DC-OCEAN 1.2

An algorithm to detect the ocean in-situ duplicate profiles (Song et al., 2024, FMS)

Duplicated_checking_IQuOD (DC_OCEAN)

Release v1.2

Author: Zhetao Tan (IAP/CAS), Xinyi Song (IAP/CAS), Lijing Cheng (IAP/CAS)

Contributors: International Quality-controlled Ocean Database (IQuOD) members

Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS)

1. Overview

This algorithm, namely DC_OCEAN, aims at detecting the ocean in-situ duplicate profiles by reducing the computational intensity in a cost-effective way.

It utilizes a 'DNA' method, which assigns a 'DNA' to each profile using primary metadata (e.g., latitude, longitude, instrument types) and secondary data (e.g., sum of depth, sum of temperature, standard deviation of temperature in the profile). This approach is inspired by the similarity between profile data characteristics and the structure of DNA in biology, where each profile represents a 'DNA' and different metadata information corresponds to distinct segments on this 'DNA.'

The core assumption of this algorithm is that if it's a duplicate pair, most of the metadata and observational data will be identical.

The duplicate checking algorithm is contributed to the IQuOD group.

The codes need to be run with Python 3.

A scientific paper introducing this algorithm could be found in Song et al., 2024.

2. Introduction of DC_OCEAN

DC_OCEAN is an open-source Python library designed for detecting duplicate profiles in ocean in-situ observations, such as temperature and salinity profiles. It was developed to identify duplicate ocean profiles, label them, and simultaneously reduce computational demands and human workload associated with manual quality control.

Why DC_OCEAN

• DC_OCEAN represents the first open-source software package for checking duplicate ocean observation profiles.

• The performance and robustness of DC_OCEAN have been meticulously analyzed and evaluated in a scientific peer-reviewed journal (refer to Song et al., 2024; FMS).

• As part of the contribution to the International Quality-controlled Ocean Database (IQuOD ) Task Team, specifically the Duplicate Checking Task Team, the DC_OCEAN has been adopted and recommended by IQuOD.

• DC_OCEAN utilizes 'DNA' algorithms, which employ mathematical, statistical methods like the entropy weight method and principal component analysis to establish a unique 'DNA' for each profile. This approach offers greater flexibility in comparisons and significantly reduces the time complexity of the screening process, all while ensuring screening accuracy.

2.1 Composition for DC_OECAN

The DC_OCEAN is composed of two main components:

• The first component involves the preprocessing of metadata by calculating their corresponding 'DNA' for each profile. These files are stored in the 'support' folder.

• The second component is the core program of DC_OCEAN, designed to determine whether potential duplicate pairs are real duplicates or not.

The first component includes a total of 2 scripts:

(1) ./support/N00_read_data_metadata.py

This script aims at reading the metadata and other information from the original netCDF files (we use the WOD18 single netCDF format) and then preprocessing the metadata.

(2) ./support/N01_possible_duplicates.py

This script aims at utilizing 14 distinct screening criteria to calculate the 'DNA' and identify possible duplicate pairs. The output is a possible duplicate pair list file (*.txt).

The second component consists of two files:

(1) M01_MAIN_check_nc_duplicate_manual.py

(2) M02_MAIN_check_nc_duplicate_list.py

In short, there are 4 steps to run the DC_OCEAN (see Table 1).

Table 1. The composition of DC_OCEAN

Order	Filename	Comments
1	support/N00_read_data_metadata.py	Preprocess the metadata
2	support/N01_possible_duplicates.py	Utilize fourteen distinct screening criteria to calculate the 'DNA' and identify potential duplicate pairs.
3	M01_MAIN_check_nc_duplicate_manual.py	Determine whether the potential duplicates from N01 are the real duplicates or not by manually checking.
4	M02_MAIN_check_nc_duplicate_list.py	The same as step 3, but in automatic check.

For more details and interpretation of the codes above, please refer to Song et al., 2023, Frontier in Marine Science.

3. Installation

3.1 Requirement packages

• Python 3 (>=3.7)
• numpy (= 1.19.1)
• timezonefinder (= 6.0.1)
• netCDF4 (= 1.5.5.1)
• pandas (= 1.0.3)
• scipy (=1.7.3)

Computer Memory: >8GB is obligatory.

