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Datastorr: a workflow and package for delivering successive versions of 'evolving data' directly into R.

ABSTRACT: The sharing and re-use of data has become a cornerstone of modern science. Multiple platforms now allow easy publication of datasets. So far, however, platforms for data sharing offer limited functions for distributing and interacting with evolving datasets- those that continue to grow with time as more records are added, errors fixed, and new data structures are created. In this article, we describe a workflow for maintaining and distributing successive versions of an evolving dataset, allowing users to retrieve and load different versions directly into the R platform. Our workflow utilizes tools and platforms used for development and distribution of successive versions of an open source software program, including version control, GitHub, and semantic versioning, and applies these to the analogous process of developing successive versions of an open source dataset. Moreover, we argue that this model allows for individual research groups to achieve a dynamic and versioned model of data delivery at no cost.

SUBMITTER: Falster DS

PROVIDER: S-EPMC6506717 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Datastorr: a workflow and package for delivering successive versions of 'evolving data' directly into R.

Falster Daniel S DS FitzJohn Richard G RG Pennell Matthew W MW Cornwell William K WK

GigaScience 20190501 5

The sharing and re-use of data has become a cornerstone of modern science. Multiple platforms now allow easy publication of datasets. So far, however, platforms for data sharing offer limited functions for distributing and interacting with evolving datasets- those that continue to grow with time as more records are added, errors fixed, and new data structures are created. In this article, we describe a workflow for maintaining and distributing successive versions of an evolving dataset, allowing ...[more]

PMID: 31042286

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Project description:IntroductionFor many Kenyans, high-quality primary health care (PHC) services remain unavailable, inaccessible, or unaffordable. To address these challenges, the Government of Kenya has committed to strengthening the country's PHC system by introducing a comprehensive package of PHC services and promoting the efficient use of existing resources through its primary care network approach. Our study estimated the costs of delivering PHC services in public sector facilities in seven sub-counties, comparing actual costs to normative costs of delivering Kenya's PHC package and determining the corresponding financial resource gap to achieving universal coverage.MethodsWe collected primary data from a sample of 71 facilities, including dispensaries, health centers, and sub-county hospitals. Data on facility-level recurrent costs were collected retrospectively for 1 year (2018-2019) to estimate economic costs from the public sector perspective. Total actual costs from the sampled facilities were extrapolated using service utilization data from the Kenya Health Information System for the universe of facilities to obtain sub-county and national PHC cost estimates. Normative costs were estimated based on standard treatment protocols and the populations in need of PHC in each sub-county.Results and discussionThe average actual PHC cost per capita ranged from US$ 9.3 in Ganze sub-county to US$ 47.2 in Mukurweini while the normative cost per capita ranged from US$ 31.8 in Ganze to US$ 42.4 in Kibwezi West. With the exception of Mukurweini (where there was no financial resource gap), closing the resource gap would require significant increases in PHC expenditures and/or improvements to increase the efficiency of PHC service delivery such as improved staff distribution, increased demand for services and patient loads per clinical staff, and reduced bypass to higher level facilities. This study offers valuable evidence on sub-national cost variations and resource requirements to guide the implementation of the government's PHC reforms and resource mobilization efforts.

Dataset Information

Datastorr: a workflow and package for delivering successive versions of 'evolving data' directly into R.

Publications

Datastorr: a workflow and package for delivering successive versions of 'evolving data' directly into R.

Similar Datasets

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