Data Intelligence最新文献_第10页

Galaxy: A Decade of Realising CWFR Concepts 银河:实现CWFR概念的十年

IF 3.9 3区计算机科学

Data Intelligence Pub Date : 2022-04-01 DOI: 10.1162/dint_a_00136

Beatriz Serrano-Solano, A. Fouilloux, Ignacio Eguinoa, Matúš Kalaš, B. Grüning, Frederik Coppens

引用次数: 3

Editors’ Note: Special Issue on Canonical Workflow Frameworks for Research 编者注:研究规范工作流框架特刊

IF 3.9 3区计算机科学

Data Intelligence Pub Date : 2022-04-01 DOI: 10.1162/dint_e_00122

P. Wittenburg, A. Hardisty, Amirpasha Mozzafari, Limor Peer, N. Skvortsov, A. Spinuso, Zhiming Zhao

引用次数: 0

Canonical Workflow for Experimental Research 规范的实验研究工作流程

IF 3.9 3区计算机科学

Data Intelligence Pub Date : 2022-04-01 DOI: 10.1162/dint_a_00123

Dirk Betz, Claudia Biniossek, Christophe Blanchi, Felix Henninger, T. Lauer, P. Wieder, P. Wittenburg, M. Zünkeler

{"title":"Canonical Workflow for Experimental Research","authors":"Dirk Betz, Claudia Biniossek, Christophe Blanchi, Felix Henninger, T. Lauer, P. Wieder, P. Wittenburg, M. Zünkeler","doi":"10.1162/dint_a_00123","DOIUrl":"https://doi.org/10.1162/dint_a_00123","url":null,"abstract":"Abstract The overall expectation of introducing Canonical Workflow for Experimental Research and FAIR digital objects (FDOs) can be summarised as reducing the gap between workflow technology and research practices to make experimental work more efficient and improve FAIRness without adding administrative load on the researchers. In this document, we will describe, with the help of an example, how CWFR could work in detail and improve research procedures. We have chosen the example of “experiments with human subjects” which stretches from planning an experiment to storing the collected data in a repository. While we focus on experiments with human subjects, we are convinced that CWFR can be applied to many other data generation processes based on experiments. The main challenge is to identify repeating patterns in existing research practices that can be abstracted to create CWFR. In this document, we will include detailed examples from different disciplines to demonstrate that CWFR can be implemented without violating specific disciplinary or methodological requirements. We do not claim to be comprehensive in all aspects, since these examples are meant to prove the concept of CWFR.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42683678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Canonical Workflow for Machine Learning Tasks 机器学习任务的规范工作流

IF 3.9 3区计算机科学

Data Intelligence Pub Date : 2022-04-01 DOI: 10.1162/dint_a_00124

Christophe Blanchi, B. Gebre, P. Wittenburg

{"title":"Canonical Workflow for Machine Learning Tasks","authors":"Christophe Blanchi, B. Gebre, P. Wittenburg","doi":"10.1162/dint_a_00124","DOIUrl":"https://doi.org/10.1162/dint_a_00124","url":null,"abstract":"Abstract There is a huge gap between (1) the state of workflow technology on the one hand and the practices in the many labs working with data driven methods on the other and (2) the awareness of the FAIR principles and the lack of changes in practices during the last 5 years. The CWFR concept has been defined which is meant to combine these two intentions, increasing the use of workflow technology and improving FAIR compliance. In the study described in this paper we indicate how this could be applied to machine learning which is now used by almost all research disciplines with the well-known effects of a huge lack of repeatability and reproducibility. Researchers will only change practices if they can work efficiently and are not loaded with additional tasks. A comprehensive CWFR framework would be an umbrella for all steps that need to be carried out to do machine learning on selected data collections and immediately create a comprehensive and FAIR compliant documentation. The researcher is guided by such a framework and information once entered can easily be shared and reused. The many iterations normally required in machine learning can be dealt with efficiently using CWFR methods. Libraries of components that can be easily orchestrated using FAIR Digital Objects as a common entity to document all actions and to exchange information between steps without the researcher needing to understand anything about PIDs and FDO details is probably the way to increase efficiency in repeating research workflows. As the Galaxy project indicates, the availability of supporting tools will be important to let researchers use these methods. Other as the Galaxy framework suggests, however, it would be necessary to include all steps necessary for doing a machine learning task including those that require human interaction and to document all phases with the help of structured FDOs.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41320073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling Canonical Analysis Workflows Documented Data Harmonization on Global Air Quality Data 实现全球空气质量数据规范化分析工作流程文档化数据协调