MATLAB is needed to run the support codes.

3.2 Installing DC_OCEAN

Now the 'DC_OCEAN' package is uploaded to pypi (https://pypi.org/project/DC-OCEAN/1.2). For those of you interested, you can easily and freely access via 'pip' with the following steps:

Step1: Using pip to quickly install

If you don’t already have PIP running on your machine, first you need to install pip, then you can run:

pip install DC_OCEAN

Please make sure PIP fits your version of python3.X. In some machines, you should use pip3 to install DC_OCEAN because "pip" may be linked to python2.X

Then, you will wait for several seconds to install the package.

If you fail this step, you can manually install the package with the DC_OCEAN-1.2-py3-none-any.whlfile:

pip install DC_OCEAN-1.2-py3-none-any.whl

Step 2: Make a first and easiest QC test.

Here, we provide a demo. Now, you can make a first and most effortless test to check whether the DC_OCEAN package works well.

Launch your Python 3

Then go to the tests folder, and run the Example file: Example1_check_nc_duplicate_demo.py

cd <DC_OCEAN path>/tests
python Example1_check_nc_duplicate_demo.py

Then, the following information is output:

---------Please input two netCDF files which are potential duplicates--------
The first netCDF file name is: 

Then, input the following two NetCDF filename:

WOD18_19750602_00177_OSD.nc
WOD18_19750603_00105_OSD.nc

Output profile information or not(1: Yes; 0: No) : 1

If return the following result, congratulations!! The DC_OCEAN package works well.

              WOD_id:             8891325 ,              8891307
            Acess_no:                 416 ,                  416
             Dataset: bottle/rossette/net ,  bottle/rossette/net
                 Lat:             39.5000 ,              39.0167
                Long:            136.7667 ,             136.7333
                Year:                1975 ,                 1975
               Month:                   6 ,                    6
                 Day:                   3 ,                    2
                Hour:                   0 ,                   20
              Minute:                  30 ,                   54
           sum_depth:            935.0000 ,             935.0000
            sum_temp:             74.5400 ,              74.5400
        sum_salinity:            341.0000 ,             341.0000
          Probe type:                     ,                     
            Recorder:                     ,                     
        Depth_number:                  10 ,                   10
       Maximum Depth:             300.000 ,              300.000
             hasTemp:                   1 ,                    1
         hasSalinity:                   1 ,                    1
           hasOxygen:                   0 ,                    0
      hasChlonophyII:                   0 ,                    0
             Country:                  28 ,                   28
            GMT_time:               0.500 ,               20.920
     Database_origin:                     ,                     
        Project_name:   POD (1963 - 1996) ,    POD (1963 - 1996)
            Platform:       TATEYAMA-MARU ,        TATEYAMA-MARU
             Vehicle:                     ,                     
           Institute:TOYAMA PREFECTURAL FISHERIES EXPERIMENTAL STATION , TOYAMA PREFECTURAL FISHERIES EXPERIMENTAL STATION
WOD_cruise_identifier:            JP024640 ,             JP024640
      Wind_Direction:55 DEGREES - 64 DEGREES ,                     
          Wind_Speed:            999.0000 ,             999.0000
           Std_depth:             91.9796 ,              91.9796
            Std_temp:              5.4069 ,               5.4069
        Std_salinity:              0.0647 ,               0.0647
     Corr_temp&depth:             -0.8597 ,              -0.8597
      Corr_sal&depth:             -0.7677 ,              -0.7677


