Pub Date : 2021-11-09DOI: 10.7191/jeslib.2021.1220
H. Ganshorn, Z. Premji
Data management practices for systematic reviews and other types of knowledge syntheses are variable, with some reviews following open science practices and others with poor reporting practices leading to lack of transparency or reproducibility. Reporting standards have improved the level of detail being shared in published reviews, and also encourage more open sharing of data from various stages of the review process. Similar to project planning or completion of an ethics application, systematic review teams should create a data management plan alongside creation of their study protocol. This commentary provides a brief description of a Data Management Plan Template created specifically for systematic reviews. It also describes the companion LibGuide which was created to provide more detailed examples, and to serve as a living document for updates and new guidance. The creation of the template was funded by the Portage Network.
{"title":"Data Management for Systematic Reviews: Guidance is Needed","authors":"H. Ganshorn, Z. Premji","doi":"10.7191/jeslib.2021.1220","DOIUrl":"https://doi.org/10.7191/jeslib.2021.1220","url":null,"abstract":"Data management practices for systematic reviews and other types of knowledge syntheses are variable, with some reviews following open science practices and others with poor reporting practices leading to lack of transparency or reproducibility. Reporting standards have improved the level of detail being shared in published reviews, and also encourage more open sharing of data from various stages of the review process. Similar to project planning or completion of an ethics application, systematic review teams should create a data management plan alongside creation of their study protocol. This commentary provides a brief description of a Data Management Plan Template created specifically for systematic reviews. It also describes the companion LibGuide which was created to provide more detailed examples, and to serve as a living document for updates and new guidance. The creation of the template was funded by the Portage Network.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44540935","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 : 2021-11-09DOI: 10.7191/jeslib.2021.1214
K. Bjornen, Cinthya Ippoliti
Objective: Customer journey mapping and design thinking were identified as useful tools for identifying deeper insights into the research data service needs of researchers on our campus with their direct input. In this article we discuss ways to improve the process in order to identify data needs earlier in the project life and at a more granular level. Methods: Customer journey mapping and design thinking were employed to get direct input from researchers about their research processes and data management needs. Responses from mapping templates and follow-up interviews were then used to identify themes to be explored using design thinking. Finally, a toolkit was created in Open Science Framework to guide other libraries who wish to employ these techniques Results: Outcomes from the customer journey mapping and design thinking sessions identified needs in the areas of data storage, organization and sharing. We also identified project-management lessons learned. The first lesson was to ensure the researchers who participate adequately represent the range of data needs on campus. Another was that customer journey mapping would be more effective if the responses were collected in real time and researchers were allowed more flexibility in the mapping process. Conclusions: Modifications to the customer journey mapping and design thinking techniques will provide real-time responses and deeper insights into the research data service needs of researchers on our campus. Our pilot identified some important gaps but we felt that more subtle and useful outcomes were possible by making changes to our process.
