With the rise of data-intensive research,data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence(AI)readiness.In the biomedical domain,data are charac...With the rise of data-intensive research,data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence(AI)readiness.In the biomedical domain,data are characterized by high complexity and privacy sensitivity,calling for robust and systematic data management skills.This paper reviews current trends in scientific data governance and the evolving policy landscape,highlighting persistent challenges such as inconsistent standards,semantic misalignment,and limited awareness of compliance.These issues are largely rooted in the lack of structured training and practical support for researchers.In response,this study builds on existing data literacy frameworks and integrates the specific demands of biomedical research to propose a comprehensive,lifecycle-oriented data literacy competency model with an emphasis on ethics and regulatory awareness.Furthermore,it outlines a tiered training strategy tailored to different research stages—undergraduate,graduate,and professional,offering theoretical foundations and practical pathways for universities and research institutions to advance data literacy education.展开更多
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 implem...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.展开更多
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 bee...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.展开更多
In this paper, we study resource management models and algorithms that satisfy multiple performance objects simultaneously. We realize the proportional fairness principle based QoS model, which defines both delay and ...In this paper, we study resource management models and algorithms that satisfy multiple performance objects simultaneously. We realize the proportional fairness principle based QoS model, which defines both delay and loss rate requirements of a class, to include fairness, which is important for the integration of multiple service classes. The resulting Proportional Fairness Scheduling model formalizes the goals of the network performance, user’s QoS requirement and system fairness and exposes the fundamental tradeoffs between these goals. In particular, it is difficult to simultaneously provide these objects. We propose a novel scheduling algorithm called Proportional Fairness Scheduling (PFS) that approximates the model closely and efficiently. We have implemented the PFS scheduling in Linux. By performing simulation and measurement experiments, we evaluate the delay and loss rate proportional fairness of PFS, and determine the computation overhead.展开更多
In the dynamic environment of hospitals, valuable real-world data often remain underutilised despite their potential to revolutionize cancer research and personalised medicine. This study explores the challenges and o...In the dynamic environment of hospitals, valuable real-world data often remain underutilised despite their potential to revolutionize cancer research and personalised medicine. This study explores the challenges and opportunities in managing hospital-generated data, particularly within the Masaryk Memorial Cancer Institute (MMCI) in Brno, Czech Republic. Utilizing Next-Generation Sequencing (NGS) technology, MMCI generates substantial volumes of genomic data. Due to inadequate curation, these data remain difficult to integrate with clinical records for secondary use (such as personalised treatment outcome prediction and patient stratification based on their genomic profiles). This paper proposes solutions based on the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) to enhance data sharing and reuse. The primary output of our work is the development of an automated pipeline that continuously processes and integrates NGS data with clinical and biobank information upon their creation. It stores the data in a special secured repository for sensitive data in a structured form to ensure smooth retrieval.展开更多
Minerals,like many other natural materials of geological origin(i.e.,geomaterials),face the challenge of name variations.This in turn hinders the data-intensive geoscience research,which often needs to integrate data ...Minerals,like many other natural materials of geological origin(i.e.,geomaterials),face the challenge of name variations.This in turn hinders the data-intensive geoscience research,which often needs to integrate data from multiple sources.It is clear that mineral name is not an appropriate identifier to connect records within and amongst data sources.The Mindat database,as one of the biggest resources for open data in mineralogy,has received significant volume of feedback on the heterogeneity of mineral and rock names.To address that issue,we established a persistent identifier service on Mindat to provide persistent and meaningful access to the records of geomaterials(mineral/rock/variety),localities,mineral occurrences,references,photos,and specimens.A key development was the long-form identifier,which adds contextual information such as identifier authorities and data types into the identifier structure.Moreover,a UUID service was built along with the long-form identifier to further increase the interoperability.The identifier service has been successfully implemented to mint millions of identifiers to different types of data objects on Mindat.Several use case scenarios were developed to illustrate the utility of the identifiers in the real world.We believe the persistent identifier will help address the challenges caused by name variations,and we welcome Mindat users to test the identifiers and send feedback to us for future extensions.展开更多