Spatial-temporal checks--Simultaneously but at different location: 0
Spatial-temporal checks--Simultaneously and co-located: 0
Correlation check: 0
Truncation check: 0
Layer by layer check - wrong location: 1
Layer by layer check - wrong date: 1
Layer by layer check - wrong time: 1
Layer by layer check - wrong country: 0
Layer by layer check - wrong instrument: 0
Exact duplicates check: 0
Interpolation (missing data) check: 0
CTD double data check: 0

    depth1     depth2 depth_diff      temp1      temp2  temp_diff   sal_diff
     0.000      0.000      0.000    17.1000    17.1000     0.0000     0.0000
    10.000     10.000      0.000    14.1300    14.1300     0.0000     0.0000
    20.000     20.000      0.000    12.6000    12.6000     0.0000     0.0000
    30.000     30.000      0.000     9.6700     9.6700     0.0000     0.0000
    50.000     50.000      0.000     6.9900     6.9900     0.0000     0.0000
    75.000     75.000      0.000     5.3100     5.3100     0.0000     0.0000
   100.000    100.000      0.000     4.2600     4.2600     0.0000     0.0000
   150.000    150.000      0.000     2.5000     2.5000     0.0000     0.0000
   200.000    200.000      0.000     1.3200     1.3200     0.0000     0.0000
   300.000    300.000      0.000     0.6600     0.6600     0.0000     0.0000
Duplicate result is: Near Duplicate

Now, you can get started with DC_OCEAN!

4. Getting Started with DC_OCEAN

Here, we will use some in-situ observational profiles in 1995 downloaded from the World Ocean Database (WOD18) to run the DC_OCEAN, aiming to detect the potential duplicate profiles within this dataset. These netCDF files are stored in <DC_ocean>/Input_files/WOD18_sample_1995

Here, we provided a Jupyter Notebook demo file (Demo_full_run.ipynb), you can use this file to fully run all scripts. But we recommend you to read the following instructions before you run the demo.

4.1 Run support files

cd <DC_ocean>/support
python N00_read_data_metadata.py

Then, the following information is output:

Please input the path that storge all netCDF files:

Here, we input the sample 1995 WOD18 netCDF files to test:

<DC_ocean>/Input_files/WOD18_sample_1995

Please replace the '<DC_ocean>' to the DC_OCEAN installed path.

Due to the upload limitation in the GitHub repository, the <DC_ocean>/Input_files/WOD18_sample_1995 only contains less than 200 netCDF files. To test as much as possible netCDF files, please download manually the compressed files here, and uncompress it to the <DC_ocean>/Input_files/WOD18_sample_1995 path

If return the following result, congratulations!! The first step works well.

Processing file 1/1883: wod_007274912O.nc
....
....
Processing file 1880/1883: wod_007275383O.nc
Processing file 1881/1883: wod_007274923O.nc
Processing file 1882/1883: wod_007274937O.nc
Processing file 1883/1883: wod_007275397O.nc
The DNA formatted file are output to current folder: ../Input_files

The DNA filename is: ../Input_files/DNA_summary.npz

In this script, we will preprocesses the profile data and metadata, employing ASCII to transform character (string) variables into numerical variables. This process generates the ../Input_files/DNA_summary.npz file. You'll find three variables in this npz file: DNA_series, filename_info, and variable_name.

Next, use the DNA_summary.npz file as input for the sequential execution of support/N01_possible_duplicates.py. These scripts apply various 'DNA' algorithms (with 14 criteria in total) to uncover as many potential duplicates as possible. This process generates the possible duplicate pairs list:

cd <DC_ocean>/support
python N01_possible_duplicates.py

Then, the following information is output:

Please enter the path to your DNA summary files (*.npz):

Please input the following path with the npz files output from N00_read_data_metadata.py:

<DC_ocean>/Input_files/DNA_summary.npz

If return the following result, congratulations!! The first step works well.

loading the DNA summary files....
Running the Crude Screen check: the No.1 criteria check...
Running the Crude Screen check: the No.2 criteria check...
Running the Crude Screen check: the No.3 criteria check...
Running the Crude Screen check: the No.4 criteria check...
Running the Crude Screen check: the No.5 criteria check...
Running the Crude Screen check: the No.6 criteria check...
Running the Crude Screen check: the No.7 criteria check...
Running the Crude Screen check: the No.8 criteria check...
Running the Crude Screen check: the No.9 criteria check...
Running the Crude Screen check: the No.10 criteria check...
Running the Crude Screen check: the No.11 criteria check...
Running the Crude Screen check: the No.12 criteria check...
Running the Crude Screen check: the No.13 criteria check...
Running the Crude Screen check: the No.14 criteria check...
The number of the possible duplicates pairs are:
('wod_007275858O.nc', 'wod_007276143O.nc')
...
...
('wod_007275523O.nc', 'wod_007275526O.nc')
('wod_007275894O.nc', 'wod_007276157O.nc')
The number of the possible duplicates pairs are: 258
The possible duplicates pair list is stored in: ../Input_files/sorted_unique_pairs_generic.txt
Then, please run the M01/M02 files to determine whether the potential duplicate pairs are exact/possible/no duplicates or not
SUCCESSFULLY run the crude screen check!!

