Release v1.3.1
Author: Zhetao Tan (IAP/CAS), Xinyi Song (IAP/CAS), Lijing Cheng (IAP/CAS), Rebecca Cowley (CSIRO), Huifeng Yuan (CNIC/CAS), Guilherme Castelao (SIO), Simona Simoncelli (INGV), Shoichi Kizu (Tohoku University), Ricardo Locarnini (NOAA/NCEI), Tim Boyer (NOAA/NCEI), Franco Reseghetti (INGV), Viktor Gouretski (IAP/CAS)
International Quality-controlled Ocean Database (IQuOD)
Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS)
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 MATLAB and Python 3.
A scientific paper introducing this algorithm could be found in Song et al., 2024.
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.
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DC_OCEAN represents the first open-source software package for checking duplicate ocean observation profiles.
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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).
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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.
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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.
The DC_OCEAN is composed of two main components:
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The first component involves the preprocessing of metadata by calculating their corresponding 'DNA' for each profile. These files are stored in the 'support' folder.
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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) N00_Create_Profile_Summary_Score.py
This script aims at reading the metadata and secondary information from the original netCDF files (we use the WOD18 single netCDF format) and then preprocessing the metadata to create a Profile Summary Score (PSS) list.
(2) N01_Possible_Duplicate_Check.py
This script aims at utilizing 14 distinct screening criteria to calculate the Profile Summary Score (PSS) and identify possible duplicate pairs. The output is a possible duplicate pair list file (*.txt).
The second component consists of two files:
(1) Duplicate_Checker.py
(2) M00_Duplicate_Check_MAIN.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_Create_Profile_Summary_Score.py | Preprocess the metadata |
| 2 | support/N01_Possible_Duplicate_Check.py | Utilize fourteen distinct screening criteria to calculate the Profile Summary Score and identify potential duplicate pairs. |
| 3 | Duplicate_Checker.py | Determine whether the potential duplicates in the results of Possible_Duplicate_check.py are the real duplicates or not by manually checking and automatic check. |
| 4 | M00_Duplicate_Check_MAIN.py | The overall flow of duplicate check. It is the entry for the duplicate check program. |
For more details and interpreation of the codes above, please refer to Song et al., 2024, Frontier in Marine Science.
- Python 3 (>=3.8)
- numpy (= 1.19.1)
- timezonefinder (= 6.0.1)
- netCDF4 (= 1.5.5.1)
- pandas (= 1.0.3)
- scipy (=1.7.3)
- argparse (>=1.4.0)
Computer Memory: >8GB is obligatory.
Now the 'DC_OCEAN' package is uploaded to pypi (https://pypi.org/project/DC-OCEAN/1.3.1). 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_OCEANPlease 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-1.3-py3-none-any.whlStep 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
python3 Example1_check_nc_duplicate_demo.pyThen, 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.ncOutput profile information or not(1: Yes; 0: No) : 1If 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 DuplicateNow, you can get started with DC_OCEAN!
##4. Logical flow of DC_OCEAN
In this step, we will preprocesses the profile data and metadata, employing ASCII to transform character (string) variables into numerical variables. This process generates the ../Input_files/Profile_Summary_Score_list.npz file. You'll find three variables in this npz file: PSS_series, filename_info, and variable_name.
cd <DC_ocean>/support
python python N00_Create_Profile_Summary_Score.py -i <input_path> -o <output_path>The acceptable input parameters are as follows:
-i or --input: the path that storge all netCDF file. The default value is <DC_ocean>/Input_files/WOD18_sample_1995
-o or --output: the path that storge DNA_Summary.npz. The default value is <DC_ocean>/Input_files
Here, we try the following command:
python N00_Create_Profile_Summary_Score.py -i <DC_ocean>/Input_files/WOD18_sample_1995 -o <DC_ocean>/Input_filesPlease 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_1995path
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 Profile Summary Score formatted file are output to current folder: <DC_ocean>\Input_files
The Profile Summary Score filename is: <DC_ocean>\Input_files\Profile_Summary_Score_list.npz
Profile_Summary_Score_list.npz Complete !
If a file with the same name already exists in the output path, you will be prompted as shown below, select it as needed.
Update DNA Summary or not(1: Yes (default); 0: No):
Next, use the Profile_Summary_Score_list.npz file as input for the sequential execution of support/N01_Possible_Duplicate_Check.py. These scripts apply various strategies algorithms (with 14 strategies 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_Duplicate_Check.py -d <DC_ocean>/Input_files/Profile_Summary_Score_list.npzIf return the following result, congratulations!! The first step works well.
loading the Profile Summary Score list 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_007276168O.nc', 'wod_007276473O.nc')
...
...
