Pub Date : 2019-09-01DOI: 10.1109/eScience.2019.00073
M. Stoica, S. Peckham
We present a preliminary methodology, currently in development, for automated generation of domain-specific, machine-readable representations of qualitative and quantitative scientific variable concepts. The method presented is based on the top level universal categories and modular design patterns declared within the Scientific Variables Ontology (v 1.0.0) blueprint. These scientific variable representations can be used to annotate electronic resources, such as data and models and, along with reasoning algorithms, can be used to provide explainable automated resource alignment capabilities in the assembly of scientific workflows.
{"title":"Incorporating New Concepts Into the Scientific Variables Ontology","authors":"M. Stoica, S. Peckham","doi":"10.1109/eScience.2019.00073","DOIUrl":"https://doi.org/10.1109/eScience.2019.00073","url":null,"abstract":"We present a preliminary methodology, currently in development, for automated generation of domain-specific, machine-readable representations of qualitative and quantitative scientific variable concepts. The method presented is based on the top level universal categories and modular design patterns declared within the Scientific Variables Ontology (v 1.0.0) blueprint. These scientific variable representations can be used to annotate electronic resources, such as data and models and, along with reasoning algorithms, can be used to provide explainable automated resource alignment capabilities in the assembly of scientific workflows.","PeriodicalId":142614,"journal":{"name":"2019 15th International Conference on eScience (eScience)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134402314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mineral dust, defined as aerosol originating from the soil, can have various harmful effects to the environment and human health. The detection of dust, and particularly incoming dust storms, may help prevent some of these negative impacts. In this paper, using satellite observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation Observation (CALIPSO), we compared several machine learning algorithms to traditional physical models and evaluated their performance regarding mineral dust detection. Based on the comparison results, we proposed a hybrid algorithm to integrate physical model with the data mining model, which achieved the best accuracy result among all the methods. Further, we identified the ranking of different channels of MODIS data based on the importance of the band wavelengths in dust detection. Our model also showed the quantitative relationships between the dust and the different band wavelengths.
{"title":"A Hybrid Algorithm for Mineral Dust Detection Using Satellite Data","authors":"Peichang Shi, Qianqian Song, Janita Patwardhan, Zhibo Zhang, Jianwu Wang, A. Gangopadhyay","doi":"10.1109/eScience.2019.00012","DOIUrl":"https://doi.org/10.1109/eScience.2019.00012","url":null,"abstract":"Mineral dust, defined as aerosol originating from the soil, can have various harmful effects to the environment and human health. The detection of dust, and particularly incoming dust storms, may help prevent some of these negative impacts. In this paper, using satellite observations from Moderate Resolution Imaging Spectroradiometer (MODIS) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation Observation (CALIPSO), we compared several machine learning algorithms to traditional physical models and evaluated their performance regarding mineral dust detection. Based on the comparison results, we proposed a hybrid algorithm to integrate physical model with the data mining model, which achieved the best accuracy result among all the methods. Further, we identified the ranking of different channels of MODIS data based on the importance of the band wavelengths in dust detection. Our model also showed the quantitative relationships between the dust and the different band wavelengths.","PeriodicalId":142614,"journal":{"name":"2019 15th International Conference on eScience (eScience)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132430924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/eScience.2019.00094
Sandeep Puthanveetil Satheesan, Alan B. Craig, Yu Zhang
Social construction is a theoretical position that social reality is created through the humans' definition and interaction as opposed to something that exists by default. As one type of social reality, juvenile delinquency is perceived as part of social problems, deeply contextualized and socially constructed in American society. The social construction of juvenile delinquency started far earlier than the first juvenile court in 1899 in the U.S. Scholars have tried traditional historical analysis to explore the timeline of the social construction of juvenile delinquency in the past, but it is inefficient to examine hundred years of documents using traditional paper-pencil documenting method. We propose to research, develop and apply image and text analysis methods to analyze hundreds of years of newspaper data and show a clear development of social construction of juvenile delinquency in American society. The project aims to explore questions around how the media started depicting certain types of juvenile behavior as delinquency, how they described those behaviors; who are those juveniles (age, race, gender, family background, community background, etc.), how other social institutions treat those juveniles in those stories; how the depiction of juvenile delinquency has changed during the past 100 years; whether the analysis results support social construction perspective in terms of juvenile delinquency or not. In this paper, we present our ongoing work of doing image analysis on the newspaper collection from the Library of Congress Chronicling America website, initial results, observations, current conclusions, and future work.
