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| 1 | FAIR Principles:Interpretations and Implementation Considerations显示文摘The FAIR principles have been widely cited,endorsed and adopted by a broad range of stakeholders since their publication in 2016.By intention,the 15 FAIR guiding principles do not dictate specific technological implementations,but provide guidance for improving Findability,Accessibility,Interoperability and Reusability of digital resources.This has likely contributed to the broad adoption of the FAIR principles,because individual stakeholder communities can implement their own FAIR solutions.However,it has also resulted in inconsistent interpretations that carry the risk of leading to incompatible implementations.Thus,while the FAIR principles are formulated on a high level and may be interpreted and implemented in different ways,for true interoperability we need to support convergence in implementation choices that are widely accessible and(re)-usable.We introduce the concept of FAIR implementation considerations to assist accelerated global participation and convergence towards accessible,robust,widespread and consistent FAIR implementations.Any self-identified stakeholder community may either choose to reuse solutions from existing implementations,or when they spot a gap,accept the challenge to create the needed solution,which,ideally,can be used again by other communities in the future.Here,we provide interpretations and implementation considerations(choices and challenges)for each FAIR principle. | Annika Jacobsen Ricardo de Miranda Azevedo Nick Juty Dominique Batista Simon Coles Ronald Cornet Melanie Courtot Merce Crosas Michel Dumontier Chris T.Evelo Carole Goble Giancarlo Guizzardi Karsten Kryger Hansen Ali Hasnain Kristina Hettne Jaap Heringa Rob W.W.Hooft Melanie Imming Keith G.Jeffery Rajaram Kaliyaperumal Martijn GKersloot Christine R.Kirkpatrick Tobias Kuhn Ignasi Labastida Barbara Magagna PeterMcQuilton Natalie Meyers Annalisa Montesanti Mirjam van Reisen Philippe Rocca-Serra Robert Pergl Susanna-Assunta Sansone Luiz Olavo Bonino da Silva Santos Juliane Schneider George Strawn Mark Thompson Andra Waagmeester Tobias Weigel Mark D.Wilkinson Egon L.Willighagen Peter Wittenburg Marco Roos Barend Mons Erik Schultes | 2020 | Data Intelligence2020，2，1： | 26 |
| 2 | FAIR Data and Services in Biodiversity Science and Geoscience显示文摘We examine the intersection of the FAIR principles(Findable,Accessible,Interoperable and Reusable),the challenges and opportunities presented by the aggregation of widely distributed and heterogeneous data about biological and geological specimens,and the use of the Digital Object Architecture(DOA)data model and components as an approach to solving those challenges that offers adherence to the FAIR principles as an integral characteristic.This approach will be prototyped in the Distributed System of Scientific Collections(DiSSCo)project,the pan-European Research Infrastructure which aims to unify over 110 natural science collections across 21 countries.We take each of the FAIR principles,discuss them as requirements in the creation of a seamless virtual collection of bio/geo specimen data,and map those requirements to Digital Object components and facilities such as persistent identification,extended data typing,and the use of an additional level of abstraction to normalize existing heterogeneous data structures.The FAIR principles inform and motivate the work and the DO Architecture provides the technical vision to create the seamless virtual collection vitally needed to address scientific questions of societal importance. | Larry Lannom Dimitris Koureas Alex R.Hardisty | 2020 | Data Intelligence2020，2，1： | 12 |
| 3 | AMiner:Search and Mining of Academic Social Networks显示文摘AMiner is a novel online academic search and mining system,and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and heterogeneous networks formed by authors,papers,conferences,journals and organizations.The system is subsequently able to extract researchers’profiles automatically from the Web and integrates them with published papers by a way of a process that first performs name disambiguation.Then a generative probabilistic model is devised to simultaneously model the different entities while providing a topic-level expertise search.In addition,AMiner offers a set of researcher-centered functions,including social influence analysis,relationship mining,collaboration recommendation,similarity analysis and community evolution.The system has been in operation since 2006 and has been accessed from more than 8 million independent IP addresses residing in more than 200 countries and regions. | Huaiyu Wan Yutao Zhang Jing Zhang Jie Tang | 2019 | Data Intelligence2019，1，1： | 11 |
| 4 | Unique,Persistent,Resolvable:Identifiers as the Foundation of FAIR显示文摘The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem.Persistent,globally unique identifiers,resolvable on the Web,and associated with a set of additional descriptive metadata,are foundational to FAIR data.Here we describe some basic principles and exemplars for their design,use and orchestration with other system elements to achieve FAIRness for digital research objects. | Nick Juty Sarala M.Wimalaratne Stian Soiland-Reyes John Kunze Carole A.Goble Tim Clark | 2020 | Data Intelligence2020，2，1： | 11 |
