More Like this

1. The mwtab Python Library for RESTful Access and Enhanced Quality Control, Deposition, and Curation of the Metabolomics Workbench Data Repository

   https://doi.org/10.3390/metabo11030163  

   Powell, Christian D.; Moseley, Hunter N.B. (March 2021, Metabolites)

   null (Ed.)

   The Metabolomics Workbench (MW) is a public scientific data repository consisting of experimental data and metadata from metabolomics studies collected with mass spectroscopy (MS) and nuclear magnetic resonance (NMR) analyses. MW has been constantly evolving; updating its ‘mwTab’ text file format, adding a JavaScript Object Notation (JSON) file format, implementing a REpresentational State Transfer (REST) interface, and nearly quadrupling the number of datasets hosted on the repository within the last three years. In order to keep up with the quickly evolving state of the MW repository, the ‘mwtab’ Python library and package have been continuously updated to mirror the changes in the ‘mwTab’ and JSONized formats and contain many new enhancements including methods for interacting with the MW REST interface, enhanced format validation features, and advanced features for parsing and searching for specific metabolite data and metadata. We used the enhanced format validation features to evaluate all available datasets in MW to facilitate improved curation and FAIRness of the repository. The ‘mwtab’ Python package is now officially released as version 1.0.1 and is freely available on GitHub and the Python Package Index (PyPI) under a Clear Berkeley Software Distribution (BSD) license with documentation available on ReadTheDocs. 

   more » « less
2. The ProteomeXchange consortium in 2020: enabling ‘big data’ approaches in proteomics

   https://doi.org/10.1093/nar/gkz984  

   Deutsch, Eric W; Bandeira, Nuno; Sharma, Vagisha; Perez-Riverol, Yasset; Carver, Jeremy J; Kundu, Deepti J; García-Seisdedos, David; Jarnuczak, Andrew F; Hewapathirana, Suresh; Pullman, Benjamin S; et al (November 2019, Nucleic Acids Research)

   Abstract The ProteomeXchange (PX) consortium of proteomics resources (http://www.proteomexchange.org) has standardized data submission and dissemination of mass spectrometry proteomics data worldwide since 2012. In this paper, we describe the main developments since the previous update manuscript was published in Nucleic Acids Research in 2017. Since then, in addition to the four PX existing members at the time (PRIDE, PeptideAtlas including the PASSEL resource, MassIVE and jPOST), two new resources have joined PX: iProX (China) and Panorama Public (USA). We first describe the updated submission guidelines, now expanded to include six members. Next, with current data submission statistics, we demonstrate that the proteomics field is now actively embracing public open data policies. At the end of June 2019, more than 14 100 datasets had been submitted to PX resources since 2012, and from those, more than 9 500 in just the last three years. In parallel, an unprecedented increase of data re-use activities in the field, including ‘big data’ approaches, is enabling novel research and new data resources. At last, we also outline some of our future plans for the coming years. 

   more » « less
3. MESSES: Software for Transforming Messy Research Datasets into Clean Submissions to Metabolomics Workbench for Public Sharing

   https://doi.org/10.3390/metabo13070842  

   Thompson, P Travis; Moseley, Hunter_N B (July 2023, Metabolites)

   In recent years, the FAIR guiding principles and the broader concept of open science has grown in importance in academic research, especially as funding entities have aggressively promoted public sharing of research products. Key to public research sharing is deposition of datasets into online data repositories, but it can be a chore to transform messy unstructured data into the forms required by these repositories. To help generate Metabolomics Workbench depositions, we have developed the MESSES (Metadata from Experimental SpreadSheets Extraction System) software package, implemented in the Python 3 programming language and supported on Linux, Windows, and Mac operating systems. MESSES helps transform tabular data from multiple sources into a Metabolomics Workbench specific deposition format. The package provides three commands, extract, validate, and convert, that implement a natural data transformation workflow. Moreover, MESSES facilitates richer metadata capture than is typically attempted by manual efforts. The source code and extensive documentation is hosted on GitHub and is also available on the Python Package Index for easy installation. 

   more » « less
4. A proposed FAIR approach for disseminating geospatial information system maps

   https://doi.org/10.1038/s41597-023-02281-1  

   Thompson, P. Travis; Ojha, Sweta; Powell, Christian D.; Pennell, Kelly G.; Moseley, Hunter N. B. (June 2023, Scientific Data)

   Abstract We present a draft Minimum Information About Geospatial Information System (MIAGIS) standard for facilitating public deposition of geospatial information system (GIS) datasets that follows the FAIR (Findable, Accessible, Interoperable and Reusable) principles. The draft MIAGIS standard includes a deposition directory structure and a minimum javascript object notation (JSON) metadata formatted file that is designed to capture critical metadata describing GIS layers and maps as well as their sources of data and methods of generation. The associated miagis Python package facilitates the creation of this MIAGIS metadata file and directly supports metadata extraction from both Esri JSON and GEOJSON GIS data formats plus options for extraction from user-specified JSON formats. We also demonstrate their use in crafting two example depositions of ArcGIS generated maps. We hope this draft MIAGIS standard along with the supporting miagis Python package will assist in establishing a GIS standards group that will develop the draft into a full standard for the wider GIS community as well as a future public repository for GIS datasets. 

   more » « less
5. An Architecture for Cell-Centric Indexing of Datasets

   Qiu, Lixuan; Jia, Haiyan; Davison, Brian D.; Heflin, Jeff (December 2020, CEUR workshop proceedings)

   null (Ed.)

   ncreasingly, large collections of datasets are made available to the public via the Web, ranging from government-curated datasets like those of data.gov to communally-sourced datasets such as Wikipedia tables. It has become clear that traditional search techniques are insufficient for such sources, especially when the user is unfamiliar with the terminology used by the creators of the relevant datasets. We propose to address this problem by elevating the datum to a first-class object that is indexed, thereby making it less dependent on how a dataset is structured. In a data table, a cell contains a value for a particular row as described by a particular column. In our cell-centric indexing approach, we index the metadata of each cell, so that information about its dataset and column simply become metadata rather than constraining concepts. In this paper we define cell-centric indexing and present a system architecture that supports its use in exploring datasets. We describe how cell-centric indexing can be implemented using traditional information retrieval technology and evaluate the scalability of the architecture. 

   more » « less