{"title":"A Historical Big Data Analysis to Understand the Social Construction of Juvenile Delinquency in the United States","authors":"Sandeep Puthanveetil Satheesan, Alan B. Craig, Yu Zhang","doi":"10.1109/eScience.2019.00094","DOIUrl":"https://doi.org/10.1109/eScience.2019.00094","url":null,"abstract":"Social construction is a theoretical position that social reality is created through the humans' definition and interaction as opposed to something that exists by default. As one type of social reality, juvenile delinquency is perceived as part of social problems, deeply contextualized and socially constructed in American society. The social construction of juvenile delinquency started far earlier than the first juvenile court in 1899 in the U.S. Scholars have tried traditional historical analysis to explore the timeline of the social construction of juvenile delinquency in the past, but it is inefficient to examine hundred years of documents using traditional paper-pencil documenting method. We propose to research, develop and apply image and text analysis methods to analyze hundreds of years of newspaper data and show a clear development of social construction of juvenile delinquency in American society. The project aims to explore questions around how the media started depicting certain types of juvenile behavior as delinquency, how they described those behaviors; who are those juveniles (age, race, gender, family background, community background, etc.), how other social institutions treat those juveniles in those stories; how the depiction of juvenile delinquency has changed during the past 100 years; whether the analysis results support social construction perspective in terms of juvenile delinquency or not. In this paper, we present our ongoing work of doing image analysis on the newspaper collection from the Library of Congress Chronicling America website, initial results, observations, current conclusions, and future work.","PeriodicalId":142614,"journal":{"name":"2019 15th International Conference on eScience (eScience)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128228876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/eScience.2019.00017
R. Isdahl, Odd Erik Gundersen
Even machine learning experiments that are fully conducted on computers are not necessarily reproducible. An increasing number of open source and commercial, closed source machine learning platforms are being developed that help address this problem. However, there is no standard for assessing and comparing which features are required to fully support reproducibility. We propose a quantitative method that alleviates this problem. Based on the proposed method we assess and compare the current state of the art machine learning platforms for how well they support making empirical results reproducible. Our results show that BEAT and Floydhub have the best support for reproducibility with Codalab and Kaggle as close contenders. The most commonly used machine learning platforms provided by the big tech companies have poor support for reproducibility.
{"title":"Out-of-the-Box Reproducibility: A Survey of Machine Learning Platforms","authors":"R. Isdahl, Odd Erik Gundersen","doi":"10.1109/eScience.2019.00017","DOIUrl":"https://doi.org/10.1109/eScience.2019.00017","url":null,"abstract":"Even machine learning experiments that are fully conducted on computers are not necessarily reproducible. An increasing number of open source and commercial, closed source machine learning platforms are being developed that help address this problem. However, there is no standard for assessing and comparing which features are required to fully support reproducibility. We propose a quantitative method that alleviates this problem. Based on the proposed method we assess and compare the current state of the art machine learning platforms for how well they support making empirical results reproducible. Our results show that BEAT and Floydhub have the best support for reproducibility with Codalab and Kaggle as close contenders. The most commonly used machine learning platforms provided by the big tech companies have poor support for reproducibility.","PeriodicalId":142614,"journal":{"name":"2019 15th International Conference on eScience (eScience)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132946898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/eScience.2019.00027
M. Taufer, E. Deelman, Stephen Thomas, Michael R. Wyatt, T. Do, L. Pottier, Rafael Ferreira da Silva, H. Weinstein, M. Cuendet, Trilce Estrada
Molecular Dynamics (MD) simulations executed on state-of-the-art supercomputers are producing data at rates faster than it can be written out to disk. In situ and in transit analysis of data generated by MD simulations reduce the original volume of information by several orders of magnitude, thereby alleviating the negative impact of I/O bottlenecks. This work focuses on characterizing the impact of in situ and in transit analytics on the overall MD workflow performance, and the capability for capturing rapid, rare events in the simulated molecular system. The MD simulation and analysis processes share data via remote direct memory access (RDMA) using DataSpaces. Our metrics of interest are time spent waiting in I/O by the MD simulation, lost frames of the MD simulation, and idle time of the analysis. We measure these metrics for a diverse set of molecular systems and characterize their trends for in situ and in transit configurations. We then model which frames are dropped and which ones are analyzed for a real use case. The insights gained from this study are generally applicable for in situ and in transit workflows that require optimization of parameters to minimize loss in workflow performance and analytic accuracy.
