Dataset Information

Biologer: an open platform for collecting biodiversity data.

ABSTRACT: Background:We have developed a new platform named "Biologer" intended for recording species observations in the field (but also from literature resources and collections). The platform is created as user-friendly, open source, multilingual software that is compatible with Darwin Core standard and accompanied by a simple Android application. It is made from the user's perspective, allowing everyone to choose how they share the data. Project team members are delegated by involved organisations. The team is responsible for development of the platform, while local Biologer communities are engaged in data collection and verification. New information:Biologer has been online and available for use in Serbia since 2018 and was soon adopted in Croatia and Bosnia and Herzegovina. In total, we have assembled 536 users, who have collected 163,843 species observation records data from the field and digitalised 33,458 literature records. The number of active users and their records is growing daily. Out of the total number of gathered data, 89% has been made open access by the users, 10% is accessible on the scale of 10×10 km and only 1% is closed. In the future, we plan to provide a taxonomic data portal that could be used by local and national initiatives in Eastern Europe, aggregate all data into a single web location, create detailed data overview and enable fluent communication between users.

SUBMITTER: Popovic M

PROVIDER: S-EPMC7303222 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Biologer: an open platform for collecting biodiversity data.

Popović Miloš M Vasić Nikola N Koren Toni T Burić Ivona I Živanović Nenad N Kulijer Dejan D Golubović Ana A

Biodiversity data journal 20200611

<h4>Background</h4>We have developed a new platform named "Biologer" intended for recording species observations in the field (but also from literature resources and collections). The platform is created as user-friendly, open source, multilingual software that is compatible with Darwin Core standard and accompanied by a simple Android application. It is made from the user's perspective, allowing everyone to choose how they share the data. Project team members are delegated by involved organisat ...[more]

PMID: 32581636

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Project description:BackgroundWith a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field.ObjectiveWide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy.MethodsRADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided.ResultsGeneral principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts.ConclusionsRADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

Project description:Glycosylation is among the most abundant and diverse protein post-translational modifications (PTMs) identified to date. The structural analysis of this PTM is challenging because of the diverse monosaccharides which are not conserved among organisms, the branched nature of glycans, their isomeric structures, and heterogeneity in the glycan distribution at a given site. Glycoproteomics experiments have adopted the traditional high-throughput LC-MSn proteomics workflow to analyze site-specific glycosylation. However, comprehensive computational platforms for data analyses are scarce. To address this limitation, we present a comprehensive, open-source, modular software for glycoproteomics data analysis called GlycoPAT (GlycoProteomics Analysis Toolbox; freely available from www.VirtualGlycome.org/glycopat). The program includes three major advances: (1) "SmallGlyPep," a minimal linear representation of glycopeptides for MSn data analysis. This format allows facile serial fragmentation of both the peptide backbone and PTM at one or more locations. (2) A novel scoring scheme based on calculation of the "Ensemble Score (ES)," a measure that scores and rank-orders MS/MS spectrum for N- and O-linked glycopeptides using cross-correlation and probability based analyses. (3) A false discovery rate (FDR) calculation scheme where decoy glycopeptides are created by simultaneously scrambling the amino acid sequence and by introducing artificial monosaccharides by perturbing the original sugar mass. Parallel computing facilities and user-friendly GUIs (Graphical User Interfaces) are also provided. GlycoPAT is used to catalogue site-specific glycosylation on simple glycoproteins, standard protein mixtures and human plasma cryoprecipitate samples in three common MS/MS fragmentation modes: CID, HCD and ETD. It is also used to identify 960 unique glycopeptides in cell lysates from prostate cancer cells. The results show that the simultaneous consideration of peptide and glycan fragmentation is necessary for high quality MSn spectrum annotation in CID and HCD fragmentation modes. Additionally, they confirm the suitability of GlycoPAT to analyze shotgun glycoproteomics data.

Dataset Information

Biologer: an open platform for collecting biodiversity data.

Publications

Biologer: an open platform for collecting biodiversity data.

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