To tackle the planetary environmental and climate crisis and meet the United Nations’Sustainable Development Goals(SDGs),we must fully leverage the potential of Earth observations(EO).This involves integrating global...To tackle the planetary environmental and climate crisis and meet the United Nations’Sustainable Development Goals(SDGs),we must fully leverage the potential of Earth observations(EO).This involves integrating globally sourced data on the atmosphere,hydrosphere,cryosphere,lithosphere,along with ecological and socio-economic information.By harmonizing and integrating these diverse data sources,we can more effectively incorporate observational data into multi-scale modeling and artificial intelligence(AI)frameworks.This paper is based on discussions from the“Towards Global Earth Observatory”workshop held from May 8-10,2023,organized by the World Meteorological Organization(WMO)and the Atmosphere and Climate Competence Center(ACCC),in collaboration with the Institute for Atmospheric and Earth System Research(INAR)at the University of Helsinki.The current state of EO and data repositories is fragmented,highlighting the need for a more integrated approach to establish a new global Ground-Based Earth Observatory(GGBEO).Here,we summarize the current status of selected in-situ and ground-based remote sensing observation systems and outline future actions and recommendations to meet scientific,societal,and economic needs.In addition,we identify key steps to create a coordinated and comprehensive GGBEO system that leverages existing investments,networks,and infrastructures.This system would integrate regional and global ground-based in situ and remote sensing systems,marine,and airborne observational data.An integrated approach should aim for seamless coordination,interoperable and harmonized data repositories,easily searchable and accessible data,and sustainable long-term funding.展开更多
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 kingdo...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.展开更多
Metadata,data about other digital objects,play an important role in FAIR with a direct relation to all FAIR principles.In this paper we present and discuss the FAIR Data Point(FDP),a software architecture aiming to de...Metadata,data about other digital objects,play an important role in FAIR with a direct relation to all FAIR principles.In this paper we present and discuss the FAIR Data Point(FDP),a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR principles.We present the core components and features of the FDP,its approach to metadata provision,the criteria to evaluate whether an application adheres to the FDP specifications and the service to register,index and allow users to search for metadata content of available FDPs.展开更多
The FAIR principles,an acronym for Findable,Accessible,Interoperable and Reusable,are recognised worldwide as key elements for good practice in all data management processes.To understand how the Brazilian scientific ...The FAIR principles,an acronym for Findable,Accessible,Interoperable and Reusable,are recognised worldwide as key elements for good practice in all data management processes.To understand how the Brazilian scientific community is adhering to these principles,this article reports Brazilian adherence to the GO FAIR initiative through the creation of the GO FAIR Brazil Office and the manner in which they create their implementation networks.To contextualise this understanding,we provide a brief presentation of open data policies in Brazilian research and government,and finally,we describe a model that has been adopted for the GO FAIR Brazil implementation networks.The Brazilian Institute of Information in Science and Technology is responsible for the GO FAIR Brazil Office,which operates in all fields of knowledge and supports thematic implementation networks.Today,GO FAIR Brazil-Health is the first active implementation network in operation,which works in all health domains,serving as a model for other fields like agriculture,nuclear energy,and digital humanities,which are in the process of adherence negotiation.This report demonstrates the strong interest and effort from the Brazilian scientific communities in implementing the FAIR principles in their research data management practices.展开更多
