The FAIR data principles have emerged as a major focus in the world of scientific research data, but have not had as large an impact on official statistics. While there are good reasons for this, FAIR developments within the research community may be of interest to official statistical organizations. These include the increased availability of research data, improvements in the area of machine-actionable metadata, and a focus on provenance information which could lead to increased transparency and data quality. Some activities of interest are described as a starting point for those in official statistics who may wish to follow these developments.
{"title":"Open and FAIR: Trends in scientific publishing and the implications for official statistics","authors":"Arofan Gregory","doi":"10.3233/sji-240020","DOIUrl":"https://doi.org/10.3233/sji-240020","url":null,"abstract":"The FAIR data principles have emerged as a major focus in the world of scientific research data, but have not had as large an impact on official statistics. While there are good reasons for this, FAIR developments within the research community may be of interest to official statistical organizations. These include the increased availability of research data, improvements in the area of machine-actionable metadata, and a focus on provenance information which could lead to increased transparency and data quality. Some activities of interest are described as a starting point for those in official statistics who may wish to follow these developments.","PeriodicalId":509522,"journal":{"name":"Statistical Journal of the IAOS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241645","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}
{"title":"New Developments In Science Publishing In Official Statistics (Open Data, FAIR Publishing, And The Challenges For Science Publishing To Stay Relevant)","authors":"Pieter Everaers","doi":"10.3233/sji-240022","DOIUrl":"https://doi.org/10.3233/sji-240022","url":null,"abstract":"","PeriodicalId":509522,"journal":{"name":"Statistical Journal of the IAOS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241458","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}
{"title":"SJIAOS Discussion Platform","authors":"","doi":"10.3233/sji-240010","DOIUrl":"https://doi.org/10.3233/sji-240010","url":null,"abstract":"","PeriodicalId":509522,"journal":{"name":"Statistical Journal of the IAOS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140241045","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}
Stephen Penneck, John Bailer, Ed Humpherson, Mariana Kotzeva, Denise Silva
What qualities are needed by statisticians to achieve top leadership positions in academia, business, industry and government? Five leaders from statistical societies, national and international statistical offices, and academia share their experiences. They respond to five specific questions. Firstly, is leadership just needed by top management or do all statisticians have a role? If so, what is it? Secondly, do statisticians naturally make good leaders? What new skills do they need to acquire? What skills advantages do they have? Thirdly, the panel consider the question: did you work for people who were not good leaders? How did they fall short? What good role models did panellists have? And then, is it harder for women, and for other under-represented groups? And finally they were asked: what message do you have for young statisticians aspiring to leadership roles?
{"title":"Reflections on statistical leadership: Summary of a panel discussion at the WSC 20231","authors":"Stephen Penneck, John Bailer, Ed Humpherson, Mariana Kotzeva, Denise Silva","doi":"10.3233/sji-230123","DOIUrl":"https://doi.org/10.3233/sji-230123","url":null,"abstract":"What qualities are needed by statisticians to achieve top leadership positions in academia, business, industry and government? Five leaders from statistical societies, national and international statistical offices, and academia share their experiences. They respond to five specific questions. Firstly, is leadership just needed by top management or do all statisticians have a role? If so, what is it? Secondly, do statisticians naturally make good leaders? What new skills do they need to acquire? What skills advantages do they have? Thirdly, the panel consider the question: did you work for people who were not good leaders? How did they fall short? What good role models did panellists have? And then, is it harder for women, and for other under-represented groups? And finally they were asked: what message do you have for young statisticians aspiring to leadership roles?","PeriodicalId":509522,"journal":{"name":"Statistical Journal of the IAOS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139613887","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}
Jennifer D. Parker, Lisa B. Mirel, Philip Lee, Ryan Mintz, Andrew Tungate, Ambarish Vaidyanathan
In 2020 the U.S. Federal Committee on Statistical Methodology (FCSM) released “A Framework for Data Quality”, organized by 11 dimensions of data quality grouped among three domains of quality (utility, objectivity, integrity). This paper addresses the use of the FCSM Framework for data quality assessments of blended data. The FCSM Framework applies to all types of data, however best practices for implementation have not been documented. We applied the FCSM Framework for three health-research related case studies. For each case study, assessments of data quality dimensions were performed to identify threats to quality, possible mitigations of those threats, and trade-offs among them. From these assessments the authors concluded: 1) data quality assessments are more complex in practice than anticipated and expert guidance and documentation are important; 2) each dimension may not be equally important for different data uses; 3) data quality assessments can be subjective and having a quantitative tool could help explain the results, however, quantitative assessments may be closely tied to the intended use of the dataset; 4) there are common trade-offs and mitigations for some threats to quality among dimensions. This paper is one of the first to apply the FCSM Framework to specific use-cases and illustrates a process for similar data uses.
{"title":"Evaluating data quality for blended data using a data quality framework","authors":"Jennifer D. Parker, Lisa B. Mirel, Philip Lee, Ryan Mintz, Andrew Tungate, Ambarish Vaidyanathan","doi":"10.3233/sji-230125","DOIUrl":"https://doi.org/10.3233/sji-230125","url":null,"abstract":"In 2020 the U.S. Federal Committee on Statistical Methodology (FCSM) released “A Framework for Data Quality”, organized by 11 dimensions of data quality grouped among three domains of quality (utility, objectivity, integrity). This paper addresses the use of the FCSM Framework for data quality assessments of blended data. The FCSM Framework applies to all types of data, however best practices for implementation have not been documented. We applied the FCSM Framework for three health-research related case studies. For each case study, assessments of data quality dimensions were performed to identify threats to quality, possible mitigations of those threats, and trade-offs among them. From these assessments the authors concluded: 1) data quality assessments are more complex in practice than anticipated and expert guidance and documentation are important; 2) each dimension may not be equally important for different data uses; 3) data quality assessments can be subjective and having a quantitative tool could help explain the results, however, quantitative assessments may be closely tied to the intended use of the dataset; 4) there are common trade-offs and mitigations for some threats to quality among dimensions. This paper is one of the first to apply the FCSM Framework to specific use-cases and illustrates a process for similar data uses.","PeriodicalId":509522,"journal":{"name":"Statistical Journal of the IAOS","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139628724","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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