| 5 | CN-DBpedia2: An Extraction and Verification Framework for Enriching Chinese Encyclopedia Knowledge Base显示文摘Knowledge base plays an important role in machine understanding and has been widely used in various applications, such as search engine, recommendation system and question answering. However, most knowledge bases are incomplete, which can cause many downstream applications to perform poorly because they cannot find the corresponding facts in the knowledge bases. In this paper, we propose an extraction and verification framework to enrich the knowledge bases. Specifically, based on the existing knowledge base, we first extract new facts from the description texts of entities. But not all newly-formed facts can be added directly to the knowledge base because the errors might be involved by the extraction. Then we propose a novel crowd-sourcing based verification step to verify the candidate facts. Finally, we apply this framework to the existing knowledge base CN-DBpedia and construct a new version of knowledge base CN-DBpedia2, which additionally contains the high confidence facts extracted from the description texts of entities. | Bo Xu Jiaqing Liang Chenhao Xie Bin Liang Lihan Chen Yanghua Xiao | 2019 | Data Intelligence2019，1，3： | 9 |
| 6 | Ontology-based Access Control for FAIR Data显示文摘This paper focuses on fine-grained,secure access to FAIR data,for which we propose ontology-based data access policies.These policies take into account both the FAIR aspects of the data relevant to access(such as provenance and licence),expressed as metadata,and additional metadata describing users.With this tripartite approach(data,associated metadata expressing FAIR information,and additional metadata about users),secure and controlled access to object data can be obtained.This yields a security dimension to the“A”(accessible)in FAIR,which is clearly needed in domains like security and intelligence.These domains need data to be shared under tight controls,with widely varying individual access rights.In this paper,we propose an approach called Ontology-Based Access Control(OBAC),which utilizes concepts and relations from a data set's domain ontology.We argue that ontology-based access policies contribute to data reusability and can be reconciled with privacy-aware data access policies.We illustrate our OBAC approach through a proof-of-concept and propose that OBAC to be adopted as a best practice for access management of FAIR data. | Christopher Brewster Barry Nouwt Stephan Raaijmakers Jack Verhoosel | 2020 | Data Intelligence2020，2，1： | 8 |
| 7 | FAIR Science for Social Machines: Let’s Share Metadata Knowlets in the Internet of FAIR Data and Services显示文摘In a world awash with fragmented data and tools,the notion of Open Science has been gaining a lot of momentum,but simultaneously,it caused a great deal of anxiety.Some of the anxiety may be related to crumbling kingdoms,but there are also very legitimate concerns,especially about the relative role of machines and algorithms as compared to humans and the combination of both(i.e.,social machines).There are also grave concerns about the connotations of the term“open”,but also regarding the unwanted side effects as well as the scalability of the approaches advocated by early adopters of new methodological developments.Many of these concerns are associated with mind-machine interaction and the critical role that computers are now playing in our day to day scientific practice.Here we address a number of these concerns and provide some possible solutions.FAIR(machine-actionable)data and services are obviously at the core of Open Science(or rather FAIR science).The scalable and transparent routing of data,tools and compute(to run the tools on)is a key central feature of the envisioned Internet of FAIR Data and Services(IFDS).Both the European Commission in its Declaration on the European Open Science Cloud,the G7,and the USA data commons have identified the need to ensure a solid and sustainable infrastructure for Open Science.Here we first define the term FAIR science as opposed to Open Science.In FAIR science,data and the associated tools are all Findable,Accessible under well defined conditions,Interoperable and Reusable,but not necessarily“open”;without restrictions and certainly not always“gratis”.The ambiguous term“open”has already caused considerable confusion and also opt-out reactions from researchers and other data-intensive professionals who cannot make their data open for very good reasons,such as patient privacy or national security.Although Open Science is a definition for a way of working rather than explicitly requesting for all data to be available in full Open Access, the connotation of openness of the data involved in Open Science is very strong. In FAIR science, data and the associated services to run all processes in the data stewardship cycle from design of experiment to capture to curation, processing, linking and analytics all have minimally FAIR metadata, which specify the conditions under which the actual underlying research objects are reusable, first for machines and then also for humans. This effectively means that-properly conducted- Open Science is part of FAIR science. However, FAIR science can also be done with partly closed, sensitive and proprietary data. As has been emphasized before, FAIR is not identical to “open”. In FAIR/Open Science, data should be as open as possible and as closed as necessary. Where data are generated using public funding, the default will usually be that for the FAIR data resulting from the study the accessibility will be as high as possible, and that more restrictive access and licensing policies on these data will have to be explicitly justified and described. In all cases, however, even if the reuse is restricted, data and related services should be findable for their major uses, machines, which will make them also much better findable for human users. With a tendency to make good data stewardship the norm, a very significant new market for distributed data analytics and learning is opening and a plethora of tools and reusable data objects are being developed and released. These all need FAIR metadata to be routed to each other and to be effective. | Barend Mons | 2019 | Data Intelligence2019，1，1： | 8 |
