| Title |
A Python library for FAIRer access and deposition to the Metabolomics Workbench Data Repository
|
|---|---|
| Published in |
Metabolomics, April 2018
|
| DOI | 10.1007/s11306-018-1356-6 |
| Pubmed ID | |
| Authors |
Andrey Smelter, Hunter N. B. Moseley |
| Abstract |
The Metabolomics Workbench Data Repository is a public repository of mass spectrometry and nuclear magnetic resonance data and metadata derived from a wide variety of metabolomics studies. The data and metadata for each study is deposited, stored, and accessed via files in the domain-specific 'mwTab' flat file format. In order to improve the accessibility, reusability, and interoperability of the data and metadata stored in 'mwTab' formatted files, we implemented a Python library and package. This Python package, named 'mwtab', is a parser for the domain-specific 'mwTab' flat file format, which provides facilities for reading, accessing, and writing 'mwTab' formatted files. Furthermore, the package provides facilities to validate both the format and required metadata elements of a given 'mwTab' formatted file. In order to develop the 'mwtab' package we used the official 'mwTab' format specification. We used Git version control along with Python unit-testing framework as well as continuous integration service to run those tests on multiple versions of Python. Package documentation was developed using sphinx documentation generator. The 'mwtab' package provides both Python programmatic library interfaces and command-line interfaces for reading, writing, and validating 'mwTab' formatted files. Data and associated metadata are stored within Python dictionary- and list-based data structures, enabling straightforward, 'pythonic' access and manipulation of data and metadata. Also, the package provides facilities to convert 'mwTab' files into a JSON formatted equivalent, enabling easy reusability of the data by all modern programming languages that implement JSON parsers. The 'mwtab' package implements its metadata validation functionality based on a pre-defined JSON schema that can be easily specialized for specific types of metabolomics studies. The library also provides a command-line interface for interconversion between 'mwTab' and JSONized formats in raw text and a variety of compressed binary file formats. The 'mwtab' package is an easy-to-use Python package that provides FAIRer utilization of the Metabolomics Workbench Data Repository. The source code is freely available on GitHub and via the Python Package Index. Documentation includes a 'User Guide', 'Tutorial', and 'API Reference'. The GitHub repository also provides 'mwtab' package unit-tests via a continuous integration service. |
X Demographics
The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 16% |
| Australia | 1 | 5% |
| Germany | 1 | 5% |
| United Kingdom | 1 | 5% |
| Japan | 1 | 5% |
| Unknown | 12 | 63% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 53% |
| Scientists | 8 | 42% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 28% |
| Student > Ph. D. Student | 6 | 24% |
| Student > Master | 3 | 12% |
| Other | 1 | 4% |
| Librarian | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 5 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 5 | 20% |
| Computer Science | 2 | 8% |
| Engineering | 2 | 8% |
| Chemistry | 2 | 8% |
| Social Sciences | 2 | 8% |
| Other | 4 | 16% |
| Unknown | 8 | 32% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 November 2019.
All research outputs
#3,291,558
of 24,143,470 outputs
Outputs from Metabolomics
#159
of 1,342 outputs
Outputs of similar age
#65,553
of 330,818 outputs
Outputs of similar age from Metabolomics
#7
of 33 outputs
Altmetric has tracked 24,143,470 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,342 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has done well, scoring higher than 88% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 330,818 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.