IF 3.9 3区计算机科学

Data Intelligence Pub Date : 2022-04-01 DOI: 10.1162/dint_a_00130

S. Schröder, Eleonora Epp, A. Mozaffari, M. Romberg, Niklas Selke, M. Schultz

引用次数: 1

Scaling Notebooks as Re-configurable Cloud Workflows 将笔记本扩展为可重新配置的云工作流

IF 3.9 3区计算机科学

Data Intelligence Pub Date : 2022-04-01 DOI: 10.1162/dint_a_00140

Yuandou Wang, Spiros Koulouzis, Riccardo Bianchi, N. Li, Yifang Shi, J. Timmermans, W. Kissling, Zhiming Zhao

{"title":"Scaling Notebooks as Re-configurable Cloud Workflows","authors":"Yuandou Wang, Spiros Koulouzis, Riccardo Bianchi, N. Li, Yifang Shi, J. Timmermans, W. Kissling, Zhiming Zhao","doi":"10.1162/dint_a_00140","DOIUrl":"https://doi.org/10.1162/dint_a_00140","url":null,"abstract":"Abstract Literate computing environments, such as the Jupyter (i.e., Jupyter Notebooks, JupyterLab, and JupyterHub), have been widely used in scientific studies; they allow users to interactively develop scientific code, test algorithms, and describe the scientific narratives of the experiments in an integrated document. To scale up scientific analyses, many implemented Jupyter environment architectures encapsulate the whole Jupyter notebooks as reproducible units and autoscale them on dedicated remote infrastructures (e.g., highperformance computing and cloud computing environments). The existing solutions are still limited in many ways, e.g., 1) the workflow (or pipeline) is implicit in a notebook, and some steps can be generically used by different code and executed in parallel, but because of the tight cell structure, all steps in the Jupyter notebook have to be executed sequentially and lack of the flexibility of reusing the core code fragments, and 2) there are performance bottlenecks that need to improve the parallelism and scalability when handling extensive input data and complex computation. In this work, we focus on how to manage the workflow in a notebook seamlessly. We 1) encapsulate the reusable cells as RESTful services and containerize them as portal components, 2) provide a composition tool for describing workflow logic of those reusable components, and 3) automate the execution on remote cloud infrastructure. Empirically, we validate the solution's usability via a use case from the Ecology and Earth Science domain, illustrating the processing of massive Light Detection and Ranging (LiDAR) data. The demonstration and analysis show that our method is feasible, but that it needs further improvement, especially on integrating distributed workflow scheduling, automatic deployment, and execution to develop as a mature approach.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46210347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Analysis of Pioneering Computable Biomedical Knowledge Repositories and their Emerging Governance Structures 开创性的可计算生物医学知识库及其新兴治理结构分析

IF 3.9 3区计算机科学

Data Intelligence Pub Date : 2022-03-14 DOI: 10.1162/dint_a_00148

P. Amara, M. Conte, Allen J. Flynn, Jodyn E. Platt, Grace Trinidad

{"title":"Analysis of Pioneering Computable Biomedical Knowledge Repositories and their Emerging Governance Structures","authors":"P. Amara, M. Conte, Allen J. Flynn, Jodyn E. Platt, Grace Trinidad","doi":"10.1162/dint_a_00148","DOIUrl":"https://doi.org/10.1162/dint_a_00148","url":null,"abstract":"Abstract A growing interest in producing and sharing computable biomedical knowledge artifacts (CBKs) is increasing the demand for repositories that validate, catalog, and provide shared access to CBKs. However, there is a lack of evidence on how best to manage and sustain CBK repositories. In this paper, we present the results of interviews with several pioneering CBK repository owners. These interviews were informed by the Trusted Repositories Audit and Certification (TRAC) framework. Insights gained from these interviews suggest that the organizations operating CBK repositories are somewhat new, that their initial approaches to repository governance are informal, and that achieving economic sustainability for their CBK repositories is a major challenge. To enable a learning health system to make better use of its data intelligence, future approaches to CBK repository management will require enhanced governance and closer adherence to best practice frameworks to meet the needs of myriad biomedical science and health communities. More effort is needed to find sustainable funding models for accessible CBK artifact collections.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47280853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Canonical Workflows in Simulation-based Climate Sciences 基于模拟的气候科学中的规范工作流程

