In this study,we present an investigation of the newly discovered dwarf nova ASASSN-19oc during its superoutburst on 2019 June 2.We carried out detailed UBVRcIc-photometric observations and also obtained a spectrum on...In this study,we present an investigation of the newly discovered dwarf nova ASASSN-19oc during its superoutburst on 2019 June 2.We carried out detailed UBVRcIc-photometric observations and also obtained a spectrum on day 7 of the outburst,which shows the presence of hydrogen absorption lines commonly found in dwarf nova outbursts.Analysis of photometric data reveals the occurrence of early superhumps in the initial days of observations,followed by ordinary and late superhumps.We have accurately calculated the period of the ordinary superhumps as Pord=0.05681(10)days and determined the periods at different stages,as well as the rate of change of the superhump period(P_(dot)=(5)P/P=8.1×10^(-5)).Additionally,we have derived the mass ratio of the components(q=0.09),and estimated the color temperature during the outburst as~11,000 K,the distance to the system(d=560 pc)and absolute magnitude of the system in outburst(MV=5.3).We have shown that outbursts of this star are very rare:based on brightness measurements on 600 archival photographic plates,we found only one outburst that occurred in 1984.This fact,as well as the properties listed above,convincingly shows that the variable ASASSN-19oc is a dwarf nova of WZ Sge type.展开更多
Data Science(DS)as defined by Jim Gray is an emerging paradigm in all research areas to help finding non-obvious patterns of relevance in large distributed data collections.“Open Science by Design”(OSD),i.e.,making ...Data Science(DS)as defined by Jim Gray is an emerging paradigm in all research areas to help finding non-obvious patterns of relevance in large distributed data collections.“Open Science by Design”(OSD),i.e.,making artefacts such as data,metadata,models,and algorithms available and re-usable to peers and beyond as early as possible,is a pre-requisite for a flourishing DS landscape.However,a few major aspects can be identified hampering a fast transition:(1)The classical“Open Science by Publication”(OSP)is not sufficient any longer since it serves different functions,leads to non-acceptable delays and is associated with high curation costs.Changing data lab practices towards OSD requires more fundamental changes than OSP.(2)The classical publication-oriented models for metrics,mainly informed by citations,will not work anymore since the roles of contributors are more difficult to assess and will often change,i.e.,other ways for assigning incentives and recognition need to be found.(3)The huge investments in developing DS skills and capacities by some global companies and strong countries is leading to imbalances and fears by different stakeholders hampering the acceptance of Open Science(OS).(4)Finally,OSD will depend on the availability of a global infrastructure fostering an integrated and interoperable data domain-“one data-domain”as George Strawn calls it-which is still not visible due to differences about the technological key pillars.OS therefore is a need for DS,but it will take much more time to implement it than we may have expected.展开更多
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(observ...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.展开更多
Data-intensive science is reality in large scientific organizations such as the Max Planck Society,but due to the inefficiency of our data practices when it comes to integrating data from different sources,many projec...Data-intensive science is reality in large scientific organizations such as the Max Planck Society,but due to the inefficiency of our data practices when it comes to integrating data from different sources,many projects cannot be carried out and many researchers are excluded.Since about 80%of the time in data-intensive projects is wasted according to surveys we need to conclude that we are not fit for the challenges that will come with the billions of smart devices producing continuous streams of data-our methods do not scale.Therefore experts worldwide are looking for strategies and methods that have a potential for the future.The first steps have been made since there is now a wide agreement from the Research Data Alliance to the FAIR principles that data should be associated with persistent identifiers(PID)and metadata(MD).In fact after 20 years of experience we can claim that there are trustworthy PID systems already in broad use.It is argued,however,that assigning PIDs is just the first step.If we agree to assign PIDs and also use the PID to store important relationships such as pointing to locations where the bit sequences or different metadata can be accessed,we are close to defining Digital Objects(DOs)which could indeed indicate a solution to solve some of the basic problems in data management and processing.In addition to standardizing the way we assign PIDs,metadata and other state information we could also define a Digital Object Access Protocol as a universal exchange protocol for DOs stored in repositories using different data models and data organizations.We could also associate a type with each DO and a set of operations allowed working on its content which would facilitate the way to automatic processing which has been identified as the major step for scalability in data science and data industry.A globally connected group of experts is now working on establishing testbeds for a DO-based data infrastructure.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