The FAIR principles have been accepted globally as guidelines for improving data-driven science and data management practices,yet the incentives for researchers to change their practices are presently weak.In addition...The FAIR principles have been accepted globally as guidelines for improving data-driven science and data management practices,yet the incentives for researchers to change their practices are presently weak.In addition,data-driven science has been slow to embrace workflow technology despite clear evidence of recurring practices.To overcome these challenges,the Canonical Workflow Frameworks for Research(CWFR)initiative suggests a large-scale introduction of self-documenting workflow scripts to automate recurring processes or fragments thereof.This standardised approach,with FAIR Digital Objects as anchors,will be a significant milestone in the transition to FAIR data without adding additional load onto the researchers who stand to benefit most from it.This paper describes the CWFR approach and the activities of the CWFR initiative over the course of the last year or so,highlights several projects that hold promise for the CWFR approaches,including Galaxy,Jupyter Notebook,and RO Crate,and concludes with an assessment of the state of the field and the challenges ahead.展开更多
The open science movement has gained significant momentum within the last few years.This comes along with the need to store and share research artefacts,such as publications and research data.For this purpose,research...The open science movement has gained significant momentum within the last few years.This comes along with the need to store and share research artefacts,such as publications and research data.For this purpose,research repositories need to be established.A variety of solutions exist for implementing such repositories,covering diverse features,ranging from custom depositing workflows to social media-like functions.In this article,we introduce the FAIREST principles,a framework inspired by the well-known FAIR principles,but designed to provide a set of metrics for assessing and selecting solutions for creating digital repositories for research artefacts.The goal is to support decision makers in choosing such a solution when planning for a repository,especially at an institutional level.The metrics included are therefore based on two pillars:(1)an analysis of established features and functionalities,drawn from existing dedicated,general purpose and commonly used solutions,and(2)a literature review on general requirements for digital repositories for research artefacts and related systems.We further describe an assessment of 11 widespread solutions,with the goal to provide an overview of the current landscape of research data repository solutions,identifying gaps and research challenges to be addressed.展开更多
The rapid evolution of Large Language Models(LLMs) highlights the necessity for ethical considerations and data integrity in AI development, particularly emphasizing the role of FAIR(Findable, Accessible, Interoperabl...The rapid evolution of Large Language Models(LLMs) highlights the necessity for ethical considerations and data integrity in AI development, particularly emphasizing the role of FAIR(Findable, Accessible, Interoperable, Reusable) data principles. While these principles are crucial for ethical data stewardship, their specific application in the context of LLM training data remains an under-explored area. This research gap is the focus of our study, which begins with an examination of existing literature to underline the importance of FAIR principles in managing data for LLM training. Building upon this, we propose a novel frame-work designed to integrate FAIR principles into the LLM development lifecycle. A contribution of our work is the development of a comprehensive checklist intended to guide researchers and developers in applying FAIR data principles consistently across the model development process. The utility and effectiveness of our frame-work are validated through a case study on creating a FAIR-compliant dataset aimed at detecting and mitigating biases in LLMs. We present this framework to the community as a tool to foster the creation of technologically advanced, ethically grounded, and socially responsible AI models.展开更多
The investigation proposes the application of an ontological semantic approach to describing workflow control patterns,research workflow step patterns,and the meaning of the workflows in terms of domain knowledge.The ...The investigation proposes the application of an ontological semantic approach to describing workflow control patterns,research workflow step patterns,and the meaning of the workflows in terms of domain knowledge.The approach can provide wide opportunities for semantic refinement,reuse,and composition of workflows.Automatic reasoning allows verifying those compositions and implementations and provides machine-actionable workflow manipulation and problem-solving using workflows.The described approach can take into account the implementation of workflows in different workflow management systems,the organization of workflows collections in data infrastructures and the search for them,the semantic approach to the selection of workflows and resources in the research domain,the creation of research step patterns and their implementation reusing fragments of existing workflows,the possibility of automation of problemsolving based on the reuse of workflows.The application of the approach to CWFR conceptions is proposed.展开更多