{"title":"Characterizing In Situ and In Transit Analytics of Molecular Dynamics Simulations for Next-Generation Supercomputers","authors":"M. Taufer, E. Deelman, Stephen Thomas, Michael R. Wyatt, T. Do, L. Pottier, Rafael Ferreira da Silva, H. Weinstein, M. Cuendet, Trilce Estrada","doi":"10.1109/eScience.2019.00027","DOIUrl":"https://doi.org/10.1109/eScience.2019.00027","url":null,"abstract":"Molecular Dynamics (MD) simulations executed on state-of-the-art supercomputers are producing data at rates faster than it can be written out to disk. In situ and in transit analysis of data generated by MD simulations reduce the original volume of information by several orders of magnitude, thereby alleviating the negative impact of I/O bottlenecks. This work focuses on characterizing the impact of in situ and in transit analytics on the overall MD workflow performance, and the capability for capturing rapid, rare events in the simulated molecular system. The MD simulation and analysis processes share data via remote direct memory access (RDMA) using DataSpaces. Our metrics of interest are time spent waiting in I/O by the MD simulation, lost frames of the MD simulation, and idle time of the analysis. We measure these metrics for a diverse set of molecular systems and characterize their trends for in situ and in transit configurations. We then model which frames are dropped and which ones are analyzed for a real use case. The insights gained from this study are generally applicable for in situ and in transit workflows that require optimization of parameters to minimize loss in workflow performance and analytic accuracy.","PeriodicalId":142614,"journal":{"name":"2019 15th International Conference on eScience (eScience)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133009482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/eScience.2019.00056
K. Chard, Ian T Foster
The adoption of computation- and data-intensive science, or eScience, makes research progress increasingly dependent on the availability, management, and use of sophisticated cyberinfrastructure. An unfortunate consequence is that researchers face increasingly burdensome demands for managing and maintaining cyberinfrastructure. The advent of virtualization and cloud computing has helped, by allowing outsourcing of some such tasks to reliable and scalable cloud providers. But much more progress is needed before we can create a research cyberinfrastructure that allows researchers to focus on creative thought rather than systems management. We examine here how the emerging paradigm of serverless computing, in which arbitrary functions can be dispatched seamlessly to scalable, secure, and reliable service providers, can move us in that direction. To demonstrate how serverless computing can transform scientific computing, we describe three serverless computing models: service-oriented computing, research automation, and function as a service, presenting illustrative case studies for each.
{"title":"Serverless Science for Simple, Scalable, and Shareable Scholarship","authors":"K. Chard, Ian T Foster","doi":"10.1109/eScience.2019.00056","DOIUrl":"https://doi.org/10.1109/eScience.2019.00056","url":null,"abstract":"The adoption of computation- and data-intensive science, or eScience, makes research progress increasingly dependent on the availability, management, and use of sophisticated cyberinfrastructure. An unfortunate consequence is that researchers face increasingly burdensome demands for managing and maintaining cyberinfrastructure. The advent of virtualization and cloud computing has helped, by allowing outsourcing of some such tasks to reliable and scalable cloud providers. But much more progress is needed before we can create a research cyberinfrastructure that allows researchers to focus on creative thought rather than systems management. We examine here how the emerging paradigm of serverless computing, in which arbitrary functions can be dispatched seamlessly to scalable, secure, and reliable service providers, can move us in that direction. To demonstrate how serverless computing can transform scientific computing, we describe three serverless computing models: service-oriented computing, research automation, and function as a service, presenting illustrative case studies for each.","PeriodicalId":142614,"journal":{"name":"2019 15th International Conference on eScience (eScience)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114827312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/eScience.2019.00089
C. Pagé, W. S. D. Cerff, M. Plieger, A. Spinuso, Xavier Pivan
Access to Climate data is crucial to sustain research and climate change impact assessments. It has a strong societal impact as those changes will have to be mitigated as much as possible. The whole climate data archive is expected to reach a volume of 30 PB in 2019 and up to 2000 PB in 2024 (estimated), evolving from 30 TB in 2007 and 2 PB in 2014. Data processing and analysis must now happen remotely for the users: they now have to rely on heterogeneous infrastructures and services between the data and their location. Developers of Research Infrastructures have to provide services to those users, hence having to define standards and generic services to fulfill those requirements. It will be shown how the DARE eScience Platform (http://project-dare.eu) will help developers to develop more rapidly needed services for a large range of scientific researchers. The platform is designed for efficient and traceable development of complex experiments and domain-specific services on the Cloud. It will be also shown how the integration of the DARE platform together with the climate IS-ENES (https://is.enes.org) Research Infrastructure front-end climate4impact (C4I: https://climate4impact.eu/) will help developers leverage heterogeneous architectures transparently for the benefit of researchers.