Here, the possible duplicate pairs list is saved as ./Input_files/sorted_unique_pairs_generic.txt, which can be easily opened using Excel for further examination.

You can now go to the Section 4.2.

4.2 Run main files

This program aims to use the knowledge of physical oceanography and the expert experiences in the field tests to determine the authenticity of potential duplicates identified in Section 4.1. A total of 7 criteria are set.

• Simultaneously but at a different location

• Simultaneously and co-located

• Correlation check

• Truncation check

• Layer-by-layer check (wrong location, wrong date, wrong time, wrong country, wrong instrument)

• Exact duplicates check (i.e., depth-by-depth check)

• Interpolation (missing data) check

Two input and output methods are provided:

1. Manually inputting the file name of potential duplicate pairs using M01_MAIN_check_nc_duplicate_manual.py, which checks one pair at a time (refer to 4.2.1 for details).
2. Directly using the .txt file output in Section 4.1 as input for the main program (M02_MAIN_check_nc_duplicate_list.py) to automatically check all potential duplicate data pairs (see Section 4.2.2 for details).

4.2.1 Manual check: M01_MAIN_check_nc_duplicate_manual.py

Using M01_MAIN_check_nc_duplicate_manual.py enables a manual check, providing a side-by-side comparison of metadata information between potential duplicate and unduplicated profile data pairs. This facilitates a more precise determination of their duplicate status.

The M01_MAIN_check_nc_duplicate_manual.py is storage at the <DC_OCEAN> main folder.

"""
    Manually check whether the potential duplicates are 'true' duplicates based on some criterias
    This check is manually, one by one pair
    The automatic check is in the M02_MAIN_check_nc_duplicate_list.py
    input data: the potential pair
    output: whether it is real duplicated or not. (Screen output)
"""
import DC_OCEAN
import netCDF4 as nc
import numpy as np
import math
import os
from DC_OCEAN.util import country_table as t_country
from DC_OCEAN.util import compair_main as compair_main
import warnings
warnings.filterwarnings('ignore')

Class Duplicate_check(object):
    def __init__(self):
        pass

    def validate_file(self,input_path):
        # Normalize the path
        normalized_path = os.path.normpath(input_path)

        # Check if the fiile exists
        if not os.path.exists(normalized_path):
            return False

        return True
      
    def run(self):
        while True:
            print('---------Please input two netCDF files which are potential duplicates--------')
            file1=input('The first netCDF file name is: ').rstrip().lstrip()
            file2=input('The second netCDF file name is: ').rstrip().lstrip()
            isOutput_detail = input("Output profile information or not(1: Yes; 0: No)")
            ......
            ### Read the first netCDF file data
            content1=self.read_nc_data(filepath1)   # content1 is a dictionary
            ### Read the second netCDF file data
            content2=self.read_nc_data(filepath2)

            ### Output the information of two netCDF files
            self.output_info_pairs(content1,content2)

            ### Determine whether it is really repeated
            isDuplicated=compair_main.compair(content1,content2)

            if(isOutput_detail=='1'):
                self.output_detail(content1,content2)

            if(isDuplicated==1):
                print('Duplicate result is: Exact Duplicate')
            elif(isDuplicated==2):
                print('Duplicate result is: Near Duplicate')
            else:
                print('Duplicate result is: Not Duplicate')
                
	  def output_detail(self,content1,content2):
  	# Output data information and secondary processing information of two profiles
  	......
    def output_info_pairs(self,content1,content2):
  	# Output detail metadata information of two profiles
  	......
  	def read_nc_data(self,file):
  	# Read netCDF file
  	......
  	def find_id_country(self,country_name):
  	......
  	def add_parameters(self,params, **kwargs):
  	......
  	def find_order_dataset(self,dataset_name):
  	......
    