In this article we analyse 3D models of cultural heritage with the aim of answering three main questions:what processes can be put in place to create a FAIR-by-design digital twin of a temporary exhibition?What are th...In this article we analyse 3D models of cultural heritage with the aim of answering three main questions:what processes can be put in place to create a FAIR-by-design digital twin of a temporary exhibition?What are the main challenges in applying FAIR principles to 3D data in cultural heritage studies and how are they different from other types of data(e.g.images)from a data management perspective?We begin with a comprehensive literature review touching on:FAIR principles applied to cultural heritage data;representation models;both Object Provenance Information(OPI)and Metadata Record Provenance Information(MRPI),respectively meant as,on the one hand,the detailed history and origin of an object,and-on the other hand-the detailed history and origin of the metadata itself,which describes the primary object(whether physical or digital);3D models as cultural heritage research data and their creation,selection,publication,archival and preservation.We then describe the process of creating the Aldrovandi Digital Twin,by collecting,storing and modelling data about cultural heritage objects and processes.We detail the many steps from the acquisition of the Digital Cultural Heritage Objects(DCHO),through to the upload of the optimised DCHO onto a web-based framework(ATON),with a focus on open technologies and standards for interoperability and preservation.Using the FAIR Principles for Heritage Library,Archive and Museum Collections[1]as a framework,we look in detail at how the Digital Twin implements FAIR principles at the object and metadata level.We then describe the main challenges we encountered and we summarise what seem to be the peculiarities of 3D cultural heritage data and the possible directions for further research in this field.展开更多
文摘With the rise of data-intensive research,data literacy has become a critical capability for improving scientific data quality and achieving artificial intelligence(AI)readiness.In the biomedical domain,data are characterized by high complexity and privacy sensitivity,calling for robust and systematic data management skills.This paper reviews current trends in scientific data governance and the evolving policy landscape,highlighting persistent challenges such as inconsistent standards,semantic misalignment,and limited awareness of compliance.These issues are largely rooted in the lack of structured training and practical support for researchers.In response,this study builds on existing data literacy frameworks and integrates the specific demands of biomedical research to propose a comprehensive,lifecycle-oriented data literacy competency model with an emphasis on ethics and regulatory awareness.Furthermore,it outlines a tiered training strategy tailored to different research stages—undergraduate,graduate,and professional,offering theoretical foundations and practical pathways for universities and research institutions to advance data literacy education.
基金The work of A.Jacobsen,C.Evelo,M.Thompson,R.Cornet,R.Kaliyaperuma and M.Roos is supported by funding from the European Union’s Horizon 2020 research and innovation program under the EJP RD COFUND-EJP N°825575.The work of A.Jacobsen,C.Evelo,C.Goble,M.Thompson,N.Juty,R.Hooft,M.Roos,S-A.Sansone,P.McQuilton,P.Rocca-Serra and D.Batista is supported by funding from ELIXIR EXCELERATE,H2020 grant agreement number 676559.R.Hooft was further funded by NL NWO NRGWI.obrug.2018.009.N.Juty and C.Goble were funded by CORBEL(H2020 grant agreement 654248)N.Juty,C.Goble,S-A.Sansone,P.McQuilton,P.Rocca-Serra and D.Batista were funded by FAIRplus(IMI grant agreement 802750)+13 种基金N.Juty,C.Goble,M.Thompson,M.Roos,S-A.Sansone,P.McQuilton,P.Rocca-Serra and D.Batista were funded by EOSClife H2020-EU(grant agreement number 824087)C.Goble was funded by DMMCore(BBSRC BB/M013189/)M.Thompson,M.Roos received funding from NWO(VWData 400.17.605)S-A.Sansone,P.McQuilton,P.Rocca-Serra and D.Batista have been funded by grants awarded to S-A.Sansone from the UK BBSRC and Research Councils(BB/L024101/1,BB/L005069/1)EU(H2020-EU 634107H2020-EU 654241,IMI(IMPRiND 116060)NIH Data Common Fund,and from the Wellcome Trust(ISA-InterMine 212930/Z/18/ZFAIRsharing 208381/A/17/Z)The work of A.Waagmeester has been funded by grant award number GM089820 from the National Institutes of Health.M.Kersloot was funded by the European Regional Development Fund(KVW-00163).The work of N.Meyers was funded by the National Science Foundation(OAC 1839030)The work of M.D.Wilkinson is funded by Isaac Peral/Marie Curie cofund with the Universidad Politecnica de Madrid and the Ministerio de Economia y Competitividad grant number TIN2014-55993-RMThe work of B.Magagna,E.Schultes,L.da Silva Santos and K.Jeffery is funded by the H2020-EU 824068The work of B.Magagna,E.Schultes and L.da Silva Santos is funded by the GO FAIR ISCO grant of the Dutch Ministry of Science and CultureThe work of G.Guizzardi is supported by the OCEAN Project(FUB).M.Courtot received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No.802750.R.Cornet was further funded by FAIR4Health(H2020-EU grant agreement number 824666)K.Jeffery received funding from EPOS-IP H2020-EU agreement 676564 and ENVRIplus H2020-EU agreement 654182.