{"title":"Using Customer Journey Mapping and Design Thinking to Understand the Library’s Role in Supporting the Research Data Lifecycle","authors":"K. Bjornen, Cinthya Ippoliti","doi":"10.7191/jeslib.2021.1214","DOIUrl":"https://doi.org/10.7191/jeslib.2021.1214","url":null,"abstract":"Objective: Customer journey mapping and design thinking were identified as useful tools for identifying deeper insights into the research data service needs of researchers on our campus with their direct input. In this article we discuss ways to improve the process in order to identify data needs earlier in the project life and at a more granular level.\u0000\u0000Methods: Customer journey mapping and design thinking were employed to get direct input from researchers about their research processes and data management needs. Responses from mapping templates and follow-up interviews were then used to identify themes to be explored using design thinking. Finally, a toolkit was created in Open Science Framework to guide other libraries who wish to employ these techniques\u0000\u0000Results: Outcomes from the customer journey mapping and design thinking sessions identified needs in the areas of data storage, organization and sharing. We also identified project-management lessons learned. The first lesson was to ensure the researchers who participate adequately represent the range of data needs on campus. Another was that customer journey mapping would be more effective if the responses were collected in real time and researchers were allowed more flexibility in the mapping process.\u0000\u0000Conclusions: Modifications to the customer journey mapping and design thinking techniques will provide real-time responses and deeper insights into the research data service needs of researchers on our campus. Our pilot identified some important gaps but we felt that more subtle and useful outcomes were possible by making changes to our process.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48139603","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 : 2021-11-09DOI: 10.7191/jeslib.2021.1217
Ben B. Chiewphasa, Anna K. Moeller
Objectives: As certified Carpentries instructors, the authors organized and co-taught the University of Montana’s first in-person Carpentries workshop focused on the R programming language during early 2020. Due to the COVID-19 pandemic, a repeated workshop was postponed to the fall of 2020 and was adapted for a fully online setting. The authors share their Carpentries journey from in-person to online instruction, hoping to inspire those interested in organizing Carpentries at their institution for the first time and those interested in improving their existing Carpentries presence. Methods: The authors reflected on their experience facilitating the same Carpentries workshop in-person and online. They used this unique opportunity to compare the effectiveness of a face-to-face environment versus a virtual modality for delivering an interactive workshop. Results: When teaching in the online setting, the authors learned to emphasize the basics, create many opportunities for feedback using formative assessments, reduce the amount of material presented, and include helpers who are familiar with technology and troubleshooting. Conclusions: Although the online environment came with challenges (i.e., Zoom logistics and challenges, the need to further condense curricula, etc.), the instructors were surprised at the many advantages of hosting an online workshop. With some adaptations, Carpentries workshops work well in online delivery.
{"title":"Reflections from Transitioning Carpentries Workshops Online","authors":"Ben B. Chiewphasa, Anna K. Moeller","doi":"10.7191/jeslib.2021.1217","DOIUrl":"https://doi.org/10.7191/jeslib.2021.1217","url":null,"abstract":"Objectives: As certified Carpentries instructors, the authors organized and co-taught the University of Montana’s first in-person Carpentries workshop focused on the R programming language during early 2020. Due to the COVID-19 pandemic, a repeated workshop was postponed to the fall of 2020 and was adapted for a fully online setting. The authors share their Carpentries journey from in-person to online instruction, hoping to inspire those interested in organizing Carpentries at their institution for the first time and those interested in improving their existing Carpentries presence.\u0000\u0000Methods: The authors reflected on their experience facilitating the same Carpentries workshop in-person and online. They used this unique opportunity to compare the effectiveness of a face-to-face environment versus a virtual modality for delivering an interactive workshop.\u0000\u0000Results: When teaching in the online setting, the authors learned to emphasize the basics, create many opportunities for feedback using formative assessments, reduce the amount of material presented, and include helpers who are familiar with technology and troubleshooting.\u0000\u0000Conclusions: Although the online environment came with challenges (i.e., Zoom logistics and challenges, the need to further condense curricula, etc.), the instructors were surprised at the many advantages of hosting an online workshop. With some adaptations, Carpentries workshops work well in online delivery.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42834954","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 : 2021-11-01DOI: 10.1016/j.esci.2021.10.004
Xiulei Ji
{"title":"A Perspective of ZnCl2 Electrolytes: the Physical and Electrochemical Properties","authors":"Xiulei Ji","doi":"10.1016/j.esci.2021.10.004","DOIUrl":"https://doi.org/10.1016/j.esci.2021.10.004","url":null,"abstract":"","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87260642","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 : 2021-08-11DOI: 10.7191/jeslib.2021.1201
Alexandra Cooper, M. Steeleworthy, Ève Paquette-Bigras, E. Clary, E. Macpherson, Louise Gillis, J. Brodeur
Purpose: This paper introduces the Portage Network’s Dataverse Curation Guide and the new bilingual curation framework developed to support it. Brief Description: Canadian academic institutions and national organizations have been building infrastructure, staffing, and programming to support research data management. Amidst this work, a notable gap emerged between requirements for data curation in general repositories like Dataverse and the requisite workflows and guidance materials needed by curators to meet them. In response, Portage, a national network of data experts, organized a working group to develop a Dataverse curation guide built upon the Data Curation Network’s CURATED workflow. To create a bilingual resource, the original CURATE(D) acronym was modified to CURATION—which has the same meaning in both French and English—and steps were augmented with Dataverse-specific guidance and mapped to three conceptualized levels of curation to assist curators in prioritizing curation actions. Methods: An environmental scan of relevant deposit and curation guidance materials from Canadian and international institutions identified the need for a comprehensive Dataverse Curation Guide, as most existing resources were either depositor-focused or contained only partial workflows. The resulting Guide synthesized these guidance materials into the CURATION steps and mapped actions to various theoretical levels of data repository services and levels of curation. Resources: The following documents are supplemental to the Dataverse Curation Guide: the Portage Dataverse North Metadata Best Practices Guide, the Scholars Portal Dataverse Guide, and the Data Curation Network CURATED Workflow and Data Curation Primers.