def main():
    dc=Duplicate_check()
    netCDF_filepath=input('Please input the path that storge all netCDF files:').lstrip().rstrip()
    if dc.validate_file(netCDF_filepath):
        dc.run(netCDF_filepath)
    else:
        print("The entered path of netCDF files is not valid. Please ensure the path is correct and try again.")


if __name__ == '__main__':
    main()

Please update the netCDF_filepath to suit your specific case. We've provided a demo using WOD18 data for all of 1995 in netCDF format. You can download the compressed file here and then extract it to your local directory.

Run the file M01_MAIN_check_nc_duplicate_manual.py and enter the file the path that storge all netCDF files:

Please input the path that storge all netCDF files: <DC_OCEAN>/Input_files/WOD18_sample_1995

Then, inout the name of the profile pair to be checked according to the prompts on the screen output:

---------Please input two netCDF files which are potential duplicates--------
The first netCDF file name is: wod_007275043O.nc
The second netCDF file name is: wod_007275048O.nc

Input 1 or 0 to determine whether to output metadata information according to your needs (yes is 1, no is 0); in this case, 1 is used.

Output profile information or not(1: Yes; 0: No)1

              WOD_id:             7275043 ,              7275048
            Acess_no:                 306 ,                  306
             Dataset:                 XBT ,                  XBT
                 Lat:             25.2833 ,              25.3833
                Long:            133.2500 ,             133.0000
                Year:                1995 ,                 1995
               Month:                   6 ,                    6
                 Day:                   4 ,                    4
                Hour:                  19 ,                   19
              Minute:                   0 ,                    0
           sum_depth:              0.0000 ,               0.0000
            sum_temp:             27.5000 ,              27.5000
        sum_salinity:              0.0000 ,               0.0000
          Probe type:   XBT: TYPE UNKNOWN ,    XBT: TYPE UNKNOWN
            Recorder:                     ,                     
        Depth_number:                   1 ,                    1
       Maximum Depth:               0.000 ,                0.000
             hasTemp:                   1 ,                    1
         hasSalinity:                   0 ,                    0
           hasOxygen:                   0 ,                    0
      hasChlonophyII:                   0 ,                    0
             Country:                  28 ,                   28
            GMT_time:              19.000 ,               19.000
       XBT depth fix:                     ,                     
     Database_origin:        GTSP Program ,         GTSP Program
        Project_name:                     ,                     
            Platform:HAKUREI MARU (R/V;call sign JBHT;built 03.1974;IMO7353999) , HAKUREI MARU (R/V;call sign JBHT;built 03.1974;IMO7353999)
             Vehicle:                     ,                     
           Institute:                     ,                     
WOD_cruise_identifier:            JP141577 ,             JP141577
      Wind_Direction:                     ,                     
          Wind_Speed:            999.0000 ,             999.0000
           Std_depth:              0.0000 ,               0.0000
            Std_temp:              0.0000 ,               0.0000
        Std_salinity:            999.0000 ,             999.0000
     Corr_temp&depth:            999.0000 ,             999.0000
      Corr_sal&depth:            999.0000 ,             999.0000


Spatial-temporal checks--Simultaneously but at different location: 0
Spatial-temporal checks--Simultaneously and co-located: 0
Correlation check: 0
Truncation check: 0
Layer by layer check - wrong location: 1
Layer by layer check - wrong date: 0
Layer by layer check - wrong time: 0
Layer by layer check - wrong country: 0
Layer by layer check - wrong instrument: 0
Exact duplicates check: 0
Interpolation (missing data) check: 0
CTD double data check: 0


    depth1     depth2 depth_diff      temp1      temp2  temp_diff   sal_diff
     0.000      0.000      0.000    27.5000    27.5000     0.0000        nan
Duplicate result is: Possible Duplicate

According to the running results, the two profile data are possible duplicate, with the duplication type attributed to interpolation (missing data).

4.2.2 M02_MAIN_check_nc_duplicate_list.py

The logical flow is consistent with Section 4.2.1, with the only difference being the modification of input and output formats.