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.
基金M.Dumontier was supported by grants from NWO(400.17.605628.011.011)+5 种基金NIH(3OT3TR002027-01S11OT3OD025467-011OT3OD025464-01)H2020-EU EOSClife(824087)ELIXIR,the research infrastructure for life-science data.R.de Miranda Azevedo was supported by grants from H2020-EU EOSClife(824087)ELIXIR,the research infrastructure for life-science data.
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.
基金supported by the National Natural Science Foundation of China(Grant Nos.90104002,69725003)
文摘In this paper, we study resource management models and algorithms that satisfy multiple performance objects simultaneously. We realize the proportional fairness principle based QoS model, which defines both delay and loss rate requirements of a class, to include fairness, which is important for the integration of multiple service classes. The resulting Proportional Fairness Scheduling model formalizes the goals of the network performance, user’s QoS requirement and system fairness and exposes the fundamental tradeoffs between these goals. In particular, it is difficult to simultaneously provide these objects. We propose a novel scheduling algorithm called Proportional Fairness Scheduling (PFS) that approximates the model closely and efficiently. We have implemented the PFS scheduling in Linux. By performing simulation and measurement experiments, we evaluate the delay and loss rate proportional fairness of PFS, and determine the computation overhead.
基金funding from the project SALVAGE(P JACreg.no.CZ.02.01.01/00/22_008/0004644)-funded by the European Union and by the State Budget of the Czech Republic,from MH CZ-DRO(MMCI,00209805)+1 种基金BBMRI.cz(no.LM2023033)Computational resources were provided by the e-INFRA CZ project(no.LM2023054).
文摘In the dynamic environment of hospitals, valuable real-world data often remain underutilised despite their potential to revolutionize cancer research and personalised medicine. This study explores the challenges and opportunities in managing hospital-generated data, particularly within the Masaryk Memorial Cancer Institute (MMCI) in Brno, Czech Republic. Utilizing Next-Generation Sequencing (NGS) technology, MMCI generates substantial volumes of genomic data. Due to inadequate curation, these data remain difficult to integrate with clinical records for secondary use (such as personalised treatment outcome prediction and patient stratification based on their genomic profiles). This paper proposes solutions based on the FAIR principles (Findability, Accessibility, Interoperability, and Reusability) to enhance data sharing and reuse. The primary output of our work is the development of an automated pipeline that continuously processes and integrates NGS data with clinical and biobank information upon their creation. It stores the data in a special secured repository for sensitive data in a structured form to ensure smooth retrieval.
基金funded by the U.S.National Science Foundation(Grant No.2126315).
文摘Minerals,like many other natural materials of geological origin(i.e.,geomaterials),face the challenge of name variations.This in turn hinders the data-intensive geoscience research,which often needs to integrate data from multiple sources.It is clear that mineral name is not an appropriate identifier to connect records within and amongst data sources.The Mindat database,as one of the biggest resources for open data in mineralogy,has received significant volume of feedback on the heterogeneity of mineral and rock names.To address that issue,we established a persistent identifier service on Mindat to provide persistent and meaningful access to the records of geomaterials(mineral/rock/variety),localities,mineral occurrences,references,photos,and specimens.A key development was the long-form identifier,which adds contextual information such as identifier authorities and data types into the identifier structure.Moreover,a UUID service was built along with the long-form identifier to further increase the interoperability.The identifier service has been successfully implemented to mint millions of identifiers to different types of data objects on Mindat.Several use case scenarios were developed to illustrate the utility of the identifiers in the real world.We believe the persistent identifier will help address the challenges caused by name variations,and we welcome Mindat users to test the identifiers and send feedback to us for future extensions.
