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This project consists in the implementation of a series of Quality Checks (QC) on marine biodiversity records (implemented as Python dictionaries). The records are either stored in zipped DwCA archive files or in a MS SQL database.

Based on:


And other libraries, almost all from Iobis ( and authored by Pieter Provoost.


A modern PC, 8G of RAM, better if OS is Linux because all the development has been performed under Ubuntu 20.04 using Pycharm. Pycharm is not needed in order to run the examples provided with system, and all the examples except the biggest dataset currently available run fine on an modest Atom based PC (2009, 4G RAM).

QC System architecture

The diagram below can help figure out at a glance the main architectural components of the EUROBIS-QC system. image

The EUROBIS-QC system relies on:

  • Local Logic
  • SQLITE Database Lookups
  • External REST API Calls

The bulk of the QC calculations happen in the eurobisqc package. The QCs rely on local logic (for instance to verify presence and validity of latitude and longitude, min/max depth or validity of dates), or on calls to external REST APIs (for instance the pyxylookup API to verify that a point is at sea and which is the point's depth).

A number of QCs are implemented by mean of lookup into a locally available SQLITE database. The lookup tables can be easily modified by acting on a set of configuration files and quickly regenerated, including index creation.
This SQLITE database contains a copy of the WORMS database for the Taxonomy QCs, most noticeably to lookup the aphia ids and to establish the rank of the aphia ids.

All Database Logic is in the dbworks package. This includes the SQLITE for lookups and the MS SQL logic to access the eurobis datasets on which to calculate the QC values.

Furthermore, the verification of the record's Lat/Lon to ascertain that a point is at sea and that the reported depth is coherent with the depth map of the point is performed through calls to the pyxylookup service. These calls are performed for batches of 1000 (and leftovers) points for better network usage.

Another API which is called to obtain the geographical areas in which the dataset has been collected, is the IMIS database. The geographical retrieval of the area through IMIS is specific to the datasets and it has been implemented in the EurobisDataset ( For the DwCA files, the same information is extracted from the DwCA file's eml.xml, which is extracted and held by DWCAProcessor structures.

External API calls are protected as they run within a "timeoutable" thread, which returns None in case of failure. In those cases, the API calls are simply re-issued after the timeout period has expired.

Examples provided

The QCs works on Events/Occurrence records from DwCA files as well as on records stored in a MSSQL database. A set of small DwCA archives are provided under eurobisqc/test/data.

The examples provided, all found under /eurobisqc/examples, can be used to process:

  • a single DwCA file (QCs are not stored) (
  • a set of DwCA files contained in a directory using multiprocessing (
  • one or more datasets contained in the eurobis database ( - UPDATES the database
  • a random number of datasets (2% selected among those with less than 2500 records) from the database UPDATES the database
  • the calculation of a random record from a random data set, having less than 10000 records, printing all explanatory info: ( Also include the processing of n random records in sequence, without updating the repository.
  • The module that will allow you to process the entire database (

To process the entire database can be used to process the main DB with two parameters: with_multiprocessing and with_logging. It runs either by splitting the list of active datasets among a pool of processes OR by giving the entire list to a single process, which will act sequentially.

It is important that if using multiprocessing, only one of these pools will run. This is because normally the database locks is active at the row level and if two processes will attempt to lock the same database row, one of them will be denied and an exception shall be generated.

QC applied to records

QC is calculated on Event Records and Occurrence records, as follows: image

The class at the core of the system is the Enum QFlag, in eurobisqc.util.qc_flags. It contains all the defined QCs, and utilities to combine/encode/decode QC flags:

REQUIRED_FIELDS_PRESENT = ("All the required fields are present", 1) 
TAXONOMY_RANK_OK = ("Taxon level more detailed than Genus", 3)
GEO_LAT_LON_PRESENT = ("Lat and Lon present and not equal to None", 4)
GEO_LAT_LON_VALID = ("Lat or Lon present and valid (-90 to 90 and -180 to 180)", 5) 
GEO_LAT_LON_ON_SEA = ("Lat - Lon on sea / coastline", 6) 
DATE_TIME_OK = ("Year or Start Year or End Year complete and valid", 7) 
TAXON_APHIAID_NOT_EXISTING = ("Marine Taxon not existing in APHIA", 8)  # NOT IMPLEMENTED
GEO_COORD_AREA = ("Coordinates in one of the specified areas", 9) 
OBIS_DATAFORMAT_OK = ("Valid codes found in basisOfRecord", 10) 
VALID_DATE_1 = ("Valid sampling date", 11)  
VALID_DATE_2 = ("Start sampling date before End date - dates consistent", 12) 
VALID_DATE_3 = ("Sampling time valid / timezone completed", 13)  
OBSERVED_COUNT_PRESENT = ("Observed individual count found", 14) 
OBSERVED_WEIGTH_PRESENT = ("Observed weigth found", 15) 
SAMPLE_SIZE_PRESENT = ("Observed individual count > 0 sample size present", 16) 
SEX_PRESENT = ("Sex observation found", 17) 
MIN_MAX_DEPTH_VERIFIED = ("Depths consistent", 18) 
DEPTH_MAP_VERIFIED = ("Depth coherent with depth map", 19) 
DEPTH_FOR_SPECIES_OK = ("Depth coherent with species depth range", 20)  # NOT IMPLEMENTED