It should be noted that the input of this code is sourced from the output in 4.1

#!/usr/bin/env python3
"""
    This program is used to determine whether the potential duplicate pairs quickly identified in the N02 step are actually duplicated, and if so, output
    input data: the txt file output from the ./support/N01_possible_duplicates.py
    output: two txt files: the duplicated list and the non-duplicated list. These two files can be opened by using Excel etc.
"""
import DC_OCEAN
import netCDF4 as nc
import numpy as np
import math
import os
from DC_OCEAN.util import country_table as t_country
from DC_OCEAN.util import compair_main as compair_main
import warnings
warnings.filterwarnings('ignore')
warnings

class Duplicate_check(object):
    def __init__(self):
        pass

    def read_potential_txt(self,txt_path):
        data=[]
        with open(txt_path,'r') as f:
            for line in f.readlines():
                ss=line.split()
                data.append(ss)

        return data
    def validate_file(self,input_path):
        # Normalize the path
        normalized_path = os.path.normpath(input_path)

        # Check if the file exists
        if not os.path.exists(normalized_path):
            return False

        return True
      
    def run(self,netCDF_filepath,potential_txt_path):

        ### Read potential_files_txt
        potential_files_list=self.read_potential_txt(potential_txt_path)

        potential_output_path='DuplicateList_'+potential_txt_path
        duplicate_number=0
        fid_duplicate_list=open(potential_output_path,'w+')
        print('filename1, filename2, unique_id_cast1, unique_id_cast2, same_moment_diff_loc_cruise, diff_records_in_same_Moment&Loc_cruise, scaled_records, rounded_truncate, wrong_location, wrong_date, wrong_moments, wrong_country, wrong_instru_types, identical_info, interpolated_pairs, CTD multiple observations, ',end='',file=fid_duplicate_list)
        print('Instrument_cast1, Instrument_cast2, Accession_cast1, Accession_cast2, lat_cast1, lat_cast2, lon_cast1, lon_cast2, year_cast1, year_cast2, month_cast1, month_cast2, day_cast1, day_cast2, hour_cast1, hour_cast2, minute_cast1, minute_cast2,',end='',file=fid_duplicate_list)
        print('probe_type_cast1, probe_type_cast2, recorder_cast1, recorder_cast2, depth_number_cast1, depth_number_cast2, maximum_depth_cast1, maximum_depth_cast2, country_cast1, country_cast2, GMT_time_cast1, GMT_time_cast2, dbase_orig_cast1, dbase_orig_cast2,',end='',file=fid_duplicate_list)
        print('project_cast1, project_cast2, Platform_cast1, Platform_cast2, ocean_vehicle_cast1, ocean_vehicle_cast2,WOD_cruise_identifier1,WOD_cruise_identifier2,Institute1,Institute2,need_z_fix1,need_z_fix2,sum_depth_cast1, sum_depth_cast2, sum_temp_cast1, sum_temp_cast2, sum_salinity_cast1, sum_salinity_cast2',file=fid_duplicate_list)

        ### Output a txt file containing nonduplicated profiles
        potential_output_unduplicate_path = 'Unduplicatelist_' + potential_txt_path
        fid_unduplicate_list = open(potential_output_unduplicate_path, 'w+')

        for i,potential_pairs in enumerate(potential_files_list):
            file1=potential_pairs[0].rstrip().lstrip()
            for i in range(1,len(potential_pairs)):
                file2=potential_pairs[i].rstrip().lstrip()
                # isOutput_detail = input("Output profile information or not(1: Yes; 0: No)")
                isOutput_detail='0'
                ......
                