{"title":"Enabling Transparent Access to Heterogeneous Architectures for IS-ENES Climate4Impact using the DARE Platform","authors":"C. Pagé, W. S. D. Cerff, M. Plieger, A. Spinuso, Xavier Pivan","doi":"10.1109/eScience.2019.00089","DOIUrl":"https://doi.org/10.1109/eScience.2019.00089","url":null,"abstract":"Access to Climate data is crucial to sustain research and climate change impact assessments. It has a strong societal impact as those changes will have to be mitigated as much as possible. The whole climate data archive is expected to reach a volume of 30 PB in 2019 and up to 2000 PB in 2024 (estimated), evolving from 30 TB in 2007 and 2 PB in 2014. Data processing and analysis must now happen remotely for the users: they now have to rely on heterogeneous infrastructures and services between the data and their location. Developers of Research Infrastructures have to provide services to those users, hence having to define standards and generic services to fulfill those requirements. It will be shown how the DARE eScience Platform (http://project-dare.eu) will help developers to develop more rapidly needed services for a large range of scientific researchers. The platform is designed for efficient and traceable development of complex experiments and domain-specific services on the Cloud. It will be also shown how the integration of the DARE platform together with the climate IS-ENES (https://is.enes.org) Research Infrastructure front-end climate4impact (C4I: https://climate4impact.eu/) will help developers leverage heterogeneous architectures transparently for the benefit of researchers.","PeriodicalId":142614,"journal":{"name":"2019 15th International Conference on eScience (eScience)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115754671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/eScience.2019.00074
T. Wiktorski, Y. Demchenko, O. Chertov
This paper presents experiences of development and teaching three different types of Big Data Infrastructure courses as a part of the general Data Science curricula. The authors built the discussed courses based on the EDISON Data Science Framework (EDSF), in particular, Data Science Body of Knowledge (DS-BoK) related to Data Science Engineering knowledge area group (KAG-DSENG). The paper provides overview of the sandboxes, Cloud-based platforms and tools for Big Data Analytics and stresses importance of including into curriculum the practical work with Clouds for future graduates or specialists workplace adaptability. The paper discusses a relationship between the DSENG BoK and Big Data technologies and platforms, in particular Hadoop-based applications and tools for data analytics that should be promoted through all course activities: lectures, practical activities and self-study.