| 8 | FAIR Data Reuse-the Path through Data Citation显示文摘One of the key goals of the FAIR guiding principles is defined by its final principle-to optimize data sets for reuse by both humans and machines.To do so,data providers need to implement and support consistent machine readable metadata to describe their data sets.This can seem like a daunting task for data providers,whether it is determining what level of detail should be provided in the provenance metadata or figuring out what common shared vocabularies should be used.Additionally,for existing data sets it is often unclear what steps should be taken to enable maximal,appropriate reuse.Data citation already plays an important role in making data findable and accessible,providing persistent and unique identifiers plus metadata on over 16 million data sets.In this paper,we discuss how data citation and its underlying infrastructures,in particular associated metadata,provide an important pathway for enabling FAIR data reuse. | Paul Groth Helena Cousijn Tim Clark Carole Goble | 2020 | Data Intelligence2020，2，1： | 8 |
| 9 | Not Ready for Convergence in Data Infrastructures显示文摘Much research is dependent on Information and Communication Technologies(ICT).Researchers in different research domains have set up their own ICT systems(data labs)to support their research,from data collection(observation,experiment,simulation)through analysis(analytics,visualisation)to publication.However,too frequently the Digital Objects(DOs)upon which the research results are based are not curated and thus neither available for reproduction of the research nor utilization for other(e.g.,multidisciplinary)research purposes.The key to curation is rich metadata recording not only a description of the DO and the conditions of its use but also the provenance-the trail of actions performed on the DO along the research workflow.There are increasing real-world requirements for multidisciplinary research.With DOs in domain-specific ICT systems(silos),commonly with inadequate metadata,such research is hindered.Despite wide agreement on principles for achieving FAIR(findable,accessible,interoperable,and reusable)utilization of research data,current practices fall short.FAIR DOs offer a way forward.The paradoxes,barriers and possible solutions are examined.The key is persuading the researcher to adopt best practices which implies decreasing the cost(easy to use autonomic tools)and increasing the benefit(incentives such as acknowledgement and citation)while maintaining researcher independence and flexibility. | Keith Jeffery Peter Wittenburg Larry Lannom George Strawn Claudia Biniossek Dirk Betz Christophe Blanchi | 2021 | Data Intelligence2021，3，1： | 7 |
| 10 | Data Management Planning:How Requirements and Solutions are Beginning to Converge显示文摘Effective stewardship of data is a critical precursor to making data FAIR.The goal of this paper is to bring an overview of current state of the art of data management and data stewardship planning solutions(DMP).We begin by arguing why data management is an important vehicle supporting adoption and implementation of the FAIR principles,we describe the background,context and historical development,as well as major driving forces,being research initiatives and funders.Then we provide an overview of the current leading DMP tools in the form of a table presenting the key characteristics.Next,we elaborate on emerging common standards for DMPs,especially the topic of machine-actionable DMPs.As sound DMP is not only a precursor of FAIR data stewardship,but also an integral part of it,we discuss its positioning in the emerging FAIR tools ecosystem.Capacity building and training activities are an important ingredient in the whole effort.Although not being the primary goal of this paper,we touch also the topic of research workforce support,as tools can be just as much effective as their users are competent to use them properly.We conclude by discussing the relations of DMP to FAIR principles,as there are other important connections than just being a precursor. | Sarah Jones Robert Pergl Rob Hooft Tomasz Miksa Robert Samors Judit Ungvari Rowena I.Davis Tina Lee | 2020 | Data Intelligence2020，2，1： | 7 |
| 11 | OpenKG Chain:A Blockchain Infrastructure for Open Knowledge Graphs显示文摘The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between things.Currently publishing knowledge bases as open data on the Web has gained significant attention.In China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge Graphs.Unlike existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge infrastructure.This article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust network.We have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple level.We have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,000.For the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity level.At present,all operations on the data sets and tool sets at OpenKG.CN,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted mode.Via this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community. | Huajun Chen Ning Hu Guilin Qi Haofen Wang Zhen Bi Jie Li Fan Yang | 2021 | Data Intelligence2021，3，2： | 7 |
