3. What needs to be checked?

4. Quality control procedures for OBIS data - background
Aim:
Help data providers & data managers
Checking quality
Checking completeness
Detect (possible) errors
Assign quality flags to each available record
Evaluation of fitness for purpose & use (NOT: good or bad)
Filter out records with certain quality standard
Example
Abra alba at latitude 24,53 & longitude 67,94 in 1983
Record suitable for general distribution analysis (species occurrence)
Record suitable for general temporal analysis (yearly trends)
Record not suitable for seasonal analysis
Record not suitable for abundance-related analyses (presence only)
Slide over waarom we qc doen.
Voorbeeld toont aan dat we een record niet als goed/slecht willen bestempelen, maar vooral willen aangeven waarvoor het wel of niet geschikt is.
=> Communication with provider can improve quality of the contributing data
=> users can take direct action based on the results

5. Quality control procedures for OBIS data - background
Aim:
Help data providers & data managers
Checking quality
Checking completeness
Detect (possible) errors
Assign quality flags to each available record
Evaluation of fitness for purpose & use (NOT: good or bad)
Filter out records with certain quality standard
Approach:
Automated process, within the database
Allows creation of filters
Allows feedback to data providers
Online web services
Freely available for use to everyone
Allows direct feedback to user (result reports)
=> Communication with provider can improve quality of the contributing data
users can take direct action based on the results
Will not go into detail on all the checks, will be the second presentation
Technical:
18 quality control steps, on individual record level
10 outlier checks, on dataset or species level
Each QC step = yes (1)/no (0) question
Creation of a bit-sequence (2(x-1))
=> stored as an integer value for the QC
=> unique value for each possible combination

6. Technical: 18 quality control steps, on individual record level
10 outlier checks, on dataset or species level
Each QC step = yes (1)/no (0) question
Creation of a bit-sequence (2(x-1))
=> stored as an integer value for the QC
=> unique value for each possible combination
QC step
Value
Bit-seq. 1
2(1-1)
= 1 2
2(2-1)
= 2 3
= 0 4
2(4-1)
= 8 5
TOTAL
= 11
QC step
Value
Bit-seq. 1
2(1-1)
= 1 2
2(2-1)
= 2 3
2(3-1)
= 4 4
2(4-1)
= 8 5
2(5-1)
= 16
TOTAL
= 31
If a record is evaluated positive for a certain QC, then two to the power of the unique number of this QC minus one is added to the field QC in the table ‘Eurobis’. If for example a record is evaluated positive for QC number 3 then the value 2(3-1) is added to the field QC. This way, a bit sequence is created and stored as an integer value in the field QC. This makes it possible to store the results of different QC’s in one (integer) field.

7. LifeWatch: home for a multitude of web services
Part of European Strategy Forum on Research Infrastructures (ESFRI)
Distributed virtual laboratory:
Biodiversity research
Climatological & environmental impact studies
Support development of ecosystem services
Provide information for policy makers
Biodiversity observatories, databases, web services and modelling tools
Integration of existing systems, upgrades, new systems
LifeWatch wants
Standardization of species data
Integration of distributed biodiversity data repositories & operating facilities
LifeWatch needs
Species information services
LifeWatch was established as part of the European Strategy Forum on Research Infrastructures (ESFRI). LifeWatch is a distributed virtual laboratory and will be used for biodiversity research, for climatological and environmental impact studies, to support the development of ecosystem services and to provide information for policy makers in Europe. This large European research infrastructure will consist of several biodiversity observatories, databases, web services and modeling tools. It will be integrating the existing systems, upgrading them were possible and developing new systems where needed.
Flemish contribution:
Lifewatch.eu will build a distributed infrastructure for the study and monitoring of biodiversity in europe
Pariticipation from many institutes in eu countries, and existing european networks (Marbef, LTER, PESI, GBIF, SPECIES2000,4D4live,….)
VLIZ and INBO have several existing systems to offer and are already participating as partner (lifewatch preparatory project) , or as network (all of the above)
The price is reasonable and the chances of success are high because it’s an upgrade of existing systems, and the expertise of both partners is proven.
But the project is also innovative: integration to very large scale, novel web technologies, development of new biosenors, and unprecedented: such infrastrucure is not yet available
The infrastructure will benefit many scientists because available online
Marine observatory; taxonomic backbone
Note: ‘central’ does not mean 1 server or 1 database but that it’s central in the workflow mechanisms. Similarly ‘integrating’ does not imply creating a new database.