{"title":"Data Science Model Curriculum Implementation for Various Types of Big Data Infrastructure Courses","authors":"T. Wiktorski, Y. Demchenko, O. Chertov","doi":"10.1109/eScience.2019.00074","DOIUrl":"https://doi.org/10.1109/eScience.2019.00074","url":null,"abstract":"This paper presents experiences of development and teaching three different types of Big Data Infrastructure courses as a part of the general Data Science curricula. The authors built the discussed courses based on the EDISON Data Science Framework (EDSF), in particular, Data Science Body of Knowledge (DS-BoK) related to Data Science Engineering knowledge area group (KAG-DSENG). The paper provides overview of the sandboxes, Cloud-based platforms and tools for Big Data Analytics and stresses importance of including into curriculum the practical work with Clouds for future graduates or specialists workplace adaptability. The paper discusses a relationship between the DSENG BoK and Big Data technologies and platforms, in particular Hadoop-based applications and tools for data analytics that should be promoted through all course activities: lectures, practical activities and self-study.","PeriodicalId":142614,"journal":{"name":"2019 15th International Conference on eScience (eScience)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116110558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/eScience.2019.00049
Illyoung Choi, Jude C. Nelson, Larry L. Peterson, J. Hartman
Scientific computing is becoming more data-centric and more collaborative, requiring increasingly large datasets to be transferred across the Internet. Transferring these datasets efficiently and making them accessible to scientific workflows is an increasingly difficult task. In addition, the data transfer time can be a significant portion of the overall workflow running time. This paper presents SDM (Syndicate Dataset Manager), a scientific dataset delivery platform. Unlike general-purpose data transfer tools, SDM offers on-demand access to remote scientific datasets. On-demand access doesn't require staging datasets to local file systems prior to computing on them, and it also enables overlapping computation and I/O. In addition, SDM offers a simple interface for users to locate and access datasets. To validate the usefulness of SDM, we performed realistic metagenomic sequence analysis workflows on remote genomic datasets. In general, SDM configured with a CDN outperforms existing data access methods. With warm CDN caches, SDM completes the workflow 17-20% faster than staging methods. Its performance is even comparable to local storage. SDM is only 9% slower than local HDD storage and 18% slower than local SSD storage. Together, its performance and its ease-of-use make SDM an attractive platform for performing scientific workflows on remote datasets.
{"title":"SDM: A Scientific Dataset Delivery Platform","authors":"Illyoung Choi, Jude C. Nelson, Larry L. Peterson, J. Hartman","doi":"10.1109/eScience.2019.00049","DOIUrl":"https://doi.org/10.1109/eScience.2019.00049","url":null,"abstract":"Scientific computing is becoming more data-centric and more collaborative, requiring increasingly large datasets to be transferred across the Internet. Transferring these datasets efficiently and making them accessible to scientific workflows is an increasingly difficult task. In addition, the data transfer time can be a significant portion of the overall workflow running time. This paper presents SDM (Syndicate Dataset Manager), a scientific dataset delivery platform. Unlike general-purpose data transfer tools, SDM offers on-demand access to remote scientific datasets. On-demand access doesn't require staging datasets to local file systems prior to computing on them, and it also enables overlapping computation and I/O. In addition, SDM offers a simple interface for users to locate and access datasets. To validate the usefulness of SDM, we performed realistic metagenomic sequence analysis workflows on remote genomic datasets. In general, SDM configured with a CDN outperforms existing data access methods. With warm CDN caches, SDM completes the workflow 17-20% faster than staging methods. Its performance is even comparable to local storage. SDM is only 9% slower than local HDD storage and 18% slower than local SSD storage. Together, its performance and its ease-of-use make SDM an attractive platform for performing scientific workflows on remote datasets.","PeriodicalId":142614,"journal":{"name":"2019 15th International Conference on eScience (eScience)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121019028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/eScience.2019.00055
Beth Plale
Both the landscape of eScience research and the environment in which the research is conducted are undergoing change. Transparency by design in eScience is proposed as a term to describe transparency in eScience practices, processes, methodologies, and research results. We break down different aspects of transparency and urge the eScience community towards a renewed commitment to scientific rigor because of the important role that we as scientists have to improve society and protect the good will that society has bestowed on science.
{"title":"Transparency by Design in eScience Research","authors":"Beth Plale","doi":"10.1109/eScience.2019.00055","DOIUrl":"https://doi.org/10.1109/eScience.2019.00055","url":null,"abstract":"Both the landscape of eScience research and the environment in which the research is conducted are undergoing change. Transparency by design in eScience is proposed as a term to describe transparency in eScience practices, processes, methodologies, and research results. We break down different aspects of transparency and urge the eScience community towards a renewed commitment to scientific rigor because of the important role that we as scientists have to improve society and protect the good will that society has bestowed on science.","PeriodicalId":142614,"journal":{"name":"2019 15th International Conference on eScience (eScience)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132116266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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