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Collaborative scientific platforms for accessing, processing and validation of biodiversity observation and biosensor data. Francisco Hernandez, Joram.

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Presentation on theme: "Collaborative scientific platforms for accessing, processing and validation of biodiversity observation and biosensor data. Francisco Hernandez, Joram."— Presentation transcript:

1 Collaborative scientific platforms for accessing, processing and validation of biodiversity observation and biosensor data. Francisco Hernandez, Joram Declerck, Klaas Deneudt, Cis Hernandez Lifewatch - EGI engage May 2015

2 Outline Lifewatch EGI - ENGAGE – Collaborative scientific platform Biosensors Challenges Status – Virtual labs and research environments Progress

3 Lifewatch infrastructure European research infrastructure, ESFRI roadmap Study of biodiversity and ecosystems Access to realtime data Biosensor networks BIG data, inline data processing More info : http://www.lifewatch.euhttp://www.lifewatch.eu Example VRE : http://marine.lifewatch.eu/http://marine.lifewatch.eu/ Belgian contribution: http://www.lifewatch.behttp://www.lifewatch.be

4 Biosensors Biological sensors that enable observations at space and time scales relevant to organisms behavior, physiology and life history – Optics, Acoustics, Genetics, GPS Discrete => continuous Delayed mode => real-time

5 http://www.lifewatch.be/en/sensors

6 Zooscan High resolution flatbed scanner for water samples Digital storage and processing of zooplankton samples -> taxonomic composition and abundance Image acquisition: 2400-4800 dpi Data generation: +/- 4 GB/sample; 432 Gb/year Analysis: pattern recognition software (Plankton Identifier [Tanagra], ZooImage [R], …)

7 Video Plankton Recorder (VPR) Real-time underwater digital camera system + strobe Rapid quantification of plankton taxonomic composition and abundance Image acquisition: 30 frames/second of 7.2 ml image volume Data generation: +/- 10GB/h (at 150kb/image), 1.5M files / h Analysis: pattern recognition software (Visual Plankton software [Matlab])

8 Flow cytometer Laser based instrument for particle detection and characterisation in fluids Counting and characterisation of phytoplankton particles -> taxonomic composition and abundance Image acquisition: particle scattering + fluorescence Data generation: +/- 200 MB/sample; 1Tb/year Analysis: clustering software (Easyclus [Matlab], FCM [R], …)

9 Acoustic fish telemetry Acoustic fish tag tracking Studying distribution, migration and habitat use Data generation: 25 MB/month Analysis: GIS mapping & visualization (CartoDB), behaviour analysis (Matlab, Python)

10 Multibeam echosounder Acoustic high resolution depth sounding sonar Bathymetry and sediment typology Data generation: sediment 10Gb; water column 100Gb/day Analysis: data cleaning and validation, chart creation, deriving sediment typology (CARIS, Fledermaus)

11 Sediment profiler imaging Digital Sediment Profiling Camera Vertical cross section of the sediment/water interface Image acquisition: 24.1 Mpixel images of 320 cm2 of sediment Data generation: 1Gb/image; 130 Gb/year Analysis: Image pattern recogition software, relating to benthic communities

12 Acoustic bat recorder Ultrasound detection and recording Sound acquisition: 500 kHz with 16 Bit amplitude resolution Data generation: 1 MB/per second of sound recording; 0.5 Gb/night Analysis: Call detection and recogition software

13 Bird tracking with GPS Tracking of large birds with GPS tags developed by UvA-BiTS Studying migration and habitat use GPS position every 10min, 70 birds, soon > 100 Data generation: 3G/year (Flemish LifeWatch), multiple GB/year (UvA-BiTS) Analysis: GIS mapping & visualization, behaviour analysis (Matlab,Python,R)

14 Challenges Biosensor – buy and install equipment: sensors, winches, antennas – technical calibration, raw signal processing, vendor softw. IT – real-time data transfer, storage, processing Biology – sensor output -> biodiversity parameters – aggregating, combine data, models & predictions, visualization, sharing, validation,…

15 Doctoral studies on sensor use and application -4 PhD’s at Ghent University -Started October 2014 -Work on : -Standard operation procedures -Algorithms for translation of sensor output to biodiversity parameters on abundance and distribution of taxa -Recommendations on optimization and upgrade of the infrastructure -Applications of the infrastructure in biodiversity and ecosystem studies

16 Collaborative platform for sensor data processing and analysis Flowcytometer Sensor network Fish tagsBird tracks Sensor data Archive ? R, Java, Python, Rstudio, Rshiny Scripts, workflows, algorithms Scientific data Rsync Calibration data Processing network Species identifications, field observations, crowd sourcing Science community VPR Bird Radar Benthic img Fish sonar Remote sensing In situ habitat obs. Bird video Hydrophones eDNA Postgis, Geoserver LW ICT core FedCloud ?

17 InfrastructureStatus Bird GPS tracking network + web cams 3 base stations installed and operational 70 birds with GPS Flow CytometerInstalled on RV Simon Stevin and operational Multi beamInstalled on RV Simon Stevin and operational Acoustic bat detectorsTest installation installed. Offshore installation in preparation ZooScanInstalled in MSO lab and operational Video Plankton RecorderTo be installed July 2014 Sediment Profile ImagingInstalled on RV Simon Stevin and operational Acoustic fish telemetry40 fishes tagged, 51 receivers installed, data for >2 years Environmental DNASeveral species of fish and jellyfish under investigation

18

19 mongoDB network

20 File management

21 Progress Ongoing testing with ICT Core facilities: IFCA, EGI ENGAGE Create virtual servers – on fedcloud : problem with OCCI – on IFCA / LW servers : on going / tickets – R, R studio server, Rshiny : at VLIZ – PostGIS, geoserver : at VLIZ Set up data transfer – from ship & stations to IFCA : daily Rsync operational – from VLIZ geoserver to IFCA – manage storage capacity Set up user interfaces for scientists – VLIZ users : started – external users – manage resources: user data, algorithms, scripts, models, …

22 Lifewatch virtual labs to ICT core Meeting in Amsterdam Collect and present existing virtual labs – Lifewatch Marine Virtual Research Environment (VRE) : done – VRE for terrestrial & other thems: started Upscale virtual lab components using LW ICT core – Environmental layers, Species occurence data – BIOVEL portal, Biodiversity catalogue – Taxonomic, Biostatical & Modelling webservices http://marine.lifewatch.eu/

23 More info Lifewatch website http://www.lifewatch.euhttp://www.lifewatch.eu Marine VRE : http://marine.lifewatch.eu/http://marine.lifewatch.eu/ Belgian contribution: http://www.lifewatch.behttp://www.lifewatch.be Flanders Marine Institute : http://www.vliz.behttp://www.vliz.be

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