| 12 | Overview of CCKS 2020 Task 3: Named Entity Recognition and Event Extraction in Chinese Electronic Medical Records显示文摘The China Conference on Knowledge Graph and Semantic Computing(CCKS)2020 Evaluation Task 3 presented clinical named entity recognition and event extraction for the Chinese electronic medical records.Two annotated data sets and some other additional resources for these two subtasks were provided for participators.This evaluation competition attracted 354 teams and 46 of them successfully submitted the valid results.The pre-trained language models are widely applied in this evaluation task.Data argumentation and external resources are also helpful. | Xia Li Qinghua Wen Hu Lin Zengtao Jiao Jiangtao Zhang | 2021 | Data Intelligence2021，3，3： | 6 |
| 13 | Microsoft Concept Graph:Mining Semantic Concepts for Short Text Understanding显示文摘Knowlege is important for text-related applications.In this paper,we introduce Microsoft Concept Graph,a knowledge graph engine that provides concept tagging APIs to facilitate the understanding of human languages.Microsoft Concept Graph is built upon Probase,a universal probabilistic taxonomy consisting of instances and concepts mined from the Web.We start by introducing the construction of the knowledge graph through iterative semantic extraction and taxonomy construction procedures,which extract 2.7 million concepts from 1.68 billion Web pages.We then use conceptualization models to represent text in the concept space to empower text-related applications,such as topic search,query recommendation,Web table understanding and Ads relevance.Since the release in 2016,Microsoft Concept Graph has received more than 100,000 pageviews,2 million API calls and 3,000 registered downloads from 50,000 visitors over 64 countries. | Lei Ji Yujing Wang Botian Shi Dawei Zhang Zhongyuan Wang Jun Yan | 2019 | Data Intelligence2019，1，3： | 6 |
| 14 | Joint Entity and Event Extraction with Generative Adversarial Imitation Learning显示文摘We propose a new framework for entity and event extraction based on generative adversarial imitation learning-an inverse reinforcement learning method using a generative adversarial network(GAN).We assume that instances and labels yield to various extents of difficulty and the gains and penalties(rewards)are expected to be diverse.We utilize discriminators to estimate proper rewards according to the difference between the labels committed by the ground-truth(expert)and the extractor(agent).Our experiments demonstrate that the proposed framework outperforms state-of-the-art methods. | Tongtao Zhang Heng Ji Avirup Sil | 2019 | Data Intelligence2019，1，2： | 6 |
| 15 | How to(Easily)Extend the FAIRness of Existing Repositories显示文摘Data repository infrastructures for academics have appeared in waves since the dawn of Web technology.These waves are driven by changes in societal needs,archiving needs and the development of cloud computing resources.As such,the data repository landscape has many flavors when it comes to sustainability models,target audiences and feature sets.One thing that links all data repositories is a desire to make the content they host reusable,building on the core principles of cataloging content for economical and research speed efficiency.The FAIR principles are a common goal for all repository infrastructures to aim for.No matter what discipline or infrastructure,the goal of reusable content,for both humans and machines,is a common one.This is the first time that repositories can work toward a common goal that ultimately lends itself to interoperability.The idea that research can move further and faster as we un-silo these fantastic resources is an achievable one.This paper investigates the steps that existing repositories need to take in order to remain useful and relevant in a FAIR research world. | Mark Hahnel Dan Valen | 2020 | Data Intelligence2020，2，1： | 6 |
| 16 | Helping the Consumers and Producers of Standards,Repositories and Policies to Enable FAIR Data显示文摘Thousands of community-developed(meta)data guidelines,models,ontologies,schemas and formats have been created and implemented by several thousand data repositories and knowledge-bases,across all disciplines.These resources are necessary to meet government,funder and publisher expectations of greater transparency and access to and preservation of data related to research publications.This obligates researchers to ensure their data is FAIR,share their data using the appropriate standards,store their data in sustainable and community-adopted repositories,and to conform to funder and publisher data policies.FAIR data sharing also plays a key role in enabling researchers to evaluate,re-analyse and reproduce each other’s work.We can map the landscape of relationships between community-adopted standards and repositories,and the journal publisher and funder data policies that recommend their use.In this paper,we show how the work of the GO-FAIR FAIR Standards,Repositories and Policies(StRePo)Implementation Network serves as a central integration and cross-fertilisation point for the reuse of FAIR standards,repositories and data policies in general.Pivotal to this effort,the FAIRsharing,an endorsed flagship resource of the Research Data Alliance that maps the landscape of relationships between community-adopted standards and repositories,and the journal publisher and funder data policies that recommend their use.Lastly,we highlight a number of activities around FAIR tools,services and educational efforts to raise awareness and encourage participation. | Peter McQuilton Dominique Batista Oya Beyan Ramon Granell Simon Coles Massimiliano Izzo Allyson L.Lister Robert Pergl Philippe Rocca-Serra Ben Schaap Hugh Shanahan Milo Thurston Susanna-Assunta Sansone | 2020 | Data Intelligence2020，2，1： | 5 |