('wod_007275019O.nc', 'wod_007275021O.nc')
('wod_007275041O.nc', 'wod_007276232O.nc')
The number of the possible duplicates pairs are: 258
*************FINISHED****************
The possible duplicates list is stored in: <DC_OCEAN>\Input_files\sorted_unique_pairs_generic.txt
Then, please run the MAIN files (M00_Duplicate_Check_MAIN.py) to determine whether the potential duplicate pairs are exact/possible/no duplicates or not
SUCCESSFULLY run the crude screen check!!
Here, the potential 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.
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.
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Simultaneously but at a different location
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Simultaneously and co-located
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Correlation check
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Truncation check
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Layer-by-layer check (wrong location, wrong date, wrong time, wrong country, wrong instrument)
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Exact duplicates check (i.e., depth-by-depth check)
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Interpolation (missing data) check
Two input and output methods are provided:
- Manually inputting the file name of potential duplicate pairs by calling
DuplicateCheckeManualinM00_Duplicate_Check_MAIN.py, which checks the potential duplicates one by one (refer to 4.2.1 for details). - Automatically checking all the potential duplicate list by calling
DuplicateCheckeManualinM00_Duplicate_Check_MAIN.pyby using the.txtfile output in Section 4.1 as input for the main program (see Section 4.2.2 for details).
Using DuplicateCheckeManual in Duplicate_Checker.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 manual check codes are storage in the <DC_OCEAN> main folder (Duplicate_Checker.py) at Line 79-131.
'''
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_Duplicate_Check.py
output: two txt files: the duplicated list and the non-duplicated list. These two files can be opened by using Excel etc.
'''
def duplicate_checke_manual(self, netCDF_filepath):
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)")
# index_str=file1.rfind('_')
# date1=file1[index_str-14:index_str-6]
# year1=date1[0:4]
# month1=date1[4:6]
# path1=os.path.join(netCDF_filepath,year1,month1)
# index_str=file2.rfind('_')
# date2=file2[index_str-14:index_str-6]
# year2=date2[0:4]
# month2=date2[4:6]
# path2=os.path.join(netCDF_filepath,year2,month2)
filepath1=os.path.join(netCDF_filepath,file1)
filepath2=os.path.join(netCDF_filepath,file2)
### 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,duplicate_multimodels=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: Possible Duplicate')
else:
print('Duplicate result is: Not Duplicate')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.
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
"""
This program is used to determine whether the potential duplicate pairs quickly identified in the N01 step are actually duplicated, and if so, output
input data: the txt file output from the ./support/N01_Possible_Duplicate_Check.py
output: two txt files: the duplicated list and the non-duplicated list. These two files can be opened by using Excel etc.
"""
def duplicate_checke_multiple(self,netCDF_filepath,potential_txt_path):
### Read potential_files_txt
potential_files_list=self.read_potential_txt(potential_txt_path)
# script_directory = os.path.dirname(potential_txt_path)
script_directory, _filename = os.path.split(potential_txt_path)
potential_output_path=os.path.join(script_directory,'DuplicateList_'+_filename)
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 = os.path.join(script_directory,'Unduplicatelist_' + _filename)
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'
# index_str=file1.rfind('_')
# date1=file1[index_str-14:index_str-6]
# year1=date1[0:4]
# month1=date1[4:6]
# day1=date1[6:8]
# path1=os.path.join(netCDF_filepath,year1,month1)
# index_str=file2.rfind('_')
# date2=file2[index_str-14:index_str-6]
# year2=date2[0:4]
# month2=date2[4:6]
# day2=date2[6:8]
# path2=os.path.join(netCDF_filepath,year2,month2)
filepath1=os.path.join(netCDF_filepath,file1)
filepath2=os.path.join(netCDF_filepath,file2)
#print(filepath1)
### 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+': Possible Duplicate')
else:
print(file1+' v.s. '+file2+': No Duplicate')
del isDuplicated
print('\n\n')
print('***************FINISHED********************')
print("duplicate_number: " + str(duplicate_number))
print('\n')
print("Two files output: "+potential_output_unduplicate_path +' and '+potential_output_path)
print("Finished!")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 non-duplicate data and their corresponding metadata.
Table 2 presents the variables saved in the *.txt files and their corresponding metadata.
| 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.
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
We here highly recommend you to read the following instructions before you run the demo:
Ensure all necessary dependencies are installed. You can run the MAIN files M00_Duplicate_Check_MAIN.py using the following command:
python M00_Duplicate_Check_MAIN.py [-i <input_directory>] [-o <output_directory>] [-d <PSS_directory>] [-m <mode>]Parameter Description:
-i, --input: Path to the input directory (default:Input_files/WOD18_sample_1995).-o, --output: Path to the output directory (default:Input_files).-d, --PSSfolder: Path to the Profile Summary Score directory (default:Input_files).-m, --mode: Execuation mode (default: 0)0: Automatically duplicate checking mode (List)1: Manual duplicate checking mode
Example #1: To run the code with the default parameters:
python M00_Duplicate_Check_MAIN.py
Update Profile Summary Score list or not(1: Yes (default); 0: No): 1
Then, it will automatically running all supporting files (N00_Create_Profile_Summary_Score.py and N01_Possible_Duplicate_Check.py). The output files are the duplicate and non-duplicate list (test files) saved in the default output directory.
