Dataset Information

Assessing the uptake of persistent identifiers by research infrastructure users.

ABSTRACT: Significant progress has been made in the past few years in the development of recommendations, policies, and procedures for creating and promoting citations to data sets, software, and other research infrastructures like computing facilities. Open questions remain, however, about the extent to which referencing practices of authors of scholarly publications are changing in ways desired by these initiatives. This paper uses four focused case studies to evaluate whether research infrastructures are being increasingly identified and referenced in the research literature via persistent citable identifiers. The findings of the case studies show that references to such resources are increasing, but that the patterns of these increases are variable. In addition, the study suggests that citation practices for data sets may change more slowly than citation practices for software and research facilities, due to the inertia of existing practices for referencing the use of data. Similarly, existing practices for acknowledging computing support may slow the adoption of formal citations for computing resources.

SUBMITTER: Mayernik MS

PROVIDER: S-EPMC5386254 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessing the uptake of persistent identifiers by research infrastructure users.

Mayernik Matthew S MS Maull Keith E KE

PloS one 20170410 4

Significant progress has been made in the past few years in the development of recommendations, policies, and procedures for creating and promoting citations to data sets, software, and other research infrastructures like computing facilities. Open questions remain, however, about the extent to which referencing practices of authors of scholarly publications are changing in ways desired by these initiatives. This paper uses four focused case studies to evaluate whether research infrastructures a ...[more]

PMID: 28394907

Similar Datasets

Project description:Clinical research infrastructure is one of the unsung heroes of the scientific response to the current COVID-19 pandemic. The extensive, long-term funding into research support structures, skilled people, and technology allowed the United Kingdom research response to move off the starting blocks at pace by utilizing pre-existing platforms. The increasing focus from funders on evaluating the outcomes and impact of research infrastructure investment requires both a reframing and progression of the current models in order to address the contribution of the underlying support infrastructure. The majority of current evaluation/outcome models focus on a "pipeline" approach using a methodology which follows the traditional research funding route with the addition of quantitative metrics. These models fail to embrace the complexity caused by the interplay of previous investment, the coalescing of project outputs from different funders, the underlying infrastructure investment, and the parallel development across different parts of the system. Research infrastructure is the underpinning foundation of a project-driven research system and requires long-term, sustained funding and capital investment to maintain scientific and technological expertise. Therefore, the short-term focus on quantitative metrics that are easy to collect and interpret and that can be assessed in a roughly 5-year funding cycle needs to be addressed. The significant level of investment in research infrastructure necessitates investment to develop bespoke methodologies that develop fit-for-purpose, longer-term/continual approach(es) to evaluation. Real-world research should reflect real-world evaluation and allow for the accrual of a narrative of value indicators that build a picture of the contribution of infrastructure to research outcomes. The linear approach is not fit for purpose, the research endeavour is a complex, twisted road, and the evaluation approach needs to embrace this complexity through the development of realist approaches and the rapidly evolving data ecosystem. This paper sets out methodological challenges and considers the need to develop bespoke methodological approaches to allow a richer assessment of impact, contribution, attribution, and evaluation of research infrastructure. This paper is the beginning of a conversation that invites the community to "take up the mantle" and tackle the complexity of real-world research translation and evaluation.

Project description:BackgroundPrivacy should be protected in medical data that include patient information. A distributed research network (DRN) is one of the challenges in privacy protection and in the encouragement of multi-institutional clinical research. A DRN standardizes multi-institutional data into a common structure and terminology called a common data model (CDM), and it only shares analysis results. It is necessary to measure how a DRN protects patient information privacy even without sharing data in practice.ObjectiveThis study aimed to quantify the privacy risk of a DRN by comparing different deidentification levels focusing on personal health identifiers (PHIs) and quasi-identifiers (QIs).MethodsWe detected PHIs and QIs in an Observational Medical Outcomes Partnership (OMOP) CDM as threatening privacy, based on 18 Health Insurance Portability and Accountability Act of 1996 (HIPPA) identifiers and previous studies. To compare the privacy risk according to the different privacy policies, we generated limited and safe harbor data sets based on 16 PHIs and 12 QIs as threatening privacy from the Synthetic Public Use File 5 Percent (SynPUF5PCT) data set, which is a public data set of the OMOP CDM. With minimum cell size and equivalence class methods, we measured the privacy risk reduction with a trust differential gap obtained by comparing the two data sets. We also measured the gap in randomly sampled records from the two data sets to adjust the number of PHI or QI records.ResultsThe gaps averaged 31.448% and 73.798% for PHIs and QIs, respectively, with a minimum cell size of one, which represents a unique record in a data set. Among PHIs, the national provider identifier had the highest gap of 71.236% (71.244% and 0.007% in the limited and safe harbor data sets, respectively). The maximum size of the equivalence class, which has the largest size of an indistinguishable set of records, averaged 771. In 1000 random samples of PHIs, Device_exposure_start_date had the highest gap of 33.730% (87.705% and 53.975% in the data sets). Among QIs, Death had the highest gap of 99.212% (99.997% and 0.784% in the data sets). In 1000, 10,000, and 100,000 random samples of QIs, Device_treatment had the highest gaps of 12.980% (99.980% and 87.000% in the data sets), 60.118% (99.831% and 39.713%), and 93.597% (98.805% and 5.207%), respectively, and in 1 million random samples, Death had the highest gap of 99.063% (99.998% and 0.934% in the data sets).ConclusionsIn this study, we verified and quantified the privacy risk of PHIs and QIs in the DRN. Although this study used limited PHIs and QIs for verification, the privacy limitations found in this study could be used as a quality measurement index for deidentification of multi-institutional collaboration research, thereby increasing DRN safety.

Dataset Information

Assessing the uptake of persistent identifiers by research infrastructure users.

Publications

Assessing the uptake of persistent identifiers by research infrastructure users.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets