Dataset Information

Data Extraction from Graphs Using Adobe Photoshop: Applications for Meta-Analyses.

ABSTRACT: Graphs, an effective form of data presentation, are used for summarizing complex information and making them easier to understand. Extracting numerical data from graphs, which is commonly required in systematic reviews and meta-analyses, is however a challenging issue. Since this kind of results presentation is common, ignorance of such data may result in publication bias when conducting meta-analyses. On the other hand, contacting the authors of a particular publication in order to retrieve the data may take a long time and is often not very fruitful. In this case, there are a few software and methods that may be used for data extraction; however, using these software is costly and not simple as well as different types of graphs need different extraction methods. Here, we have described a simple reproducible method for extracting data from graphs using Adobe Photoshop.

SUBMITTER: Gheibi S

PROVIDER: S-EPMC6942170 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Data Extraction from Graphs Using Adobe Photoshop: Applications for Meta-Analyses.

Gheibi Sevda S Mahmoodzadeh Alireza A Kashfi Khosrow K Jeddi Sajad S Ghasemi Asghar A

International journal of endocrinology and metabolism 20191008 4

Graphs, an effective form of data presentation, are used for summarizing complex information and making them easier to understand. Extracting numerical data from graphs, which is commonly required in systematic reviews and meta-analyses, is however a challenging issue. Since this kind of results presentation is common, ignorance of such data may result in publication bias when conducting meta-analyses. On the other hand, contacting the authors of a particular publication in order to retrieve the ...[more]

PMID: 31998381

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Project description:OBJECTIVES:Systematic reviews and meta-analyses are labor-intensive and time-consuming. Automated extraction of quantitative data from primary studies can accelerate this process. ClinicalTrials.gov, launched in 2000, is the world's largest trial repository of results data from clinical trials; it has been used as a source instead of journal articles. We have developed a Web application called EXACT (EXtracting Accurate efficacy and safety information from ClinicalTrials.gov) that allows users without advanced programming skills to automatically extract data from ClinicalTrials.gov in analysis-ready format. We have also used the automatically extracted data to examine the reproducibility of meta-analyses in three published systematic reviews. STUDY DESIGN AND SETTING:We developed a Python-based software application (EXACT) that automatically extracts data required for meta-analysis from the ClinicalTrials.gov database in a spreadsheet format. We confirmed the accuracy of the extracted data and then used those data to repeat meta-analyses in three published systematic reviews. To ensure that we used the same statistical methods and outcomes as the published systematic reviews, we repeated the meta-analyses using data manually extracted from the relevant journal articles. For the outcomes whose results we were able to reproduce using those journal article data, we examined the usability of ClinicalTrials.gov data. RESULTS:EXACT extracted data at ClincalTrials.gov with 100% accuracy, and it required 60% less time than the usual practice of manually extracting data from journal articles. We found that 87% of the data elements extracted using EXACT matched those extracted manually from the journal articles. We were able to reproduce 24 of 28 outcomes using the journal article data. Of these 24 outcomes, we were able to reproduce 83.3% of the published estimates using data at ClinicalTrials.gov. CONCLUSION:EXACT (http://bio-nlp.org/EXACT) automatically and accurately extracted data elements from ClinicalTrials.gov and thus reduced time in data extraction. The ClinicalTrials.gov data reproduced most meta-analysis results in our study, but this conclusion needs further validation.

Project description:Background:The amount of plant data such as taxonomical classification, morphological characteristics, ecological attributes and geological distribution in textual and image forms has increased rapidly due to emerging research and technologies. Therefore, it is crucial for experts as well as the public to discern meaningful relationships from this vast amount of data using appropriate methods. The data are often presented in lengthy texts and tables, which make gaining new insights difficult. The study proposes a visual-based representation to display data to users in a meaningful way. This method emphasises the relationships between different data sets. Method:This study involves four main steps which translate text-based results from Extensible Markup Language (XML) serialisation format into graphs. The four steps include: (1) conversion of ontological dataset as graph model data; (2) query from graph model data; (3) transformation of text-based results in XML serialisation format into a graphical form; and (4) display of results to the user via a graphical user interface (GUI). Ontological data for plants and samples of trees and shrubs were used as the dataset to demonstrate how plant-based data could be integrated into the proposed data visualisation. Results:A visualisation system named plant visualisation system was developed. This system provides a GUI that enables users to perform the query process, as well as a graphical viewer to display the results of the query in the form of a network graph. The efficiency of the developed visualisation system was measured by performing two types of user evaluations: a usability heuristics evaluation, and a query and visualisation evaluation. Discussion:The relationships between the data were visualised, enabling the users to easily infer the knowledge and correlations between data. The results from the user evaluation show that the proposed visualisation system is suitable for both expert and novice users, with or without computer skills. This technique demonstrates the practicability of using a computer assisted-tool by providing cognitive analysis for understanding relationships between data. Therefore, the results benefit not only botanists, but also novice users, especially those that are interested to know more about plants.

Dataset Information

Data Extraction from Graphs Using Adobe Photoshop: Applications for Meta-Analyses.

Publications

Data Extraction from Graphs Using Adobe Photoshop: Applications for Meta-Analyses.

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