Open-source Data Management System Research Articles

Medna-metadata: an open-source data management system for tracking environmental DNA samples and metadata.

MotivationEnvironmental DNA (eDNA), as a rapidly expanding research field, stands to benefit from shared resources including sampling protocols, study designs, discovered sequences, and taxonomic assignments to sequences. High-quality community shareable eDNA resources rely heavily on comprehensive metadata documentation that captures the complex workflows covering field sampling, molecular biology lab work, and bioinformatic analyses. There are limited sources that provide documentation of database development on comprehensive metadata for eDNA and these workflows and no open-source software.ResultsWe present medna-metadata, an open-source, modular system that aligns with Findable, Accessible, Interoperable, and Reusable guiding principles that support scholarly data reuse and the database and application development of a standardized metadata collection structure that encapsulates critical aspects of field data collection, wet lab processing, and bioinformatic analysis. Medna-metadata is showcased with metabarcoding data from the Gulf of Maine (Polinski et al., 2019).Availability and implementationThe source code of the medna-metadata web application is hosted on GitHub (https://github.com/Maine-eDNA/medna-metadata). Medna-metadata is a docker-compose installable package. Documentation can be found at https://medna-metadata.readthedocs.io/en/latest/?badge=latest. The application is implemented in Python, PostgreSQL and PostGIS, RabbitMQ, and NGINX, with all major browsers supported. A demo can be found at https://demo.metadata.maine-edna.org/.Supplementary information Supplementary data are available at Bioinformatics online.

Open-source data management system for Parkinson's disease follow-up.

BackgroundParkinson’s disease (PD) is a neurodegenerative condition of the central nervous system that causes motor and non-motor dysfunctions. The disease affects 1% of the world population over 60 years and remains cureless. Knowledge and monitoring of PD are essential to provide better living conditions for patients. Thus, diagnostic exams and monitoring of the disease can generate a large amount of data from a given patient. This study proposes the development and usability evaluation of an integrated system, which can be used in clinical and research settings to manage biomedical data collected from PD patients.MethodsA system, so-called Sistema Integrado de Dados Biomédicos (SIDABI) (Integrated Biomedical Data System), was designed following the model-view-controller (MVC) standard. A modularized architecture was created in which all the other modules are connected to a central security module. Thirty-six examiners evaluated the system usability through the System Usability Scale (SUS). The agreement between examiners was measured by Kendall’s coefficient with a significance level of 1%.ResultsThe free and open-source web-based system was implemented using modularized and responsive methods to adapt the system features on multiple platforms. The mean SUS score was 82.99 ± 13.97 points. The overall agreement was 70.2%, as measured by Kendall’s coefficient (p < 0.001).ConclusionAccording to the SUS scores, the developed system has good usability. The system proposed here can help researchers to organize and share information, avoiding data loss and fragmentation. Furthermore, it can help in the follow-up of PD patients, in the training of professionals involved in the treatment of the disorder, and in studies that aim to find hidden correlations in data.

Evolution of the open-source data management system Ru-cio for LHC Run-3 and beyond ATLAS

Rucio, the distributed data management system of the ATLAS experiment already manages more than 400 Petabytes of physics data on the grid. Rucio was incrementally improved throughout LHC Run-2 and is currently being prepared for the HL-LHC era of the experiment. Next to these improvements the system is currently evolving into a full-scale generic data management system for application beyond ATLAS, or even beyond high-energy physics. This contribution focuses on the development roadmap of Rucio for LHC Run-3, such as event level data management, generic meta-data support and increased usage of networks and tapes. At the same time Rucio is evolving beyond the original ATLAS requirements. This includes additional authentication mechanisms, generic database compatibility, deployment and packaging of the software stack in containers, and a project paradigm shift to a full-scale open source project..

Panorama Public: A Public Repository for Quantitative Data Sets Processed in Skyline

To address the growing need for a centralized, community resource of published results processed with Skyline, and to provide reviewers and readers immediate visual access to the data behind published conclusions, we present Panorama Public (https://panoramaweb.org/public.url), a repository of Skyline documents supporting published results. Panorama Public is built on Panorama, an open source data management system for mass spectrometry data processed with the Skyline targeted mass spectrometry environment. The Panorama web application facilitates viewing, sharing, and disseminating results contained in Skyline documents via a web-browser. Skyline users can easily upload their documents to a Panorama server and allow other researchers to explore uploaded results in the Panorama web-interface through a variety of familiar summary graphs as well as annotated views of the chromatographic peaks processed with Skyline. This makes Panorama ideal for sharing targeted, quantitative results contained in Skyline documents with collaborators, reviewers, and the larger proteomics community. The Panorama Public repository employs the full data visualization capabilities of Panorama which facilitates sharing results with reviewers during manuscript review.