1.PREFACE Coming from an institute that was devoted to analysing data streams of different sorts from its beginning to understand how the human brain is processing language and how language is supporting cognition,bui...1.PREFACE Coming from an institute that was devoted to analysing data streams of different sorts from its beginning to understand how the human brain is processing language and how language is supporting cognition,building efficient data infrastructures of different scope was a key to research excellence.While first local infrastructures were sufficient,it became apparent in the 90s that local data would not be sufficient anymore to satisfy all research needs.It was a logical step to first take responsibilities in setting up the specific DOBES(Dokumentation bedrohter Sprachen)infrastructure focussing on languages of the world。展开更多
The FAIR principles articulate the behaviors expected from digital artifacts that are Findable,Accessible,Interoperable and Reusable by machines and by people.Although by now widely accepted,the FAIR Principles by des...The FAIR principles articulate the behaviors expected from digital artifacts that are Findable,Accessible,Interoperable and Reusable by machines and by people.Although by now widely accepted,the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors.As different communities have their own,often well-established implementation preferences and priorities for data reuse,coordinating a broadly accepted,widely used FAIR implementation approach remains a global challenge.In an effort to accelerate broad community convergence on FAIR implementation options,the GO FAIR community has launched the development of the FAIR Convergence Matrix.The Matrix is a platform that compiles for any community of practice,an inventory of their self-declared FAIR implementation choices and challenges.The Convergence Matrix is itself a FAIR resource,openly available,and encourages voluntary participation by any self-identified community of practice(not only the GO FAIR Implementation Networks).Based on patterns of use and reuse of existing resources,the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services.展开更多
Nanoscale L12-type ordered structures are widely used in face-centered cubic(FCC)alloys to exploit their hardening capacity and thereby improve mechanical properties.These fine-scale particles are typically fully cohe...Nanoscale L12-type ordered structures are widely used in face-centered cubic(FCC)alloys to exploit their hardening capacity and thereby improve mechanical properties.These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species,which makes them challenging to be characterized.Spatial distribution maps(SDMs)are used to probe local order by interrogating the three-dimensional(3D)distribution of atoms within reconstructed atom probe tomography(APT)data.However,it is almost impossible to manually analyze the complete point cloud(>10 million)in search for the partial crystallographic information retained within the data.Here,we proposed an intelligent L1_(2)-ordered structure recognition method based on convolutional neural networks(CNNs).The SDMs of a simulated L1_(2)-ordered structure and the FCC matrix were firstly generated.These simulated images combined with a small amount of experimental data were used to train a CNN-based L1_(2)-ordered structure recognition model.Finally,the approach was successfully applied to reveal the 3D distribution of L1_(2)–typeδ′–Al3(LiMg)nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system.The minimum radius of detectable nanodomain is even down to 5Å.The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.展开更多
Since 2009 initiatives that were selected for the roadmap of the European Strategy Forum on Research Infrastructures started working to build research infrastructures for a wide range of research disciplines.An import...Since 2009 initiatives that were selected for the roadmap of the European Strategy Forum on Research Infrastructures started working to build research infrastructures for a wide range of research disciplines.An important result of the strategic discussions was that distributed infrastructure scenarios were now seen as“complex research facilities”in addition to,for example traditional centralised infrastructures such as CERN.In this paper we look at five typical examples of such distributed infrastructures where many researchers working in different centres are contributing data,tools/services and knowledge and where the major task of the research infrastructure initiative is to create a virtually integrated suite of resources allowing researchers to carry out state-of-the-art research.Careful analysis shows that most of these research infrastructures worked on the Findability,Accessibility,Interoperability and Reusability dimensions before the term“FAIR”was actually coined.The definition of the FAIR principles and their wide acceptance can be seen as a confirmation of what these initiatives were doing and it gives new impulse to close still existing gaps.These initiatives also seem to be ready to take up the next steps which will emerge from the definition of FAIR maturity indicators.Experts from these infrastructures should bring in their 10-years’experience in this definition process.展开更多
