Findable, Accessible, Interoperable and Reusable) principles play an important role, as they guarantee the findability, accessibility, interoperability, and reusability of shared data. To improve the practice of data sharing, institutions, funders, and publishers are increasingly demanding data be shared as well as be of an acceptable level of quality, including compliance with FAIR principles. Therefore, the objective of this work is twofold: first, this research aims to determine the degree of compliance with the FAIR principles exhibited by a number of datasets; and second, it aims to explore useful and valid methodologies and procedures that can be used to perform this evaluation quickly, automatically, and effectively. For this purpose, the Data Citation Index (DCI) was used to obtain many datasets in the field of agriculture, which were further grouped by repositories and evaluated using the automated assessment tool F-UJI provided by the FAIRsFAIR project. The results indicated that the principle that exhibited the highest scores was “Findable”, while “Reusable” received the lowest scores, as none of the analysed repositories achieved a 50% compliance score in this respect. The datasets published in the Zenodo and Dryad repositories exhibited better overall results in terms of the FAIR principles, and the AG Commons repository was the third best rated repository, representing only one of the first three repositories belonging to the agricultural sector. Regarding the use of F-UJI as an automated assessment tool and DCI as a source for obtaining datasets, we conclude that this methodology is useful, and that although it can be improved, it is easy to use and implement by other scientific groups and agents of interest.     

我国城市可持续发展能力评估指标的元数据分析与管理

在我国大力推动城市可持续发展,推进国家可持续发展实验区建设的同时,采用何种评估方法和数据开展城市可持续发展能力评估是需要重点解决的问题。近年来兴起的元数据理论与技术在解决评估数据质量控制方面被视为是一种行之有效的方法。针对我国现阶段使用的一些城市可持续发展能力评估指标体系的特点,通过深入剖析每一个指标数据的来源、获取手段、适用方法等特征,提出从软件工程学思路研发城市可持续发展能力评估元数据管理系统的具体方法,帮助可持续发展实验区高效获取和管理评估所需数据信息;以"十二五"科技支撑计划项目"城市可持续发展能力评估及信息管理关键技术研究与示范"中所建立的元数据规范,对其所包含的"数据发布日期"、"数据发布形式"、"空间范围"、"时间范围(起始时间、结束时间)"、"统计频率"、"数据安全限制分级"、"数据志说明"、"在线资源链接地址"和"数据统计单位信息(单位名称、联络人、联系电话、单位地址、邮件地址)"共14项为评估数据的关键元数据项,以此追踪对标的评估数据。并通过量化数据质量评分法针对数据质量在运用元数据追踪法前后的评价结果对比发现,被评估指标的数据质量在获得元数据支持时,其数据可靠性、可比性和可持续性方面的评价分值都获得了十分显著的改善。研究认为采用元数据理论在控制和保障城市可持续发展能力评估数据质量方面具有优势作用,开发有针对性的城市可持续发展能力评估元数据管理系统能够有效提高评估数据的综合评价结果。     

AutoFAIR-A portal for automating FAIR assessments for bioinformatics resources

BackgroundResearch in Bioinformatics generates tools and datasets in Bioinformatics at a very fast rate. Meanwhile, a lot of effort is going into making these resources findable and reusable to improve resource discovery by researchers in the course of their work.PurposeThis paper proposes a semi-automated tool to assess a resource according to the Findability, Accessibility, Interoperability and Reusability (FAIR) criteria. The aim is to create a portal that presents the assessment score together with a report that researchers can use to gauge a resource.MethodOur system uses internet searches to automate the process of generating FAIR scores. The process is semi-automated in that if a particular property of the FAIR scores has not been captured by AutoFAIR, a user is able to amend and supply the information to complete the assessment.ResultsWe compare our results against FAIRshake that was used as the benchmark tool for comparing the assessments. The results show that AutoFAIR was able to match the FAIR criteria in FAIRshake with minimal intervention from the user.ConclusionsWe show that AutoFAIR can be a good repository for storing metadata about tools and datasets, together with comprehensive reports detailing the assessments of the resources. Moreover, AutoFAIR is also able to score workflows, giving an overall indication of the FAIRness of the resources used in a scientific study.     

openPDS: Protecting the Privacy of Metadata through SafeAnswers

The rise of smartphones and web services made possible the large-scale collection of personal metadata. Information about individuals'' location, phone call logs, or web-searches, is collected and used intensively by organizations and big data researchers. Metadata has however yet to realize its full potential. Privacy and legal concerns, as well as the lack of technical solutions for personal metadata management is preventing metadata from being shared and reconciled under the control of the individual. This lack of access and control is furthermore fueling growing concerns, as it prevents individuals from understanding and managing the risks associated with the collection and use of their data. Our contribution is two-fold: (1) we describe openPDS, a personal metadata management framework that allows individuals to collect, store, and give fine-grained access to their metadata to third parties. It has been implemented in two field studies; (2) we introduce and analyze SafeAnswers, a new and practical way of protecting the privacy of metadata at an individual level. SafeAnswers turns a hard anonymization problem into a more tractable security one. It allows services to ask questions whose answers are calculated against the metadata instead of trying to anonymize individuals'' metadata. The dimensionality of the data shared with the services is reduced from high-dimensional metadata to low-dimensional answers that are less likely to be re-identifiable and to contain sensitive information. These answers can then be directly shared individually or in aggregate. openPDS and SafeAnswers provide a new way of dynamically protecting personal metadata, thereby supporting the creation of smart data-driven services and data science research.     