                ### Read the first netCDF file data
                try:
                    content1=self.read_nc_data(filepath1)   # content1 is a dictionary
                    ### Read the second netCDF file data
                    content2=self.read_nc_data(filepath2)
                except:
                    print('Failed reading: '+file1+' and '+file2)
                    continue

                ### Compare the data
                isDuplicated,duplicate_multimodels=compair_main.compair(content1,content2)

                ### Output nonduplicated profile pair information
                if (isDuplicated == False):
                    self.output_UnduplicateList_txt(fid_unduplicate_list,content1,content2,file1,file2)

                elif(isDuplicated==1 or isDuplicated==2):
                    ### Output pair information

                    print(file1,file2)

                    duplicate_number=duplicate_number+1

                    ### Output metadata information and duplicate type of duplicate profile pairs
                    self.output_DuplicateList_txt(fid_duplicate_list,content1,content2,duplicate_multimodels,file1,file2)

                    if(isOutput_detail=='1'):
                        self.output_detail(content1,content2)

                    if(isDuplicated==1):
                        print(file1+' v.s. '+file2+': Exact Duplicate')
                    elif(isDuplicated==2):
                        print(file1+' v.s. '+file2+': Near Duplicate')
                    else:
                        print(file1+' v.s. '+file2+': No Duplicate')

                del isDuplicated

        print("duplicate_number: " + str(duplicate_number))  
        print("Two files output: "+potential_output_unduplicate_path +' and '+potential_output_path)
        print("Finished!")
        ......
        
def main():
    dc=Duplicate_check()
    potential_txt_path=input('Please input the path with filename (*.txt) of the potential duplicated list output from N01_possible_duplicates.py (e.g., sorted_unique_pairs_generic.txt):').lstrip().rstrip()
    if dc.validate_file(potential_txt_path):
        netCDF_filepath=input('Please input the corresponding netCDF files path:').lstrip().rstrip()
        if dc.validate_file(netCDF_filepath):
            dc.run(netCDF_filepath,potential_txt_path)
        else:
            print('The entered path of netCDF files is not valid. Please try again.')
    else:
        print("The entered path of potential duplicated list is not valid. Please ensure the path is correct and try again.")


if __name__ == '__main__':
    main()

Run the code and input the list *txt file output from Section 4.1 (sorted_unique_pairs_generic.txt) and the netCDF file path according to the prompt on the screen:

Please input the path with filename (*.txt) of the potential duplicated list output from N01_possible_duplicates.py (e.g., sorted_unique_pairs_generic.txt):<DC_ocean>/Input_files/sorted_unique_pairs_generic.txt
Please input the corresponding netCDF files path:<DC_OCEAN>/Input_files/WOD18_sample_1995

Subsequently, two text files are generated:

• duplicatelist_sorted_unique_pairs_generic.txt：Contains filenames of duplicate data and their corresponding metadata.

• Unduplicatelist_sorted_unique_pairs_generic.txt: Contains filenames of unduplicate data and their corresponding metadata.

Table 2 presents the variables saved in the *.txt files and their corresponding metadata.

Table 2. The output metadata list in M02_MAIN_check_nc_duplicate_list.py

Variable	Corresponding metadata fullname
filename	filename
unique_id	WOD unique id
Instrument_cast	dataset
Accession_cast	accession number
lat_cast	latitude
lon_cast	longitude
year_cast	year
month_cast	month
day_cast	day
hour_cast	hour
minute_cast	minute
probe_type_cast	probe type
recorder_cast	recorder
depth_number_cast	depth number
maximum_depth_cast	maximum depth
country_cast	country
GMT_time_cast	GMT time
dbase_orig_cast	dbase origin
project_cast	project
Platform_cast	platform
ocean_vehicle_cast	ocean vehicle
WOD_cruise_identifier_cast	WOD cruise identifier
Institute_cast	institute
need_z_fix_cast	need z_fix
sum_depth_cast	sum of depth records
sum_temp_cast	sum of temperature records
sum_salinity_cast	sum of salinity records

The above data files can be viewed and modified using Excel.

5. Notes for WOD18 netCDF format

In this algorithm, the input data and the data format is strictly followed WOD18 (World Ocean Database 2018) single netCDF file format. The format can be referenced [here]. The variables we used are shown in Table 3.

Therefore, if you need to use your custom format rather than using WOD18 format, please follow the Table 3 to customize your input netCDF files, otherwise the program will report many errors.