基金supported by the Atmosphere and Climate Competence Center(ACCC)Flagship,funded by the Research Council of Finland(Contract No(s).337549,357902,359340).
文摘To tackle the planetary environmental and climate crisis and meet the United Nations’Sustainable Development Goals(SDGs),we must fully leverage the potential of Earth observations(EO).This involves integrating globally sourced data on the atmosphere,hydrosphere,cryosphere,lithosphere,along with ecological and socio-economic information.By harmonizing and integrating these diverse data sources,we can more effectively incorporate observational data into multi-scale modeling and artificial intelligence(AI)frameworks.This paper is based on discussions from the“Towards Global Earth Observatory”workshop held from May 8-10,2023,organized by the World Meteorological Organization(WMO)and the Atmosphere and Climate Competence Center(ACCC),in collaboration with the Institute for Atmospheric and Earth System Research(INAR)at the University of Helsinki.The current state of EO and data repositories is fragmented,highlighting the need for a more integrated approach to establish a new global Ground-Based Earth Observatory(GGBEO).Here,we summarize the current status of selected in-situ and ground-based remote sensing observation systems and outline future actions and recommendations to meet scientific,societal,and economic needs.In addition,we identify key steps to create a coordinated and comprehensive GGBEO system that leverages existing investments,networks,and infrastructures.This system would integrate regional and global ground-based in situ and remote sensing systems,marine,and airborne observational data.An integrated approach should aim for seamless coordination,interoperable and harmonized data repositories,easily searchable and accessible data,and sustainable long-term funding.
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.
文摘Metadata,data about other digital objects,play an important role in FAIR with a direct relation to all FAIR principles.In this paper we present and discuss the FAIR Data Point(FDP),a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR principles.We present the core components and features of the FDP,its approach to metadata provision,the criteria to evaluate whether an application adheres to the FDP specifications and the service to register,index and allow users to search for metadata content of available FDPs.
文摘The FAIR principles,an acronym for Findable,Accessible,Interoperable and Reusable,are recognised worldwide as key elements for good practice in all data management processes.To understand how the Brazilian scientific community is adhering to these principles,this article reports Brazilian adherence to the GO FAIR initiative through the creation of the GO FAIR Brazil Office and the manner in which they create their implementation networks.To contextualise this understanding,we provide a brief presentation of open data policies in Brazilian research and government,and finally,we describe a model that has been adopted for the GO FAIR Brazil implementation networks.The Brazilian Institute of Information in Science and Technology is responsible for the GO FAIR Brazil Office,which operates in all fields of knowledge and supports thematic implementation networks.Today,GO FAIR Brazil-Health is the first active implementation network in operation,which works in all health domains,serving as a model for other fields like agriculture,nuclear energy,and digital humanities,which are in the process of adherence negotiation.This report demonstrates the strong interest and effort from the Brazilian scientific communities in implementing the FAIR principles in their research data management practices.
文摘The FAIR principles have been accepted globally as guidelines for improving data-driven science and data management practices,yet the incentives for researchers to change their practices are presently weak.In addition,data-driven science has been slow to embrace workflow technology despite clear evidence of recurring practices.To overcome these challenges,the Canonical Workflow Frameworks for Research(CWFR)initiative suggests a large-scale introduction of self-documenting workflow scripts to automate recurring processes or fragments thereof.This standardised approach,with FAIR Digital Objects as anchors,will be a significant milestone in the transition to FAIR data without adding additional load onto the researchers who stand to benefit most from it.This paper describes the CWFR approach and the activities of the CWFR initiative over the course of the last year or so,highlights several projects that hold promise for the CWFR approaches,including Galaxy,Jupyter Notebook,and RO Crate,and concludes with an assessment of the state of the field and the challenges ahead.
基金supported by the Fundacao para a Ciencia e a Tecnologia through the LASIGE Research DB/00408/2020,UIDP/00408/2020supported by the Federal Ministry of Education and Research of Germany(BMBF)un no.16Dll128("Deutsches Internet-Institut").