Overview of FEED, the feeding experiments end-user database

The Feeding Experiments End-user Database (FEED) is a research tool developed by the Mammalian Feeding Working Group at the National Evolutionary Synthesis Center that permits synthetic, evolutionary analyses of the physiology of mammalian feeding. The tasks of the Working Group are to compile physiologic data sets into a uniform digital format stored at a central source, develop a standardized terminology for describing and organizing the data, and carry out a set of novel analyses using FEED. FEED contains raw physiologic data linked to extensive metadata. It serves as an archive for a large number of existing data sets and a repository for future data sets. The metadata are stored as text and images that describe experimental protocols, research subjects, and anatomical information. The metadata incorporate controlled vocabularies to allow consistent use of the terms used to describe and organize the physiologic data. The planned analyses address long-standing questions concerning the phylogenetic distribution of phenotypes involving muscle anatomy and feeding physiology among mammals, the presence and nature of motor pattern conservation in the mammalian feeding muscles, and the extent to which suckling constrains the evolution of feeding behavior in adult mammals. We expect FEED to be a growing digital archive that will facilitate new research into understanding the evolution of feeding anatomy.     

A metadata-driven approach to loading and querying heterogeneous scientific data

The Ecological Metadata Language is an effective specification for describing data for long-term storage and interpretation. When used in conjunction with a metadata repository such as Metacat, and a metadata editing tool such as Morpho, the Ecological Metadata Language allows a large community of researchers to access and to share their data. Although the Ecological Metadata Language/Morpho/Metacat toolkit provides a rich data documentation mechanism, current methods for retrieving metadata-described data can be laborious and time consuming. Moreover, the structural and semantic heterogeneity of ecological data sets makes the development of custom solutions for integrating and querying these data prohibitively costly for large-scale synthesis. The Data Manager Library leverages the Ecological Metadata Language to provide automated data processing features that allow efficient data access, querying, and manipulation without custom development. The library can be used for many data management tasks and was designed to be immediately useful as well as extensible and easy to incorporate within existing applications. In this paper we describe the motivation for developing the Data Manager Library, provide an overview of its implementation, illustrate ideas for potential use by describing several planned and existing deployments, and describe future work to extend the library.     

Xeml Lab: a tool that supports the design of experiments at a graphical interface and generates computer-readable metadata files, which capture information about genotypes, growth conditions, environmental perturbations and sampling strategy

Data mining depends on the ability to access machine-readable metadata that describe genotypes, environmental conditions, and sampling times and strategy. This article presents Xeml Lab. The Xeml Interactive Designer provides an interactive graphical interface at which complex experiments can be designed, and concomitantly generates machine-readable metadata files. It uses a new eXtensible Mark-up Language (XML)-derived dialect termed XEML. Xeml Lab includes a new ontology for environmental conditions, called Xeml Environment Ontology. However, to provide versatility, it is designed to be generic and also accepts other commonly used ontology formats, including OBO and OWL. A review summarizing important environmental conditions that need to be controlled, monitored and captured as metadata is posted in a Wiki ( http://www.codeplex.com/XeO ) to promote community discussion. The usefulness of Xeml Lab is illustrated by two meta-analyses of a large set of experiments that were performed with Arabidopsis thaliana during 5 years. The first reveals sources of noise that affect measurements of metabolite levels and enzyme activities. The second shows that Arabidopsis maintains remarkably stable levels of sugars and amino acids across a wide range of photoperiod treatments, and that adjustment of starch turnover and the leaf protein content contribute to this metabolic homeostasis.     

Towards a unified data infrastructure to support European and global microbiome research: a call to action

High-quality microbiome research relies on the integrity, management and quality of supporting data. Currently biobanks and culture collections have different formats and approaches to data management. This necessitates a standard data format to underpin research, particularly in line with the FAIR data standards of findability, accessibility, interoperability and reusability. We address the importance of a unified, coordinated approach that ensures compatibility of data between that needed by biobanks and culture collections, but also to ensure linkage between bioinformatic databases and the wider research community.     

Conceptualizing a Genomics Software Institute (GSI)

Microbial ecology has been enhanced greatly by the ongoing 'omics revolution, bringing half the world's biomass and most of its biodiversity into analytical view for the first time; indeed, it feels almost like the invention of the microscope and the discovery of the new world at the same time. With major microbial ecology research efforts accumulating prodigious quantities of sequence, protein, and metabolite data, we are now poised to address environmental microbial research at macro scales, and to begin to characterize and understand the dimensions of microbial biodiversity on the planet. What is currently impeding progress is the need for a framework within which the research community can develop, exchange and discuss predictive ecosystem models that describe the biodiversity and functional interactions. Such a framework must encompass data and metadata transparency and interoperation; data and results validation, curation, and search; application programming interfaces for modeling and analysis tools; and human and technical processes and services necessary to ensure broad adoption. Here we discuss the need for focused community interaction to augment and deepen established community efforts, beginning with the Genomic Standards Consortium (GSC), to create a science-driven strategic plan for a Genomic Software Institute (GSI).     

Botanical information in the Italian Biodiversity Network: One year of data aggregation and future perspectives

Since its formal institution in May 2012, the Italian Biodiversity Network is aggregating data from several research centres, for a total of ca. 1.5 million records. Botanical data made ca. 50% of the total, and range from primary biodiversity data to ecological, morpho-anatomical and taxonomical information. However, this is only a small portion of the total amount of botanical data which could be aggregated and exposed to the scientific community, professionals and citizens. In this paper we present the current status of data aggregation of the Network and its impact on digitalisation of biodiversity data, research, conservation and environmental management and education in Italy.