COORDINATES_PRECISION_PRESENT = ("Coordinates uncertainty <5000m", 21)  # In location
SAMPLE_DEVICE_PRESENT  = ("Sampling device present", 22)  # In measurements
ABUNDANCE_PRESENT = ("Abundance", 23)  # In measurements 
ABIOTIC_MAPPED_PRESENT = ("Abiotic data is mapped to BODC terms", 24)  # In measurements
GOODMETADATA = ("includes citation, title, license, and abstract with >100 characters", 25)  # On dataset level -

As a reference, this article can be used:

It has been agreed to not implement QCs 8 and 20 for the moment, so there is no QC procedure that deals with these two. Also Outliers analysis is not part of this implementation.

QC Procedures description:

All QCs are performed on a record (Python dictionary) or a set of, responding to the specifications provided in the three references below.

Such records can come directly from DwCA archives or from an object of class EurobisDataset, which has been built to mirror the functionality of DWCAProcessor but reading from the MSSQL Database EUROBIS instead of DwCA files. The EurobisDataset class contains a method called load_dataset(dataprovider_id) which loads all the records of a dataset in memory, provides to the fields "reconciliation" so that records from DWCAProcessor and EurobisDataset will look exactly the same to the QC procedures and builds also the necessary indexes, in one pass. The EUROBIS database contains a single table for both event type records and occurrence type records. It is important to notice that this is not a generator, all the dataset records are actually loaded in memory. Different strategies may be implemented, but this has not been part of the effort, as PCs with 8 G of RAM can deal with any of the datasets currently present in the database, and a PC with 4G or RAM has been successfully tested with a dataset of 1M records.

QC Procedures are located in the files:

- eurobisqc/    QC 1, 10
- eurobisqc/           QC 4, 5, 6, 9, 18, 19
- eurobisqc/            QC 7, 11, 12, 13
- eurobisqc/           QC 2, 3 
- eurobisqc/       QC 14, 15, 16, 17

These are used in a slightly different way in examples/ and examples/, because of the way in which the records are read.

There are two types of datasets, those with the data hyerarchy starting at "Event" records (DarwinCoreType = 2) and those starting at "Occurrence" records (DarwinCoreType = 1). Record QCs is calculated as follows :

From the image, for datasets with core record type = occurrence:

  1. The Occurrence records are checked for all the implemented QC and also for Sex (as a specific field Sex is present in the eurobis table which may be populated for an occurrence record).
    These are QCs from 1 to 19 except 8 (not implemented), 14, 15 and 16. Basically all QCs except those for eMoF records.

  2. The QC calculated on the set of eMoF records which are related to the occurrence record being examined. These QCs are related to the possible measurements: Sample Size, Count, Weight, Sex (QCs 14, 15, 16, 17). The calculated values are then OR-red to the occurence records' own QC. These values are not stored in the eMoF records.

From the image, for datasets with core record type = event:

  1. QCs performed on Event records are: Location (4, 5, 6, 9, 18, 19), Dates/Times (7, 11, 12, 13), Required fields (1).

  2. eMoF records for Event Records are NOT considered for QC calculations. They relate to instruments, conditions and are not related to biological observations.

  3. The Occurrence records are checked for all the implemented QC and also for Sex (as a specific field Sex may be populated for an occurrence record). These are QCs from 1 to 19 except 8 (not implemented), 14, 15 and 16. Basically all QCs except those for eMoF records.

  4. The QCs are calculated on the set of eMoF records which are related to the occurrence record being examined. These QCs are related to the possible measurements: Sample Size, Count, Weight, Sex (QCs 14, 15, 16, 17). The calculated values are then OR-red to the occurence records' own QC. These values are not stored in the eMoF records.