文摘The open science movement has gained significant momentum within the last few years.This comes along with the need to store and share research artefacts,such as publications and research data.For this purpose,research repositories need to be established.A variety of solutions exist for implementing such repositories,covering diverse features,ranging from custom depositing workflows to social media-like functions.In this article,we introduce the FAIREST principles,a framework inspired by the well-known FAIR principles,but designed to provide a set of metrics for assessing and selecting solutions for creating digital repositories for research artefacts.The goal is to support decision makers in choosing such a solution when planning for a repository,especially at an institutional level.The metrics included are therefore based on two pillars:(1)an analysis of established features and functionalities,drawn from existing dedicated,general purpose and commonly used solutions,and(2)a literature review on general requirements for digital repositories for research artefacts and related systems.We further describe an assessment of 11 widespread solutions,with the goal to provide an overview of the current landscape of research data repository solutions,identifying gaps and research challenges to be addressed.
文摘The rapid evolution of Large Language Models(LLMs) highlights the necessity for ethical considerations and data integrity in AI development, particularly emphasizing the role of FAIR(Findable, Accessible, Interoperable, Reusable) data principles. While these principles are crucial for ethical data stewardship, their specific application in the context of LLM training data remains an under-explored area. This research gap is the focus of our study, which begins with an examination of existing literature to underline the importance of FAIR principles in managing data for LLM training. Building upon this, we propose a novel frame-work designed to integrate FAIR principles into the LLM development lifecycle. A contribution of our work is the development of a comprehensive checklist intended to guide researchers and developers in applying FAIR data principles consistently across the model development process. The utility and effectiveness of our frame-work are validated through a case study on creating a FAIR-compliant dataset aimed at detecting and mitigating biases in LLMs. We present this framework to the community as a tool to foster the creation of technologically advanced, ethically grounded, and socially responsible AI models.
基金the Russian Foundation for Basic Research,grants 19-07-01198,18-29-22096.
文摘The investigation proposes the application of an ontological semantic approach to describing workflow control patterns,research workflow step patterns,and the meaning of the workflows in terms of domain knowledge.The approach can provide wide opportunities for semantic refinement,reuse,and composition of workflows.Automatic reasoning allows verifying those compositions and implementations and provides machine-actionable workflow manipulation and problem-solving using workflows.The described approach can take into account the implementation of workflows in different workflow management systems,the organization of workflows collections in data infrastructures and the search for them,the semantic approach to the selection of workflows and resources in the research domain,the creation of research step patterns and their implementation reusing fragments of existing workflows,the possibility of automation of problemsolving based on the reuse of workflows.The application of the approach to CWFR conceptions is proposed.
基金partially funded by Project PE 0000020 CHANGES-CUP B53C22003780006NRP Mission 4 Component 2 Investment 1.3,Funded by the European Union-NextGenerationEU
文摘In this article we analyse 3D models of cultural heritage with the aim of answering three main questions:what processes can be put in place to create a FAIR-by-design digital twin of a temporary exhibition?What are the main challenges in applying FAIR principles to 3D data in cultural heritage studies and how are they different from other types of data(e.g.images)from a data management perspective?We begin with a comprehensive literature review touching on:FAIR principles applied to cultural heritage data;representation models;both Object Provenance Information(OPI)and Metadata Record Provenance Information(MRPI),respectively meant as,on the one hand,the detailed history and origin of an object,and-on the other hand-the detailed history and origin of the metadata itself,which describes the primary object(whether physical or digital);3D models as cultural heritage research data and their creation,selection,publication,archival and preservation.We then describe the process of creating the Aldrovandi Digital Twin,by collecting,storing and modelling data about cultural heritage objects and processes.We detail the many steps from the acquisition of the Digital Cultural Heritage Objects(DCHO),through to the upload of the optimised DCHO onto a web-based framework(ATON),with a focus on open technologies and standards for interoperability and preservation.Using the FAIR Principles for Heritage Library,Archive and Museum Collections[1]as a framework,we look in detail at how the Digital Twin implements FAIR principles at the object and metadata level.We then describe the main challenges we encountered and we summarise what seem to be the peculiarities of 3D cultural heritage data and the possible directions for further research in this field.