| 17 | Research Data Management Implementation at Peking University Library:Foster and Promote Open Science and Open Data显示文摘Research Data Management(RDM)has become increasingly important for more and more academic institutions.Using the Peking University Open Research Data Repository(PKU-ORDR)project as an example,this paper will review a library-based university-wide open research data repository project and related RDM services implementation process including project kickoff,needs assessment,partnerships establishment,software investigation and selection,software customization,as well as data curation services and training.Through the review,some issues revealed during the stages of the implementation process are also discussed and addressed in the paper such as awareness of research data,demands from data providers and users,data policies and requirements from home institution,requirements from funding agencies and publishers,the collaboration between administrative units and libraries,and concerns from data providers and users.The significance of the study is that the paper shows an example of creating an Open Data repository and RDM services for other Chinese academic libraries planning to implement their RDM services for their home institutions.The authors of the paper have also observed since the PKU-ORDR and RDM services implemented in 2015,the Peking University Library(PKUL)has helped numerous researchers to support the entire research life cycle and enhanced Open Science(OS)practices on campus,as well as impacted the national OS movement in China through various national events and activities hosted by the PKUL. | Hua Nie Pengcheng Luo Ping Fu | 2021 | Data Intelligence2021，3，1： | 5 |
| 18 | Considerations for the Conduction and Interpretation of FAIRness Evaluations显示文摘The FAIR principles were received with broad acceptance in several scientific communities.However,there is still some degree of uncertainty on how they should be implemented.Several self-report questionnaires have been proposed to assess the implementation of the FAIR principles.Moreover,the FAIRmetrics group released 14,general-purpose maturity for representing FAIRness.Initially,these metrics were conducted as open-answer questionnaires.Recently,these metrics have been implemented into a software that can automatically harvest metadata from metadata providers and generate a principle-specific FAIRness evaluation.With so many different approaches for FAIRness evaluations,we believe that further clarification on their limitations and advantages,as well as on their interpretation and interplay should be considered. | Ricardo de Miranda Azevedo Michel Dumontier | 2020 | Data Intelligence2020，2，1： | 5 |
| 19 | XLORE2: Large-scale Cross-lingual Knowledge Graph Construction and Application显示文摘Knowledge bases(KBs)are often greatly incomplete,necessitating a demand for KB completion.Although XLORE is an English-Chinese bilingual knowledge graph,there are only 423,974 cross-lingual links between English instances and Chinese instances.We present XLORE2,an extension of the XLORE that is built automatically from Wikipedia,Baidu Baike and Hudong Baike.We add more facts by making cross-lingual knowledge linking,cross-lingual property matching and fine-grained type inference.We also design an entity linking system to demonstrate the effectiveness and broad coverage of XLORE2. | Hailong Jin Chengjiang Li Jing Zhang Lei Hou Juanzi Li | 2019 | Data Intelligence2019，1，1： | 5 |
| 20 | A Generic Workflow for the Data FAIRification Process显示文摘The FAIR guiding principles aim to enhance the Findability,Accessibility,Interoperability and Reusability of digital resources such as data,for both humans and machines.The process of making data FAIR(“FAIRification”)can be described in multiple steps.In this paper,we describe a generic step-by-step FAIRification workflow to be performed in a multidisciplinary team guided by FAIR data stewards.The FAIRification workflow should be applicable to any type of data and has been developed and used for“Bring Your Own Data”(BYOD)workshops,as well as for the FAIRification of e.g.,rare diseases resources.The steps are:1)identify the FAIRification objective,2)analyze data,3)analyze metadata,4)define semantic model for data(4a)and metadata(4b),5)make data(5a)and metadata(5b)linkable,6)host FAIR data,and 7)assess FAIR data.For each step we describe how the data are processed,what expertise is required,which procedures and tools can be used,and which FAIR principles they relate to. | Annika Jacobsen Rajaram Kaliyaperumal Luiz Olavo Bonino da Silva Santos Barend Mons Erik Schultes Marco Roos Mark Thompson | 2020 | Data Intelligence2020，2，1： | 5 |
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