Institutions driving fundamental research at the cutting edge such as for example from the Max Planck Society(MPS)took steps to optimize data management and stewardship to be able to address new scientific questions.I...Institutions driving fundamental research at the cutting edge such as for example from the Max Planck Society(MPS)took steps to optimize data management and stewardship to be able to address new scientific questions.In this paper we selected three institutes from the MPS from the areas of humanities,environmental sciences and natural sciences as examples to indicate the efforts to integrate large amounts of data from collaborators worldwide to create a data space that is ready to be exploited to get new insights based on data intensive science methods.For this integration the typical challenges of fragmentation,bad quality and also social differences had to be overcome.In all three cases,well-managed repositories that are driven by the scientific needs and harmonization principles that have been agreed upon in the community were the core pillars.It is not surprising that these principles are very much aligned with what have now become the FAIR principles.The FAIR principles confirm the correctness of earlier decisions and their clear formulation identified the gaps which the projects need to address.展开更多
InCanonicalWorkflowFramework forResearch(CWFR)"packages"arerelevantin twodifferentdirections.In data science,workflows are in general being executed on a set of files which have been aggregated for specific ...InCanonicalWorkflowFramework forResearch(CWFR)"packages"arerelevantin twodifferentdirections.In data science,workflows are in general being executed on a set of files which have been aggregated for specific purposes,such as for training a model in deep learning.We call this type of"package"a data collection and its aggregation and metadata description is motivated by research interests.The other type of"packages"relevant for CWFR are supposed to represent workflows in a self-describing and self-contained way for later execution.In this paper,we will review different packaging technologies and investigate their usability in the context of CWFR.For this purpose,we draw on an exemplary use case and show how packaging technologies can support its realization.We conclude that packaging technologies of different flavors help on providing inputs and outputs for workflow steps in a machine-readable way,as well as on representing a workflow and all its artifacts in a self-describing and self-contained way.展开更多
In 2019 the German Leibniz research organization sponsored a conference on Open Science(OS)with the idea to publish some of the presented papers in the Data Intelligence journal.Becoming engaged as editors,we recogniz...In 2019 the German Leibniz research organization sponsored a conference on Open Science(OS)with the idea to publish some of the presented papers in the Data Intelligence journal.Becoming engaged as editors,we recognized that the term“Open Science”was coined about 10 years ago with the intention as pointed out by Michael Nielson:“OS is the idea that scientific knowledge of all kinds should be openly shared as early as is practical in the discovery process”.展开更多
基金financial support of the APVV-20-0148,VEGA 2/0030/21 and VEGA 2/0031/22grantssupport from the Government Office of the Slovak Republic within EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No.09I03-03-V01-00002the private company 4pi Systeme GmbH for partial sponsorship。
文摘In this study,we present an investigation of the newly discovered dwarf nova ASASSN-19oc during its superoutburst on 2019 June 2.We carried out detailed UBVRcIc-photometric observations and also obtained a spectrum on day 7 of the outburst,which shows the presence of hydrogen absorption lines commonly found in dwarf nova outbursts.Analysis of photometric data reveals the occurrence of early superhumps in the initial days of observations,followed by ordinary and late superhumps.We have accurately calculated the period of the ordinary superhumps as Pord=0.05681(10)days and determined the periods at different stages,as well as the rate of change of the superhump period(P_(dot)=(5)P/P=8.1×10^(-5)).Additionally,we have derived the mass ratio of the components(q=0.09),and estimated the color temperature during the outburst as~11,000 K,the distance to the system(d=560 pc)and absolute magnitude of the system in outburst(MV=5.3).We have shown that outbursts of this star are very rare:based on brightness measurements on 600 archival photographic plates,we found only one outburst that occurred in 1984.This fact,as well as the properties listed above,convincingly shows that the variable ASASSN-19oc is a dwarf nova of WZ Sge type.