{"title":"Creating Guidance for Canadian Dataverse Curators: Portage Network’s Dataverse Curation Guide","authors":"Alexandra Cooper, M. Steeleworthy, Ève Paquette-Bigras, E. Clary, E. Macpherson, Louise Gillis, J. Brodeur","doi":"10.7191/jeslib.2021.1201","DOIUrl":"https://doi.org/10.7191/jeslib.2021.1201","url":null,"abstract":"Purpose: This paper introduces the Portage Network’s Dataverse Curation Guide and the new bilingual curation framework developed to support it.\u0000\u0000Brief Description: Canadian academic institutions and national organizations have been building infrastructure, staffing, and programming to support research data management. Amidst this work, a notable gap emerged between requirements for data curation in general repositories like Dataverse and the requisite workflows and guidance materials needed by curators to meet them. In response, Portage, a national network of data experts, organized a working group to develop a Dataverse curation guide built upon the Data Curation Network’s CURATED workflow. To create a bilingual resource, the original CURATE(D) acronym was modified to CURATION—which has the same meaning in both French and English—and steps were augmented with Dataverse-specific guidance and mapped to three conceptualized levels of curation to assist curators in prioritizing curation actions.\u0000\u0000Methods: An environmental scan of relevant deposit and curation guidance materials from Canadian and international institutions identified the need for a comprehensive Dataverse Curation Guide, as most existing resources were either depositor-focused or contained only partial workflows. The resulting Guide synthesized these guidance materials into the CURATION steps and mapped actions to various theoretical levels of data repository services and levels of curation.\u0000\u0000Resources: The following documents are supplemental to the Dataverse Curation Guide: the Portage Dataverse North Metadata Best Practices Guide, the Scholars Portal Dataverse Guide, and the Data Curation Network CURATED Workflow and Data Curation Primers.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42547657","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 : 2021-08-11DOI: 10.7191/jeslib.2021.1200
Skylar Hawthorne
This commentary describes how context, quality, and efficiency guide data curation at the University of Michigan's Inter-university Consortium for Political and Social Research (ICPSR). These three principals manifest from necessity. A primary purpose of this work is to facilitate secondary data analysis but in order to so, the context of data must be documented. Since a mistake in this work would render any results published from the data inaccurate, quality is paramount. However, optimizing data quality can be time consuming, so automative curation practices are necessary for efficiency. The implementation of these principles (context, quality, and efficiency) is demonstrated by a recent case study with a high-profile dataset. As the nature of data work changes, these principles will continue to guide the practice of curation and establish valuable skills for future curators to cultivate.