Example #2: To specify custom mode parameters with default input and output path:
python M00_Duplicate_Check_MAIN.py -m 0or perform manual duplicate check :
python M00_Duplicate_Check_MAIN.py -m 1within this mode, you will be required to input the filenames of duplicate:
---------Please input two netCDF files which are potential duplicates--------
The first netCDF file name is:wod_007275538O.nc
The second netCDF file name is: wod_007275540O.nc
choose whether you want to output the comparasion information (1-yes; 0: no):
Output profile information or not(1: Yes; 0: No): 1
Then, it will return the result similar as Section 3.
Example #3: To specify custom all parameters:
python M00_Duplicate_Check_MAIN.py -i /path/to/input -o /path/to/output -m 0
In this example, the input directory and output directory are entered via argument passing.
In this algorithm, the input data and the data format is 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.
Here, we also provide a *.cdl file here for user to build an input file. Therefore, if you need to use your custom format rather than using WOD18 format, please follow the *cdl file 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.pyNote: If a specific data field does not have information (i.e., the data on your side doesn't contain the information/value of this specific data field), please set it to its default value.
| Variable name | Comment | Data type |
|---|---|---|
| Access_no | NODC accession number (used to find original data at NODC) | int |
| country | country | char |
| dataset | WOD dataset | char |
| lat | latitude | float |
| lon | longitude | float |
| Project | Project name | char |
| Temperature | Temperature | float |
| time | time | double |
| WOD_cruise_identifier | two byte country code + WOD cruise number (unique to country code) | char |
| wod_unique_cast | wod unique cast | int |
| z | depth below sea level | float |
| Salinity | Salinity | float |
| Oxygen | Oxygen | float |
| Chlorophyll | Chlorophyll | float |
| Temperature_Instrument | Device used for measurement | char |
| need_z_fix | instruction for fixing depths | char |
| Recorder | Device which recorded measurement | char |
| GMT_time | GMT time | float |
| WMO_ID | WMO identification code | int |
| dbase_orig | Database from which data were extracted | char |
| platform | Name of platform from which measurements were taken | char |
| Ocean_Vehicle | Ocean vehicle | char |
| Institute | name of institute which collected data | char |
The *.cdl file is attached here:
netcdf sample {
dimensions:
depth = unlimited; // Or specify an upper limit if known
variables:
float z(depth); // Depth below sea level in meters
z:units = "meters";
z:long_name = "depth_below_sea_level";
z:valid_range = 0.0f, 11000.0f;
z:positive = "down"
z:_FillValue = NaN;
float Temperature(depth); // Temperature in degrees Celsius
Temperature:units = "degrees_Celsius";
Temperature:long_name = "sea_water_temperature";
Temperature:valid_range = -2.0f, 50.0f;
Temperature:_FillValue = NaN;
float Salinity(depth); // Salinity in PSU (Practical Salinity Units)
Salinity:units = "PSU";
Salinity:long_name = "sea_water_salinity";
Salinity:valid_range = 0.0f, 45.0f;
Salinity:_FillValue = NaN;
float Oxygen(depth); // Oxygen concentration in mL/L
Oxygen:units = "Micromole per kilogram (µmol/kg)";
Oxygen:long_name = "Sea water dissolved oxygen";
Oxygen:valid_range = 0.0f, 1000.0f;
Oxygen:_FillValue = NaN;
float Chlorophyll(depth); // Chlorophyll concentration in mg/m^3
Chlorophyll:units = "Microgram per liter (µg l-1)";
Chlorophyll:long_name = "Chlorophyll";
Chlorophyll:valid_range = 0.0f, 50.0f;
Chlorophyll:_FillValue = NaN;
int wod_unique_cast; // WOD unique cast identifier
wod_unique_cast:long_name = "WOD Unique Cast Identifier";
wod_unique_cast:valid_range = unlimited;
wod_unique_cast:_FillValue = -9999;
float lat; // Latitude in decimal degrees
lat:units = "degrees_north";
lat:long_name = "Latitude";
lat:valid_range = -90.0f, 90.0f;
lat:_FillValue =NaN;
float lon; // Longitude in decimal degrees
lon:units = "degrees_east";
lon:long_name = "Longitude";
lon:valid_range = -180.0f, 180.0f;
lon:_FillValue = NaN;
double time; // Time of data collection in seconds since 1970-01-01 00:00:00 UTC
time:units = days since 1770-01-01 00:00:00 UTC";
time:long_name = "Time";
time:valid_range = unlimited;
time:_FillValue = NaN;