文摘Data Science(DS)as defined by Jim Gray is an emerging paradigm in all research areas to help finding non-obvious patterns of relevance in large distributed data collections.“Open Science by Design”(OSD),i.e.,making artefacts such as data,metadata,models,and algorithms available and re-usable to peers and beyond as early as possible,is a pre-requisite for a flourishing DS landscape.However,a few major aspects can be identified hampering a fast transition:(1)The classical“Open Science by Publication”(OSP)is not sufficient any longer since it serves different functions,leads to non-acceptable delays and is associated with high curation costs.Changing data lab practices towards OSD requires more fundamental changes than OSP.(2)The classical publication-oriented models for metrics,mainly informed by citations,will not work anymore since the roles of contributors are more difficult to assess and will often change,i.e.,other ways for assigning incentives and recognition need to be found.(3)The huge investments in developing DS skills and capacities by some global companies and strong countries is leading to imbalances and fears by different stakeholders hampering the acceptance of Open Science(OS).(4)Finally,OSD will depend on the availability of a global infrastructure fostering an integrated and interoperable data domain-“one data-domain”as George Strawn calls it-which is still not visible due to differences about the technological key pillars.OS therefore is a need for DS,but it will take much more time to implement it than we may have expected.
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.
文摘Data-intensive science is reality in large scientific organizations such as the Max Planck Society,but due to the inefficiency of our data practices when it comes to integrating data from different sources,many projects cannot be carried out and many researchers are excluded.Since about 80%of the time in data-intensive projects is wasted according to surveys we need to conclude that we are not fit for the challenges that will come with the billions of smart devices producing continuous streams of data-our methods do not scale.Therefore experts worldwide are looking for strategies and methods that have a potential for the future.The first steps have been made since there is now a wide agreement from the Research Data Alliance to the FAIR principles that data should be associated with persistent identifiers(PID)and metadata(MD).In fact after 20 years of experience we can claim that there are trustworthy PID systems already in broad use.It is argued,however,that assigning PIDs is just the first step.If we agree to assign PIDs and also use the PID to store important relationships such as pointing to locations where the bit sequences or different metadata can be accessed,we are close to defining Digital Objects(DOs)which could indeed indicate a solution to solve some of the basic problems in data management and processing.In addition to standardizing the way we assign PIDs,metadata and other state information we could also define a Digital Object Access Protocol as a universal exchange protocol for DOs stored in repositories using different data models and data organizations.We could also associate a type with each DO and a set of operations allowed working on its content which would facilitate the way to automatic processing which has been identified as the major step for scalability in data science and data industry.A globally connected group of experts is now working on establishing testbeds for a DO-based data infrastructure.
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.
文摘1.PREFACE Coming from an institute that was devoted to analysing data streams of different sorts from its beginning to understand how the human brain is processing language and how language is supporting cognition,building efficient data infrastructures of different scope was a key to research excellence.While first local infrastructures were sufficient,it became apparent in the 90s that local data would not be sufficient anymore to satisfy all research needs.It was a logical step to first take responsibilities in setting up the specific DOBES(Dokumentation bedrohter Sprachen)infrastructure focussing on languages of the world。
基金FAIRsharing is funded by grants awarded to SAS that include elements of this workspecifically,grants from the UK BBSRC and Research Councils(BB/L024101/1,BB/L005069/1)+1 种基金European Union(H2020-EU.3.1,634107,H2020-EU.1.4.1.3,654241,H2020-EU.1.4.1.1,676559),IMI(116060)and NIH(U54 AI117925,1U24AI117966-01,1OT3OD025459-01,1OT3OD025467-01,1OT3OD025462-01)the new FAIRsharing award from the Wellcome Trust(212930/Z/18/Z),as well as a related award(208381/A/17/Z).
文摘The FAIR principles articulate the behaviors expected from digital artifacts that are Findable,Accessible,Interoperable and Reusable by machines and by people.Although by now widely accepted,the FAIR Principles by design do not explicitly consider actual implementation choices enabling FAIR behaviors.As different communities have their own,often well-established implementation preferences and priorities for data reuse,coordinating a broadly accepted,widely used FAIR implementation approach remains a global challenge.In an effort to accelerate broad community convergence on FAIR implementation options,the GO FAIR community has launched the development of the FAIR Convergence Matrix.The Matrix is a platform that compiles for any community of practice,an inventory of their self-declared FAIR implementation choices and challenges.The Convergence Matrix is itself a FAIR resource,openly available,and encourages voluntary participation by any self-identified community of practice(not only the GO FAIR Implementation Networks).Based on patterns of use and reuse of existing resources,the Convergence Matrix supports the transparent derivation of strategies that optimally coordinate convergence on standards and technologies in the emerging Internet of FAIR Data and Services.