{"title":"An Insider’s Take on Data Curation: Context, Quality, and Efficiency","authors":"Skylar Hawthorne","doi":"10.7191/jeslib.2021.1200","DOIUrl":"https://doi.org/10.7191/jeslib.2021.1200","url":null,"abstract":"This commentary describes how context, quality, and efficiency guide data curation at the University of Michigan's Inter-university Consortium for Political and Social Research (ICPSR). These three principals manifest from necessity. A primary purpose of this work is to facilitate secondary data analysis but in order to so, the context of data must be documented. Since a mistake in this work would render any results published from the data inaccurate, quality is paramount. However, optimizing data quality can be time consuming, so automative curation practices are necessary for efficiency. The implementation of these principles (context, quality, and efficiency) is demonstrated by a recent case study with a high-profile dataset. As the nature of data work changes, these principles will continue to guide the practice of curation and establish valuable skills for future curators to cultivate.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47792814","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 : 2021-08-11DOI: 10.7191/jeslib.2021.1203
Helenmary Sheridan, Anthony J. Dellureficio, Melissa A. Ratajeski, Sara Mannheimer, Terrie R. Wheeler
Institutional data repositories are the acknowledged gold standard for data curation platforms in academic libraries. But not every institution can sustain a repository, and not every dataset can be archived due to legal, ethical, or authorial constraints. Data catalogs—metadata-only indices of research data that provide detailed access instructions and conditions for use—are one potential solution, and may be especially suitable for "challenging" datasets. This article presents the strengths of data catalogs for increasing the discoverability and accessibility of research data. The authors argue that data catalogs are a viable alternative or complement to data repositories, and provide examples from their institutions' experiences to show how their data catalogs address specific curatorial requirements. The article also reports on the development of a community of practice for data catalogs and data discovery initiatives.
{"title":"Data Curation through Catalogs: A Repository-Independent Model for Data Discovery","authors":"Helenmary Sheridan, Anthony J. Dellureficio, Melissa A. Ratajeski, Sara Mannheimer, Terrie R. Wheeler","doi":"10.7191/jeslib.2021.1203","DOIUrl":"https://doi.org/10.7191/jeslib.2021.1203","url":null,"abstract":"Institutional data repositories are the acknowledged gold standard for data curation platforms in academic libraries. But not every institution can sustain a repository, and not every dataset can be archived due to legal, ethical, or authorial constraints. Data catalogs—metadata-only indices of research data that provide detailed access instructions and conditions for use—are one potential solution, and may be especially suitable for \"challenging\" datasets. This article presents the strengths of data catalogs for increasing the discoverability and accessibility of research data. The authors argue that data catalogs are a viable alternative or complement to data repositories, and provide examples from their institutions' experiences to show how their data catalogs address specific curatorial requirements. The article also reports on the development of a community of practice for data catalogs and data discovery initiatives.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42035344","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 : 2021-08-11DOI: 10.7191/jeslib.2021.1205
F. Rios, Chun Ly
Objective: To increase data quality and ensure compliance with appropriate policies, many institutional data repositories curate data that is deposited into their systems. Here, we present our experience as an academic library implementing and managing a semi-automated, cloud-based data curation workflow for a recently launched institutional data repository. Based on our experiences we then present management observations intended for data repository managers and technical staff looking to move some or all of their curation services to the cloud. Methods: We implemented tooling for our curation workflow in a service-oriented manner, making significant use of our data repository platform’s application programming interface (API). With an eye towards sustainability, a guiding development philosophy has been to automate processes following industry best practices while avoiding solutions with high resource needs (e.g., maintenance), and minimizing the risk of becoming locked-in to specific tooling. Results: The initial barrier for implementing a data curation workflow in the cloud was high in comparison to on-premises curation, mainly due to the need to develop in-house cloud expertise. However, compared to the cost for on-premises servers and storage, infrastructure costs have been substantially lower. Furthermore, in our particular case, once the foundation had been established, a cloud approach resulted in increased agility allowing us to quickly automate our workflow as needed. Conclusions: Workflow automation has put us on a path toward scaling the service and a cloud based-approach has helped with reduced initial costs. However, because cloud-based workflows and automation come with a maintenance overhead, it is important to build tooling that follows software development best practices and can be decoupled from curation workflows to avoid lock-in.