8. LifeWatch offers compilation and combination of several web services
These services = taxonomic backbone
Taxonomy access services
Taxonomic editing environment
Species occurrence services
Catalogue services
LifeWatch infrastucture:
Identify, analyze and design online data services, models and applications
Make use of all LifeWatch data
= interactive part of LifeWatch

9. LifeWatch web services
Login / password required
System keeps track of all your “jobs”

11. Taxonomic quality control
Taxon match: World Register of Marine Species (WoRMS)
Taxon match: LifeWatch taxon match:
World Register of Marine Species
Integrated Taxonomic Information System (ITIS)
Catalogue of Life (CoL)
International Plant Name Index (IPNI)
Index Fungorum (IF)
PalaeoBiology Database (Palaeo-DB)
Pan-European Species Infrastructure (PESI)

12. Taxonomic QC step by step
TAXON NAME X
Match with WoRMS?
yes
Document LSID
Check habitat (marine/non-marine)
Check tax level (genus/species) no
Match with other registers?
yes no
Contact the data provider
for secondary check
Go through matches again
Is the taxon marine?
2004: MarBEF EU FP6 => European Register of Marine Species (ERMS)
2007: further development to World Register
Ook nog: index fungorum, Interim Register of Marine & Non-Marine Genera (IRMNG)
World Register of Marine Species
Not just a name index, but expert-based taxonomic database
> 200 taxonomic editors, Steering Committee & data management team
Permanently hosted at VLIZ
Web-based system, including web-services
International standards, permanent LSID’s
Taxon name available in WoRMS?
Is taxon equal or more detailed than genus/species?
Is taxon marine or not?
Other taxonomic databases
Catalogue of Life (CoL)
International Plant Names Index (IPNI)
Integrated Taxonomic Information System (ITIS) …
yes
Contact taxonomic editor: add taxon to WoRMS no
Add taxon to annotated list

13. WoRMS Taxon Match Tool Freely available, no password/login required
This tool uses the following components:
TAXAMATCH fuzzy matching algorithm by Tony Rees
PHP/MySql port of TAXAMATCH by Michael Giddens
Scientific Names Parser by Dmitry Mozzherin

14. Prepare your own file (Plain text [TXT], Comma Separated [CSV] & Excel Sheet [XLS, XLSX]
For convenience => colum “scientific_name”
Upload onto website

17. WoRMS taxon match results:
Exact match
Phonetic match
Near_1 match
Near_2 match
No match
Check and verify everything that is not an exact match…
Some examples:
Phonetic: Fragilaria aurivillii => Fragilaria aurivilii
Near_1: Chaetoceros seychellarum => Chaetoceros seychellarus
Near_2: Gammarus finnmarchius => Gammarus finmarchicus
Syllis armoricanus => Syllis armoricana

18. LifeWatch taxon match tool

19. If a taxon is not in WoRMS: Send email to info@marinespecies.org
Currently available taxon services
If a taxon is not in WoRMS:
Send to
Let us know if it is available in any of the other registers

21. Use this report as feedback to your provider / WoRMS

22. Taxonomic quality control – ambiguous matches
Tax. QC
Scientific name: Chondracanthus, unknown species
Kingdom Plantae (Rhodophyta)
Kingdom Animalia (Crustacea)
Scientific name: Alebion
Alebion Krøyer, 1863
=> Animalia, Crustacea, parasitic copepods
Alebion Gray, 1867
=> Animalia, Porifera
=> Accepted as Iophon Gray, 1867
Alebion Krøyer, 1863 Alebion Gray, 1867 accepted as Iophon Gray, 1867

23. Taxonomic quality control – its importance illustrated…
Tax. QC
“… In total, 6,172 unique taxon names were submitted …. After a thorough QC, however, this number was reduced to 4,525, mostly due to spelling variations and synonymy.”
“ … Such [taxonomic] quality control is highly needed, since a misspelled or obsolete name could be compared to the introduction of a rare species, with adverse effects on further (biodiversity) calculations…”
Source: Vandepitte et al. (2010). Data integration for European marine biodiversity research: creating a database on benthos and plankton to study large-scale patterns and long-term changes. Hydrobiologia 644: 1-13

24. ? Geographic quality control LifeWatch: Show on map
LifeWatch: Marine Regions Gazetteer services
Get lat-lon by MrgID
Get lat-lon by name
Get Gazetteer name by lat-lon
Get lat-lon by accepted name

25. Geographic QC – the concept
Communication with provider
Before quality control
After quality control
18°30’25’’N – 5°15’E
; 5.25
54,23N – 16.5S ;
WGS84 = World Geodetic System 1984; most used geographical reference system
Decimal degrees => easy to work with

26. Coordinates are indispensable
Coordinates = basis of a biogeographic information system
When no coordinates are provided…
Check with the data provider / the source
When existing: complete the file & run QC
When not existing:
Derive from provided map
Check Marine Regions to assign coordinates

27. Marine Regions = Standard, relational list of geographic names
Coupled with information and maps of the geographic location
Improve access and clarity of the different geographic, mainly marine names such as seas, sandbanks, ridges and bays

28. Fish species “A” present in Kenya
Marine species on land?
Link with adjacent sea area: EEZ
Indicate precision!!!!

31. The importance of geographical QC
Some examples
“Monitoring in Belgian part of the North Sea”
“Monitoring in Kongsfjorden area”
“+” & “-” signs switched
Latitude & longitude switched

32. Sightings and strandings of marine turtles around the coast of UK and Ireland
Left: coordinates as received; right: corrected. Errors due to missing minus sign

33. What else to check…?
Use common sense…

34. Dates OBIS data format check includes check on the date format: but…
Year: “1972” vs “72” vs “972”
Month: between 1-12
Day: between 1-31, check takes into account the given month
but…
Dataset from 1990, with a few records in 1909…

35. Units OBIS can capture: Are units defined? Counts Biomass Depth
Counts: individuals per m², cm², liter, m³
Biomass: wet weight, dry weight, ash-free dry weight
Depth: meter, centimeter
“I collected 4 individuals of species X from location Z”
=> Sample size? 10 cm² - 50 cm² - 1 m² - …?