Table 3. The input WOD18 data format list for ./support/N00_read_data_metadata.py

Variable name	Comment	Dimension	Data type
Access_no	NODC accession number (used to find original data at NODC)	1	int
country	country	200	char
dataset	WOD dataset	200	char
lat	latitude	1	float
lon	longitude	1	float
Project	Project name	1	char
Temperature	Temperature	-	float
time	time	1	double
WOD_cruise_identifier	two byte country code + WOD cruise number (unique to country code)	200	char
wod_unique_cast	wod unique cast	1	int
z	depth below sea level	-	float
Salinity	Salinity	-	float
Oxygen	Oxygen	-	float
Chlorophyll	Chlorophyll	-	float
Temperature_Instrument	Device used for measurement	200	char
need_z_fix	instruction for fixing depths	200	char
Recorder	Device which recorded measurement	200	char
GMT_time	GMT time	1	float
WMO_ID	WMO identification code	1	int
dbase_orig	Database from which data were extracted	200	char
platform	Name of platform from which measurements were taken	200	char
Ocean_Vehicle	Ocean vehicle	200	char
Institute	name of institute which collected data	200	char

The above-mentioned variables must be included when calling the program. Before calling the program, the user needs to ensure that the data NetCDF file contains the above variables. If there is no relevant information, it should be set to a null value.

6. References

For more information about the DC_OCEAN, please refer to the documents or links below:

DC_OCEAN Github Project: https://github.com/IQuOD/duplicated_checking_IQuOD

IQuOD project and Task Team Duplicates: https://www.iquod.org/about.html

For more information about the DC_OCEAN (performance evaluation, scientific application), please refer to:

X. Song, Z. Tan, R. Locarnini, S. Simoncelli, R. Cowley, S.i Kizu, T. Boyer, F. Reseghetti, G. Castelao, V. Gouretski, L. Cheng, 2024: An open-source algorithm for identification of duplicates in ocean database. Frontier in Marine Science

7. License

DC_OCEAN is licensed under the Apache-2.0 License.

8. Citation

Please REMEMBER to cite this study if you use DC_OCEAN for any purposes:

[1] X. Song, Z. Tan, R. Locarnini, S. Simoncelli, R. Cowley, S.i Kizu, T. Boyer, F. Reseghetti, G. Castelao, V. Gouretski, L. Cheng, 2024: An open-source algorithm for identification of duplicates in ocean database. Frontier in Marine Science

[2] Tan, Z. and Song, X. (2024) IQuOD/duplicated_checking_IQuOD: DC_OCEAN (DC_OCEAN). Zenodo. https://doi.org/10.5281/zenodo.10689637

9. Acknowledgment

This study is supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant no. XDB42040402). We extend our thanks to all the IQuOD members who contributed to the manual checks of potential duplicates. We are grateful for the support of the International Oceanographic Data and Information Exchange (IODE) program. Special thanks to Edward King from CSIRO for providing valuable insights and reference materials on duplicate checking codes.

10. Questions and feedback

We warmly welcome feedback, questions, forks, pull requests, and improvements for the DC_OCEAN project within the IQuOD community!!

If you have any questions, suggestions, or come across any bugs in the program, or if you're interested in debugging or enhancing the DC_OCEAN project, please don't hesitate to get in touch:

• Create an issue in the GitHub community
• Pull requests your debugged/improved codes in the GitHub community.
• Send us an email at: [e-mail] or [e-mail]

11. Update logs

• January 15, 2023: updated the N04 program with adding minor revisions.
• February 3, 2023: expanded the N02 series of procedures. At present, the N02_1** to N02_6** programs are based on the normalization of data by row; the N02_7** to N02_12** programs are based on the normalization of data by column; the N02_13** and N02_14** program are based on the principal component analysis method.
• March 29, 2023: updated the N04 program with minor revision; Added only output duplicate data file name and accession number program to facilitate sensitivity check; Added a program to output non-duplicate data for manual inspection; Added procedures for checking sensitivity.
• August 22, 2023: Finalized the first version of the duplicate checking algorithm (v1.0)
• November 2023: Issued DC_OCEAN Python package (v1.0).
• March 2024: Issued DC_OCEAN Python package (v1.1) and linked the package to the Zenodo.
• May 2024: Issued DC_OCEAN Python package (v1.2)