{"title":"Implementing and Managing a Data Curation Workflow in the Cloud","authors":"F. Rios, Chun Ly","doi":"10.7191/jeslib.2021.1205","DOIUrl":"https://doi.org/10.7191/jeslib.2021.1205","url":null,"abstract":"Objective: To increase data quality and ensure compliance with appropriate policies, many institutional data repositories curate data that is deposited into their systems. Here, we present our experience as an academic library implementing and managing a semi-automated, cloud-based data curation workflow for a recently launched institutional data repository. Based on our experiences we then present management observations intended for data repository managers and technical staff looking to move some or all of their curation services to the cloud.\u0000\u0000Methods: We implemented tooling for our curation workflow in a service-oriented manner, making significant use of our data repository platform’s application programming interface (API). With an eye towards sustainability, a guiding development philosophy has been to automate processes following industry best practices while avoiding solutions with high resource needs (e.g., maintenance), and minimizing the risk of becoming locked-in to specific tooling.\u0000\u0000Results: The initial barrier for implementing a data curation workflow in the cloud was high in comparison to on-premises curation, mainly due to the need to develop in-house cloud expertise. However, compared to the cost for on-premises servers and storage, infrastructure costs have been substantially lower. Furthermore, in our particular case, once the foundation had been established, a cloud approach resulted in increased agility allowing us to quickly automate our workflow as needed.\u0000\u0000Conclusions: Workflow automation has put us on a path toward scaling the service and a cloud based-approach has helped with reduced initial costs. However, because cloud-based workflows and automation come with a maintenance overhead, it is important to build tooling that follows software development best practices and can be decoupled from curation workflows to avoid lock-in.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47484748","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 : 2021-08-11DOI: 10.7191/jeslib.2021.1209
A. J. Choi, Xuying Xin
Data curation is the process of managing data to make it available for reuse and preservation and to allow FAIR (findable, accessible, interoperable, reusable) uses. It is an important part of the research lifecycle as researchers are often either required by funders or generally encouraged to preserve the dataset and make it discoverable and reusable. This has been especially important as the Open Access (OA) policy is being implemented in many institutions across the nation. In facilitating research data discovery and enhancing its easier reuse, an efficient data repository and its data curation play key roles. In this article, we briefly discuss the local institutional repository at Penn State University and the general data curation practices we adopt for the deposited files and datasets, then we focus on a data analytics tool that has recently been applied to extract tabular data from PDF files. This is an enhancement to the existing data curation practices as it adds additional tabular data to deposits with PDF files where tables are often embedded and not easily reused.
{"title":"Data Curation in Practice: Extract Tabular Data from PDF Files Using a Data Analytics Tool","authors":"A. J. Choi, Xuying Xin","doi":"10.7191/jeslib.2021.1209","DOIUrl":"https://doi.org/10.7191/jeslib.2021.1209","url":null,"abstract":"Data curation is the process of managing data to make it available for reuse and preservation and to allow FAIR (findable, accessible, interoperable, reusable) uses. It is an important part of the research lifecycle as researchers are often either required by funders or generally encouraged to preserve the dataset and make it discoverable and reusable. This has been especially important as the Open Access (OA) policy is being implemented in many institutions across the nation. In facilitating research data discovery and enhancing its easier reuse, an efficient data repository and its data curation play key roles. In this article, we briefly discuss the local institutional repository at Penn State University and the general data curation practices we adopt for the deposited files and datasets, then we focus on a data analytics tool that has recently been applied to extract tabular data from PDF files. This is an enhancement to the existing data curation practices as it adds additional tabular data to deposits with PDF files where tables are often embedded and not easily reused.","PeriodicalId":90214,"journal":{"name":"Journal of escience librarianship","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45934829","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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