Data Management Strategy, Policy and Standard

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (31 July 2018) | Viewed by 5625

Special Issue Editor

State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road A11, Chaoyang District, Beijing 100101, China
Interests: data sharing; data standards; geosciences data integration; metadata; remote sensing applications; geographic information systems; data publication; geography grid; resources and environmental databases

Special Issue Information

Dear Colleagues,

With the advent of the big data era, a large amount of scientific data has been continuously generated. An important change has taken place in the data-driven scientific research methods. The scientific research model characterized by data-intensive has been emerging. Scientific discoveries are increasingly dependent on the collection, management and analysis of vast amounts data. The level of scientific research is also increasingly dependent on the ability to accumulate data and convert data to information and knowledge. Scientific data has become an indispensable supporting condition for scientific and technological innovation, economic development and related decision-making. However, how to accumulate, manage and utilize these scientific data resources is an urgent need in all fields at present.

We would like to invite you to submit articles addressing the strategy, policy and standards of data management from different region scales or subject view, so that these data management experiences in different region and subjects can be shared and reference each other, and then push forward the data management and application level.

Dr. Juanle Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Global/national/regional data management strategy
  • Data sharing policy
  • Subject data depository (e.g., Earth science, life science, social science, etc.)
  • Data curation standard
  • Data driven science model

Published Papers (1 paper)

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Research

16 pages, 3749 KiB  
Article
Data Quality: A Negotiator between Paper-Based and Digital Records in Pakistan’s TB Control Program
by Syed Mustafa Ali, Farah Naureen, Arif Noor, Maged N. Kamel Boulos, Javariya Aamir, Muhammad Ishaq, Naveed Anjum, John Ainsworth, Aamna Rashid, Arman Majidulla and Irum Fatima
Data 2018, 3(3), 27; https://doi.org/10.3390/data3030027 - 19 Jul 2018
Cited by 4 | Viewed by 5195
Abstract
Background: The cornerstone of the public health function is to identify healthcare needs, to influence policy development, and to inform change in practice. Current data management practices with paper-based recording systems are prone to data quality defects. Increasingly, healthcare organizations are using technology [...] Read more.
Background: The cornerstone of the public health function is to identify healthcare needs, to influence policy development, and to inform change in practice. Current data management practices with paper-based recording systems are prone to data quality defects. Increasingly, healthcare organizations are using technology for the efficient management of data. The aim of this study was to compare the data quality of digital records with the quality of the corresponding paper-based records using a data quality assessment framework. Methodology: We conducted a desk review of paper-based and digital records over the study duration from April 2016 to July 2016 at six enrolled tuberculosis (TB) clinics. We input all data fields of the patient treatment (TB01) card into a spreadsheet-based template to undertake a field-to-field comparison of the shared fields between TB01 and digital data. Findings: A total of 117 TB01 cards were prepared at six enrolled sites, whereas just 50% of the records (n = 59; 59 out of 117 TB01 cards) were digitized. There were 1239 comparable data fields, out of which 65% (n = 803) were correctly matched between paper based and digital records. However, 35% of the data fields (n = 436) had anomalies, either in paper-based records or in digital records. The calculated number of data quality issues per digital patient record was 1.9, whereas it was 2.1 issues per record for paper-based records. Based on the analysis of valid data quality issues, it was found that there were more data quality issues in paper-based records (n = 123) than in digital records (n = 110). Conclusion: There were fewer data quality issues in digital records as compared with the corresponding paper-based records of tuberculosis patients. Greater use of mobile data capture and continued data quality assessment can deliver more meaningful information for decision making. Full article
(This article belongs to the Special Issue Data Management Strategy, Policy and Standard)
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