  5. The QC calculated for event records is normally pushed down to all Occurrence Records. This is because often the position, dates of the occurrences are all derived from the event, and they are not present on the occurrences.

  6. SPECIAL NOTE for the required fields check. Here, after having processed each occurrence record, occurrence record and "father" event records are looked at together to verify that all required fields are present in the (set) combination of the two records. A QC procedure located in eurobisqc/ is applied to the two records, if it passes, then QC 1 is assigned to the occurrence record.

Installation (sdist)

Ubuntu Linux 20.04

Starting from scratch - creating the basis

The basic requirements are to have Python3 installed (default) and as a minimum the modules pip and venv, which can anyway be installed following the procedure :

sudo apt install python3-pip python3-venv

Then git needs to be installed:

sudo apt install git 

Furthermore, you need to have odbc installed if you want to use the pyodbc driver for MS SQL or freetds for the pymssql driver. For installation of both options:

sudo apt install unixodbc-dev freetds-bin freetds-dev python3-dev

On ubuntu, you also need tk (there are some basic graphic elements in the demo programs).

sudo apt install python3-tk 

Clone the repository:

git clone  

Create the virtual environment

Create a directory where you want to install the project and make a virtual environment, this could (eventually) be inside the cloned repository. Then activate it :

python -m venv eurobis-qc-venv  
source ./eurobis-qc-venv/bin/activate 

Note: These are only one possibility, directory names and position/name of the virtual environment can be selected at leisure.

Customize the configuration

Edit the config.ini file contained in dbworks/resources/config.ini, by filling the following fields as in the example below:

driver        = ODBC Driver 17 for SQL Server
drivermodule  = pymssql
# drivermodule can be pymssql or pyodbc (needs to be specified).
server        =
server_local  = True
# server local will determine the number of processes spawned. MSSQL needs two cores to work OK
port          = 1433
database      = eurobis_dat
username      = <Your MS SQL User>
password      = <Your Password>

Please notice that server_local must be True if the database is running on the same machine, False otherwise. The configuration of the lookup DB does not change and a sample, which has been used during the development has been provided. Instructions to modify the lookup database can be found in the specific documentation.

Local Installation

Once all the configuration is performed, the project can be installed by running the setup file, in the eurobis-qc directory:

python install 


To verify that the installation is working, you can run the command:

python eurobisqc/examples/ 

from a terminal, on the command line. This will launch a dialog box, from which you can select the directory under test/data. The list of selected dwca archives shall be loaded and you can select one and click OK to process it :


These files are small but provide a good example of the QC processing involved.

Windows 10

Disclaimer: The entire project has been developed under Linux, and the only time it has been launched in Windows is to write these notes. The installation procedure as described in Linux fails, for several reasons. However, it is possible to create a virtual environment containing all the required packages and run the examples contained in the project from within Pycharm (for instance).

We start from a system connected to the internet, with the following installed and continue from there:

  • Python 3.6 or above
  • pip
  • git

The first things to do are to upgrade pip, then to install the virtual environment support. From a command line:

python -m pip install --upgrade pip
python -m pip install --upgrade virtualenv 

Then clone the project repository:

git clone

Create a virtual environment (better inside eurobis-qc if using Pycharm):

python -m venv eurobis-qc-venv

Install OS dependencies Download and install ODBC Drivers from MS Site:

Install also the pymssql alternative library:

python -m pip install --upgrade pymssql 

Open the project in Pycharm, in the terminal view activate the virtual environment if it is not yet activated:

.\eurobis-qc-venv\Scripts\activate.bat (use single backslashes)

install the requirements in requirements.txt.

pip install -r requirements.txt

On Windows, failures have been experienced while installing the github repositories. In that case, the github repositories shall be cloned and installed by hand on a terminal window, with the virtual environment activated, as explained later:

The command:

python install 

shall be run for each of them to install the libraries in the virtual environment. The repositories that can give troubles and can be installed from the cloned repository are as follows :

git clone
git clone
git clone
git clone     **NOTE: Should not be necessary** 


Configuration of the databases is the same as per Linux, in the same configuration file. As in Windows the default decompression settings for the libraries are not the same as per Linux, the Lookup database provided in double compressed form under


must be decompressed with a tool of choice until the file EUROBIS_QC_LOOKUP_DB.db is present in the same directory.

The main difference between the Linux install and the Windows install is that the Linux configuration files are in the virtual environments, where the packages are installed, while in Windows they are in the git clone directory


To verify that the installation is working, you can open the project from Pycharm and run the example file as described above.


Implementation of Quality Control checks on EUROBIS DwC-A files.






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