string country; // Country name where data was collected
country:long_name = "Country";
country:comment = "https://www.ncei.noaa.gov/access/world-ocean-database/CODES/country-list.html"
country:_FillValue = "";
string Temperature_Instrument; // Device used for temperature measurement
Temperature_Instrument:long_name = "Temperature Instrument";
Temperature_Instrument:comment = "https://www.ncei.noaa.gov/access/world-ocean-database/CODES/s_29_probe_type.html"
Temperature_Instrument:_FillValue = "";
string need_z_fix; // Instruction for fixing depths
need_z_fix:long_name = "Need Z Fix for XBT bias correction";
need_z_fix:comment = "https://www.ncei.noaa.gov/access/world-ocean-database/CODES/s_54_needs_depth_fix.html"
need_z_fix:_FillValue = "";
string Recorder; // Device which recorded measurement
Recorder:long_name = "Recorder";
Recorder:comment = "https://www.ncei.noaa.gov/access/world-ocean-database/CODES/s_32_recorder.html"
Recorder:_FillValue = "";
float GMT_time; // GMT time of data collection
GMT_time:units = "hours";
GMT_time:long_name = "GMT Time";
GMT_time:valid_range = 0.0f, 24.0f;
GMT_time:_FillValue = NaN;
int WMO_ID; // WMO identification code
WMO_ID:long_name = "WMO_identification_code";
WMO_ID:valid_range = 0, 999999;
WMO_ID:_FillValue = NaN;
string dbase_orig; // Database from which data were extracted
dbase_orig:long_name = "Database of Origin";
dbase_orig:_FillValue = "";
string Project; // Name of the project
Project:long_name = "Project";
Project:comment = "name or acronym of project under which data were measured"
Project:_FillValue = "";
string platform; // Name of platform from which measurements were taken
platform:long_name = "Platform";
platform:comment = "https://www.ncei.noaa.gov/access/world-ocean-database/CODES/s_3_platform.html"
platform:_FillValue = "";
string Ocean_Vehicle; // Ocean vehicle used for data collection
Ocean_Vehicle:long_name = "Ocean Vehicle";
Ocean_Vehicle:comment = "https://www.ncei.noaa.gov/access/world-ocean-database/CODES/s_74_ocean_vehicle.html"
Ocean_Vehicle:_FillValue = "";
int Access_no; // NODC accession number (used to find original data at NODC)
Access_no:long_name = "NODC_accession_number";
Access_no:comment = "https://www.ncei.noaa.gov/access/world-ocean-database/CODES/s_1_accession.html"
Access_no:_FillValue =NaN;
string Institute; // Name of the institute which collected data
Institute:long_name = "Institute";
Institute:_FillValue = "";
string WOD_cruise_identifier; // Two byte country code + WOD cruise number (unique to country code)
WOD_cruise_identifier:long_name = "WOD Cruise Identifier";
WOD_cruise_identifier:comment = "Two byte country code + WOD cruise number (unique to country code)"
WOD_cruise_identifier:_FillValue = "";
string dataset; // WOD dataset identifier
dataset:long_name = "WOD_dataset";
dataset:comment = "https://www.ncei.noaa.gov/access/world-ocean-database/CODES/wod-datasets.html"
dataset:_FillValue = "";
// Note: If a specific data field does not have information, please set it to its default value.
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, H. Yuan, G. Castelao, V. Gouretski, L. Cheng*, 2024: An open-source algorithm for identification of duplicates in ocean database. Frontier in Marine Science (under review)
DC_OCEAN is licensed under the Apache-2.0 License.
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, H. Yuan, G. Castelao, V. Gouretski, L. Cheng*, 2024: An open-source algorithm for identification of duplicates in ocean database. Frontier in Marine Science
[2] Zhetao Tan, Xinyi Song, Huifeng Yuan, & Rebecca Cowley. (2024). IQuOD/duplicated_checking_IQuOD: v1.3.1 (v1.3.1). Zenodo. https://doi.org/10.5281/zenodo.12697240
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.
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: tanzhetao19@mails.ucas.ac.cn or songxinyi231@mails.ucas.ac.cn
- January 15, 2023: updated the
N04program with adding minor revisions. - February 3, 2023: expanded the
N02series of procedures. At present, theN02_1**toN02_6**programs are based on the normalization of data by row; theN02_7**toN02_12**programs are based on the normalization of data by column; theN02_13**andN02_14**program are based on the principal component analysis method. - March 29, 2023: updated the
N04program 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)
- July 2024: Code encapsulation and framework enhancement, issue DC_OCEAN Python package (v1.3.1)