36. Significance: Needs thorough documenting
Know what you are dealing with
Comparison
Convert to OBIS standards
Depth: in meter, positive values
Abundance: NULL versus 0 (absence); positive values

37. Quality control procedures for OBIS data - automated
Remember - technical:
18 quality control steps, on individual record level
10 outlier checks, on dataset or species level
Each QC step = yes (1)/no (0) question
Creation of a bit-sequence (2(x-1))
=> stored as an integer value for the QC
=> unique value for each possible combination
Some steps not (yet) available through web services
Will be identified once data is in OBIS
Harvest report will give indication of possibly erroneous records

38. Geographic quality control – 3-dimensional check
Latitude – longitude <> 0
Latitude – longitude between -90/+90 and -180/+180
Latitude – longitude within sea area (20 km buffer)
Depth value possible
Plot corresponding latitude-longitude on the General Bathymetric Chart of the Oceans (GEBCO)
Compare GEBCO depth with actual sampling depth
Take into account 100m margin
Deze kun je desnoods weglaten.
We doen ook een check op diepte: als de gegeven diepte te sterk afwijkt van de GEBCo diepte, wordt deze ‘geflagd’. Mensen moeten er ook logisch bij nadenken. Die vis is een bodem-vis, dus de kans dat je die inderdaad kort onder het oppervlak vind is eerder klein…
Taxon
Given depth (m)
GEBCO depth (m)
Difference (m)
Desmoscolex
2080
510
+ 1570
Halieutichthys aculeatus
110
1140
- 1030
Negative evaluation in QC
Needs to be looked at…
Pancake batfish => usually bottom-dwelling…

39. Quality control procedures for OBIS data - outlier analyses
Only performed on OBIS database (=global coverage)
Geographic outliers – dataset level
Analysis on dataset level
Possible location outlier(s)
Methodology based on centroid calculations and assuming normal distribution => not applicable for strong asymetric datasets…
Communication with provider on results
Centroid
No outlier
Possible outlier
Dataset: “ICES Biological Community” (DOME)
Also identified as incorrect in record-level check of lat-lon (=land)
Outliers, op dataset niveau.
Geografisch: we kijken welke coordinaten statistisch afwijken van de norm binnen de dataset. Kan moeilijk zijn, voor datasets die een globale spreiding hebben.
De outlier analyses kunnen best gecombineerd worden met andere qc stappen (cfr de rode cirkels). Dergelijke zaken worden gecommuniceerd met de provider, om tot de correcte oplossingen te komen.
Not identified through record-level check of lat-lon (=sea), but seen as potential outlier through geographic outlier check
Provider communication:
Antarctic locations are incorrect (data error)
Northern locations are correct (sampling bias)
Vandepitte et al. (2015). Fishing for data and sorting the catch […]. Database. DOI: /database/bau125

40. Verruca stroemia (Crustacea: Cirripedia)
Environmental outliers – species level
=> Check for outliers within the available distribution records of a species
=> Geography, depth, sea surface salinity (SSS), sea surface temperature (SST)
Geography
Depth
Centroid
No outlier
Possible outlier
No outlier
Possible outlier
Environmental outliers. Hier wordt zowel geografie, diepte, SSS en SST in rekening gebracht.
Deze kaart toont vooral dat verschillende analyses verschillende resultaten kunnen hebben, en dat de gebruiker zijn gezond verstand moet gebruiken bij het gebruik van deze data.
QC flags zijn een hulpmiddel voor de gebruiker om te beslissen welke data hij wel/niet gebruikt in zijn analyses.
De dubbelzinnige resultaten (oranje cirkels) werden voorgelegd aan expert. Middellandse zee data zijn fouten, noordelijke data zijn OK.
Dubious results => additional verification:
WoRMS, species distribution => not in Mediterranean, but yes in north
Expert => Mediteranean records are due to erroneous identifications in the field
=> depth range of species varies between intertidal and 548 metres
(available data only to 300 metres depth)
Verruca stroemia (Crustacea: Cirripedia)
Vandepitte et al. (2015)
Dubious results => additional verification:
World Register of Marine Species: literature & expert-based species distribution information
Expert: ecological information

41. Questions?
Analysing the content of the European Ocean Biogeographic Information System (EurOBIS): available data, limitations, prospects and a look at the future
Hydrobiologia 667(1): 1-14 (2011)
Vandepitte L., Hernandez F., Claus S., Vanhoorne B., De Hauwere N., Deneudt K., Appeltans W., Mees, J.
Finding what you need in a sea of data: Assessing the data quality, completeness and fitness for use of data in marine biogeographic databases
Database (2015), 1-14 (doi: /database/bau125)
Vandepitte L., Bosch S., Tyberghein L., Waumans F., Vanhoorne B., Hernandez F., De Clerck O. & Mees J.