biowes Scientific Data Management Solution Dr. Petr Cisar Institute of complex systems, FFPW, USB Czech Republic.

Transcription

1 biowes Scientific Data Management Solution Dr. Petr Cisar Institute of complex systems, FFPW, USB Czech Republic.

2 Reproducibility Reproducibility An independent researcher should be able to replicate the experiment, under the same conditions, and achieve the same results. Irreproducible research (DataOne survey 2015) Incomplete information about experimental conditions Missing standardization - terminology, procedures Missing infrastructure for experiment management How to improve Provide the tools for support of metadata (description of experiment) and experimental data management Support usage of already defined standardizations

3 Data management process Management of experimental work Measure get data under specific conditions Describe reproducibility Store secured storage Standardize avoid data misinterpretation Organize do not lose track of records Link remember what you did to get results Analyze evolution protocols Share simple cooperation Search find what are you looking for after one year

4 Standardization - terminology Standardization of terminology is critical in data and metadata sharing and reproducibility Terminology Data, metadata formats Methodology Standardization sources EOL ontology ( Open Biology Ontology ( Minimum Information for Biological and Biomedical Investigations Biomedical ontology BioSharing ( BioPortal ( Issues additional effort is needed to learn and use the terminology Source:

5 Survey Experimental data management survey - AquaExcel partners involved research institutes (EU-15, non EU-1), private companies - 2 Experimental protocol design good Standardization usually local laboratory standard Data storage personal computers, personal hard drives, centralized storage repository Data sharing s, ftp Lack of tools to support data management

6 Existing solutions ELIXIR ( SEEK ( Systems focused on the data storage and processing Support starts when data are collected LIMS systems (Laboratory information management system) Often focused on the laboratory resources management with limited experiment description E-notebooks electronic laboratory notebook software Free form of the experiment description biowes Experimentalist support from the experiment design until sharing

7 biowes Scientific data and metadata management platform Focus on the reproducibility Support from protocol design to sharing Local and central repository Local manage my data and metadata Central share my metadata with others Experiment design Experiment realization Data/metadata management Data analysis Data processing

8 biowes - overview Three project fundaments Access points manager Design/filling of protocols Data processing Web interface Remote access to protocols and data Visualization Sharing Mobile devices Field experiments realization Local storage management Queries Sharing Data access evolution Data processing evolution Access rights Central storage Metadata sharing from local repository Searches Link to data provider Central storage Local storage Local storage

9 biowes - scheme Experiment Meta data Measurement device Raw data Local computer manager Web interface Standardization ATOL, EOL Local repository Data processing User 1 Data

10 biowes - reproducibility How do we support reproducibility? Standardized protocol design using predefined components Standardization support Terminology description Procedures Data and metadata on one place and linked Backtracking of data processing/analysis steps.

11 biowes one place storage Electronic protocol Metadata reproducibility point of view Data about experimental data that contains all the information needed for reproducibility of experiment Experimental setting Measurement devices Procedures Experimental data (black box) This concept allow us to deal with any type of data Data + metadata are linked and stored on one place = complete information about experiment MG63 37 C

12 BioWes data representation Three types of data structures: template Empty protocol Defines what should be described in the protocol Filled protocol template Values and images Data files Binary structure Any data type Experimental data, reports, pdf, images, papers xml xml binary template Data Data Data

13 biowes workflow 1. The user install manager to some local computer (connected to the measuring device) 2. User create template of protocol for particular experiment (similar template can be found in the repository and modified) 1. The tool designer help pro design the protocol 2. The tool supports the standardized terminology 3. Template is stored to the local server 3. User realize the experiment 1. Fill the information about experiment into protocol using manager 2. Link the data to the protocol using manager 3. Everything is stored to the local server 4. User access the data and metadata on the server through web interface 1. Download data and metadata 2. Share data and metadata 3. Insert information about processing steps 4. Search metadata

14 design and measurement manager Creation of protocol template (graphical design) Reusability of templates Usage of template for real experiment Linking of protocol and experimental data Linking of additional data (reports, papers) Storage into repository Plugins Reading of information from measurement devices Data processing modules LC-MS Image descriptors Objects detection and tracking

15 biowes electronic protocol User create the protocol from 13 components Input components Link components Informative components Graphical method for protocol design Output Experimental protocol template template is saved into local database Advantages Unification of templates Standardization Usage of information from devices

16 biowes terminology System can upload existing standards in ontology (OWL) format The terminology is offered to the user during protocol template design No need to learn the terminology Used terminology is attached to the protocol

17 Communication with devices The system supports the plug-ins for communication with devices The data can be automatically filled into protocol Plug-ins can be created on demand camera Plug-in 1 Plug-in 1 Plug-in 1 Plug-in 1

18 Data processing modules Module: DLL with specific data interface Project description Data measurement Data processing Can read data from protocol - data + metadata Process data and store results template template template Executed by manager Data processing chain Create protocol about data processing Link original data and processed data See the processing chain in Web interface Reading of external information Data Data designer generator Data processing module Visualization module manager BioWes fat client database

19 Web interface Secured data storage (server, cloud) Web interface for access to protocols and templates management access to experimental data Sharing Different levels of access rights Read only (data X metadata) Read and write (data X metadata) Queries full text Sharing to central repository metadata and templates only Data and metadata visualization development during time Data processing steps chain

20 biowes evolution tracking Backtracking of experimental data processing steps Links between protocols of individual processing steps Visualization in the form of interactive graph evolution Evidence of protocol modifications

21 biowes mobile interface Web interface for mobile devices Optimized for mobile phones and small screens Field/on site experiments Limited functionality filling of protocols, pictures attachment

22 BioWes more information

23 Institute of complex systems Prof. Dalibor Štys Dr. Jan Urban Dr. Štepán Papáček Dmytro Soloviov Pavel Souček Antonín Bárta datapartner Zdeněk Marek Jan Maňas Vojtěch Osvald Milan Kaše Daniel Hlávka The study was financially supported by the Ministry of Education, Youth and Sports of the Czech Republic - projects 'CENAKVA' (No. CZ.1.05/2.1.00/ ), 'CENAKVA II' (No. LO1205 under the NPU I program), by South Bohemia University grant GAJU 134/2013/Z and by Technology Agency of the Czech Republic.