Experience of using an open source clinical trials data management software system in Kenya.

BackgroundClinical trials data management (CTDM) remains one of the many challenges in running state of the art trials in resource-poor settings since most trials do not allocate, or have available, sufficient resources for CTDM and because of poor internet connectivity. Open-source software like OpenClinica could be a solution in such scenarios.FindingsIn 2007, the KEMRI-Wellcome Trust Research Programme (KWTRP) adopted OpenClinica (OC) community edition, an open-source software system and we share our experience and lessons learnt since its adoption. We have used OC in three different modes; direct remote data entry from sites through Global System for Mobile Communications (GSM) modems, a centralized data centre approach where all data from paper records were entered at a central location and an off-line approach where data entry was done from a copy of database hosted on a field-site server laptop, then data uploaded to a centralized server later. We have used OC in eleven trials/studies with a cumulative number of participants in excess of 6000. These include large and complex trials, with multiple sites recruiting in different regions of East Africa. In the process, we have developed substantial local capacity through hands-on training and mentorship, which we have now begun to share with other institutions in the region.ConclusionsOur experience demonstrates that an open source data management system to manage trials’ data can be utilized to international industry standards in resource-poor countries.

The design of a query monitoring system

Query monitoring refers to the problem of observing and predicting various parameters related to the execution of a query in a database system. In addition to being a useful tool for database users and administrators, it can also serve as an information collection service for resource allocation and adaptive query processing techniques. In this article, we present a query monitoring system from the ground up, describing various new techniques for query monitoring, their implementation inside a real database system, and a novel interface that presents the observed and predicted information in an accessible manner. To enable this system, we introduce several lightweight online techniques for progressively estimating and refining the cardinality of different relational operators using information collected at query execution time. These include binary and multiway joins as well as typical grouping operations and combinations thereof. We describe the various algorithms used to efficiently implement estimators and present the results of an evaluation of a prototype implementation of our framework in an open-source data management system. Our results demonstrate the feasibility and practical utility of the approach presented herein.

JUICE: a data management system that facilitates the analysis of large volumes of information in an EST project workflow

BackgroundExpressed sequence tag (EST) analyses provide a rapid and economical means to identify candidate genes that may be involved in a particular biological process. These ESTs are useful in many Functional Genomics studies. However, the large quantity and complexity of the data generated during an EST sequencing project can make the analysis of this information a daunting task.ResultsIn an attempt to make this task friendlier, we have developed JUICE, an open source data management system (Apache + PHP + MySQL on Linux), which enables the user to easily upload, organize, visualize and search the different types of data generated in an EST project pipeline. In contrast to other systems, the JUICE data management system allows a branched pipeline to be established, modified and expanded, during the course of an EST project.The web interfaces and tools in JUICE enable the users to visualize the information in a graphical, user-friendly manner. The user may browse or search for sequences and/or sequence information within all the branches of the pipeline. The user can search using terms associated with the sequence name, annotation or other characteristics stored in JUICE and associated with sequences or sequence groups. Groups of sequences can be created by the user, stored in a clipboard and/or downloaded for further analyses.Different user profiles restrict the access of each user depending upon their role in the project. The user may have access exclusively to visualize sequence information, access to annotate sequences and sequence information, or administrative access.ConclusionJUICE is an open source data management system that has been developed to aid users in organizing and analyzing the large amount of data generated in an EST Project workflow. JUICE has been used in one of the first functional genomics projects in Chile, entitled "Functional Genomics in nectarines: Platform to potentiate the competitiveness of Chile in fruit exportation". However, due to its ability to organize and visualize data from external pipelines, JUICE is a flexible data management system that should be useful for other EST/Genome projects. The JUICE data management system is released under the Open Source GNU Lesser General Public License (LGPL). JUICE may be downloaded from or .