文摘Nanoscale L12-type ordered structures are widely used in face-centered cubic(FCC)alloys to exploit their hardening capacity and thereby improve mechanical properties.These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species,which makes them challenging to be characterized.Spatial distribution maps(SDMs)are used to probe local order by interrogating the three-dimensional(3D)distribution of atoms within reconstructed atom probe tomography(APT)data.However,it is almost impossible to manually analyze the complete point cloud(>10 million)in search for the partial crystallographic information retained within the data.Here,we proposed an intelligent L1_(2)-ordered structure recognition method based on convolutional neural networks(CNNs).The SDMs of a simulated L1_(2)-ordered structure and the FCC matrix were firstly generated.These simulated images combined with a small amount of experimental data were used to train a CNN-based L1_(2)-ordered structure recognition model.Finally,the approach was successfully applied to reveal the 3D distribution of L1_(2)–typeδ′–Al3(LiMg)nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system.The minimum radius of detectable nanodomain is even down to 5Å.The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.
文摘Since 2009 initiatives that were selected for the roadmap of the European Strategy Forum on Research Infrastructures started working to build research infrastructures for a wide range of research disciplines.An important result of the strategic discussions was that distributed infrastructure scenarios were now seen as“complex research facilities”in addition to,for example traditional centralised infrastructures such as CERN.In this paper we look at five typical examples of such distributed infrastructures where many researchers working in different centres are contributing data,tools/services and knowledge and where the major task of the research infrastructure initiative is to create a virtually integrated suite of resources allowing researchers to carry out state-of-the-art research.Careful analysis shows that most of these research infrastructures worked on the Findability,Accessibility,Interoperability and Reusability dimensions before the term“FAIR”was actually coined.The definition of the FAIR principles and their wide acceptance can be seen as a confirmation of what these initiatives were doing and it gives new impulse to close still existing gaps.These initiatives also seem to be ready to take up the next steps which will emerge from the definition of FAIR maturity indicators.Experts from these infrastructures should bring in their 10-years’experience in this definition process.
文摘Institutions driving fundamental research at the cutting edge such as for example from the Max Planck Society(MPS)took steps to optimize data management and stewardship to be able to address new scientific questions.In this paper we selected three institutes from the MPS from the areas of humanities,environmental sciences and natural sciences as examples to indicate the efforts to integrate large amounts of data from collaborators worldwide to create a data space that is ready to be exploited to get new insights based on data intensive science methods.For this integration the typical challenges of fragmentation,bad quality and also social differences had to be overcome.In all three cases,well-managed repositories that are driven by the scientific needs and harmonization principles that have been agreed upon in the community were the core pillars.It is not surprising that these principles are very much aligned with what have now become the FAIR principles.The FAIR principles confirm the correctness of earlier decisions and their clear formulation identified the gaps which the projects need to address.
文摘InCanonicalWorkflowFramework forResearch(CWFR)"packages"arerelevantin twodifferentdirections.In data science,workflows are in general being executed on a set of files which have been aggregated for specific purposes,such as for training a model in deep learning.We call this type of"package"a data collection and its aggregation and metadata description is motivated by research interests.The other type of"packages"relevant for CWFR are supposed to represent workflows in a self-describing and self-contained way for later execution.In this paper,we will review different packaging technologies and investigate their usability in the context of CWFR.For this purpose,we draw on an exemplary use case and show how packaging technologies can support its realization.We conclude that packaging technologies of different flavors help on providing inputs and outputs for workflow steps in a machine-readable way,as well as on representing a workflow and all its artifacts in a self-describing and self-contained way.
文摘In 2019 the German Leibniz research organization sponsored a conference on Open Science(OS)with the idea to publish some of the presented papers in the Data Intelligence journal.Becoming engaged as editors,we recognized that the term“Open Science”was coined about 10 years ago with the intention as pointed out by Michael Nielson:“OS is the idea that scientific knowledge of all kinds should be openly shared as early as is practical in the discovery process”.
