分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-28 合作期刊: 《数据智能（英文）》
摘要： In Canonical Workflow Framework for Research (CWFR) packages are relevant in two different directions. 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
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-28 合作期刊: 《数据智能（英文）》
Peter, Wittenburg
Alex, Hardisty
Yann, Le Franc
Amirpasha, Mozaffari
Limor, Peer
Nikolay, A. Skvortsov
Zhiming, Zhao
Alessandro, Spinuso
摘要： 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.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-28 合作期刊: 《数据智能（英文）》
摘要： 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.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-28 合作期刊: 《数据智能（英文）》
Dirk, Betz
Claudia, Biniossek
Christophe, Blanchi
Felix, Henninger
Thomas, Lauer
Philipp, Wieder
Peter, Wittenburg
Martin, Zünkeler
摘要： 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.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-28 合作期刊: 《数据智能（英文）》
Peter, Wittenburg
Alex, Hardisty
Amirpasha, Mozzafari
Limor, Peer
Nikolay, Skvortsov
Alessandro, Spinuso
Zhiming, Zhao
摘要： This special issue is on Canonical Workflow Frameworks for Research (CWFR). A workflow refers to a sequence of activities, which may be more or less computer-based, used with regularity in the research process. CWFR aim to identify common patterns in such scientifically motivated workflows and to offer libraries of components based on FAIR Digital Objects as the integrative standard. Such CWFR components can be reusable independent of particular technologies, benefitting researchers in their daily work by making recurring activities more efficient, using automated workflow methods that would immediately create FAIR compliant data without adding burden.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-27 合作期刊: 《数据智能（英文）》
Keith, Jeffery
Peter, Wittenburg
Larry, Lannom
George, Strawn
Claudia, Biniossek
Dirk, Betz
Christophe, Blanchi
摘要： 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#2;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.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-27 合作期刊: 《数据智能（英文）》
摘要： 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 domainone data-domain as George Strawn calls itwhich 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.
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