IF 3.9 3区计算机科学

Data Intelligence Pub Date : 2022-03-07 DOI: 10.1162/dint_a_00127

I. Anders, Karsten Peters-von Gehlen, H. Thiemann

{"title":"Canonical Workflows in Simulation-based Climate Sciences","authors":"I. Anders, Karsten Peters-von Gehlen, H. Thiemann","doi":"10.1162/dint_a_00127","DOIUrl":"https://doi.org/10.1162/dint_a_00127","url":null,"abstract":"Abstract In this paper we present the derivation of Canonical Workflow Modules from current workflows in simulation-based climate science in support of the elaboration of a corresponding framework for simulation-based research. We first identified the different users and user groups in simulation-based climate science based on their reasons for using the resources provided at the German Climate Computing Center (DKRZ). What is special about this is that the DKRZ provides the climate science community with resources like high performance computing (HPC), data storage and specialised services, and hosts the World Data Center for Climate (WDCC). Therefore, users can perform their entire research workflows up to the publication of the data on the same infrastructure. Our analysis shows, that the resources are used by two primary user types: those who require the HPC-system to perform resource intensive simulations to subsequently analyse them and those who reuse, build-on and analyse existing data. We then further subdivided these top-level user categories based on their specific goals and analysed their typical, idealised workflows applied to achieve the respective project goals. We find that due to the subdivision and further granulation of the user groups, the workflows show apparent differences. Nevertheless, similar “Canonical Workflow Modules” can be clearly made out. These modules are “Data and Software (Re)use”, “Compute”, “Data and Software Storing”, “Data and Software Publication”, “Generating Knowledge” and in their entirety form the basis for a Canonical Workflow Framework for Research (CWFR). It is desirable that parts of the workflows in a CWFR act as FDOs, but we view this aspect critically. Also, we reflect on the question whether the derivation of Canonical Workflow modules from the analysis of current user behaviour still holds for future systems and work processes.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44864013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Reproducible Research Publication Workflow: A Canonical Workflow Framework and FAIR Digital Object Approach to Quality Research Output 可重复的研究出版工作流程:一个规范的工作流程框架和公平的数字对象方法的质量研究成果

IF 3.9 3区计算机科学

Data Intelligence Pub Date : 2022-03-07 DOI: 10.1162/dint_a_00133

Limor Peer, Claudia Biniossek, Dirk Betz, Thu-Mai Christian

引用次数: 1

Using a Workflow Management Platform in Textual Data Management 工作流管理平台在文本数据管理中的应用

IF 3.9 3区计算机科学

Data Intelligence Pub Date : 2022-03-07 DOI: 10.1162/dint_a_00139

T. Doan, S. Bingert, R. Yahyapour

{"title":"Using a Workflow Management Platform in Textual Data Management","authors":"T. Doan, S. Bingert, R. Yahyapour","doi":"10.1162/dint_a_00139","DOIUrl":"https://doi.org/10.1162/dint_a_00139","url":null,"abstract":"Abstract The paper gives a brief introduction about the workflow management platform, Flowable, and how it is used for textual-data management. It is relatively new with its first release on 13 October, 2016. Despite the short time on the market, it seems to be quickly well-noticed with 4.6 thousand stars on GitHub at the moment. The focus of our project is to build a platform for text analysis on a large scale by including many different text resources. Currently, we have successfully connected to four different text resources and obtained more than one million works. Some resources are dynamic, which means that they might add more data or modify their current data. Therefore, it is necessary to keep data, both the metadata and the raw data, from our side up to date with the resources. In addition, to comply with FAIR principles, each work is assigned a persistent identifier (PID) and indexed for searching purposes. In the last step, we perform some standard analyses on the data to enhance our search engine and to generate a knowledge graph. End-users can utilize our platform to search on our data or get access to the knowledge graph. Furthermore, they can submit their code for their analyses to the system. The code will be executed on a High-Performance Cluster (HPC) and users can receive the results later on. In this case, Flowable can take advantage of PIDs for digital objects identification and management to facilitate the communication with the HPC system. As one may already notice, the whole process can be expressed as a workflow. A workflow, including error handling and notification, has been created and deployed. Workflow execution can be triggered manually or after predefined time intervals. According to our evaluation, the Flowable platform proves to be powerful and flexible. Further usage of the platform is already planned or implemented for many of our projects.","PeriodicalId":34023,"journal":{"name":"Data Intelligence","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44504533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0