Overview

Teaching: 0 min
Exercises: 120 min

Questions

• How to convert dates to ISO?

• How to match scientific names to WoRMS?

• How to convert latitudes and longitudes to decimal degrees?

Objectives

• Aligning dates to the ISO 8601 standard.

• Matching scientific names to WoRMS.

• Converting latitude and longitude variations to decimal degrees North and East.

Now that you know what the mapping is between your raw data and the Darwin Core standard, it’s time to start cleaning up the data to align with the conventions described in the standard. The following activities are the three most common conversions a dataset will undergo to align to the Darwin Core standard:

1. Ensuring dates follow the ISO 8601 standard
2. Matching scientific names to an authoritative resource
3. Ensuring latitude and longitude values are in decimal degrees

Below is a short summary of each of those conversions as well as some example conversion scripts. The exercises are intended to give you a sense of the variability we’ve seen in datasets and how we went about converting them. While the examples use the pandas package for Python and the tidyverse collection of packages for R (in particular the lubridate package), those are not the only options for dealing with these conversions but simply the ones we use more frequently in our experiences.

Getting your dates in order

Dates can be surprisingly tricky because people record them in many different ways. For our purposes we must follow ISO 8601 which means using a four digit year, two digit month, and two digit day with dashes as separators (i.e. YYYY-MM-DD). You can also record time in ISO 8601 but make sure to include the time zone which can also get tricky if your data take place across time zones and throughout the year where daylight savings time may or may not be in effect (and start and end times of daylight savings vary across years). There are packages in R and Python that can help you with these vagaries. Finally, it is possible to record time intervals in ISO 8601 using a slash (e.g. 2022-01-02/2022-01-12). Examine the dates in your data to determine what format they are following and what amendments need to be made to ensure they are following ISO 8601. Below are some examples and solutions in Python and R for them.

ISO 8601 dates can represent moments in time at different resolutions, as well as time intervals, which use “/” as a separator. Date and time are separated by “T”. Timestamps can have a time zone indicator at the end. If not, then they are assumed to be local time. When a time is UTC, the letter “Z” is added at the end (e.g. 2009-02-20T08:40Z, which is the equivalent of 2009-02-20T08:40+00:00).

Tip

Focus on getting your package of choice to read the dates appropriately. While you can use regular expressions to replace and substitute strings to align with the ISO convention, it will typically save you time if you work in your package of choice to translate the dates.

Examples in Python

When dealing with dates using pandas in Python it is best to create a Series as your time column with the appropriate datatype. Then, when writing your file(s) using .to_csv() you can specify the format which your date will be written in using the date_format parameter.

The examples below show how to use the pandas.to_datetime() function to read various date formats. The process can be applied to entire columns (or Series) within a DataFrame.

1. 01/31/2021 17:00 GMT

   This date follows a typical date construct of month/day/year 24-hour:minute time-zone. The pandas .to_datetime() function will correctly interpret these dates without the format parameter.

   import pandas as pd
   df = pd.DataFrame({'date':['01/31/2021 17:00 GMT']})
   df['eventDate'] = pd.to_datetime(df['date'], format="%m/%d/%Y %H:%M %Z")
   df
   

                      date                 eventDate
      01/31/2021 17:00 GMT 2021-01-31 17:00:00+00:00
   

2. 31/01/2021 12:00 EST

   This date is similar to the first date but switches the month and day and identifies a different time-zone. The construct looks like day/month/year 24-hour:minute time-zone

   import pandas as pd
   df = pd.DataFrame({'date':['31/01/2021 12:00 EST']})
   df['eventDate'] = pd.to_datetime(df['date'], format="%d/%m/%Y %H:%M %Z")
   df
   

                      date                 eventDate
      31/01/2021 12:00 EST 2021-01-31 12:00:00-05:00
   

3. January, 01 2021 5:00 PM GMT

   import pandas as pd
   df = pd.DataFrame({'date':['January, 01 2021 5:00 PM GMT']})
   df['eventDate'] = pd.to_datetime(df['date'],format='%B, %d %Y %I:%M %p %Z')
   df
   

                              date                 eventDate
      January, 01 2021 5:00 PM GMT 2021-01-01 17:00:00+00:00
   

4. 1612112400 in seconds since 1970

   This uses the units of seconds since 1970 which is common when working with data in netCDF.

   import pandas as pd
   df = pd.DataFrame({'date':['1612112400']})
   df['eventDate'] = pd.to_datetime(df['date'], unit='s', origin='unix')
   df
   

            date           eventDate
      1612112400 2021-01-31 17:00:00
   

5. 44227.708333333333

   This is the numerical value for dates in Excel because Excel stores dates as sequential serial numbers so that they can be used in calculations. In some cases, when you export an Excel spreadsheet to CSV, the dates are preserved as a floating point number.

   import pandas as pd
   df = pd.DataFrame({'date':['44227.708333333333']})
   df['eventDate'] = pd.to_datetime(df['date'].astype(float), unit='D', origin='1899-12-30')
   df
   

                    date                     eventDate
      44227.708333333333 2021-01-31 17:00:00.000000256
   

6. Observations with a start date of 2021-01-30 and an end date of 2021-01-31.

   Here we store the date as a duration following the ISO 8601 convention. In some cases, it is easier to use a regular expression or simply paste strings together:

   import pandas as pd
   df = pd.DataFrame({'start_date':['2021-01-30'],
                      'end_date':['2021-01-31']})
   df['eventDate'] = df['start_date']+'/'+df['end_date']
   df
   

      start_time    end_time              eventDate
      2021-01-30  2021-01-31  2021-01-30/2021-01-31
   

Examples in R

When dealing with dates using R, there are a few base functions that are useful to wrangle your dates in the correct format. An R package that is useful is lubridate, which is part of the tidyverse. It is recommended to bookmark this lubridate cheatsheet.

The examples below show how to use the lubridate package and format your data to the ISO-8601 standard.

1. 01/31/2021 17:00 GMT

   library(lubridate)
   date_str <- '01/31/2021 17:00 GMT'
   date <- lubridate::mdy_hm(date_str,tz="UTC")
   date <- lubridate::format_ISO8601(date) # Separates date and time with a T.
   date <- paste0(date, "Z") # Add a Z because time is in UTC.
   date

   [1] "2021-01-31T17:00:00Z"

1. 31/01/2021 12:00 EST

   library(lubridate)
   date_str <- '31/01/2021 12:00 EST'
   date <- lubridate::dmy_hm(date_str,tz="EST")
   date <- lubridate::with_tz(date,tz="UTC")
   date <- lubridate::format_ISO8601(date)
   date <- paste0(date, "Z")
   date
   

   [1] "2021-01-31T17:00:00Z"
   

2. January, 01 2021 5:00 PM GMT

   library(lubridate)
   date_str <- 'January, 01 2021 5:00 PM GMT'
   date <- lubridate::mdy_hm(date_str, tz="GMT")
   lubridate::with_tz(date,tz="UTC")
   lubridate::format_ISO8601(date)
   date <- paste0(date, "Z")
   date
   

   [1] "2021-01-01T17:00:00Z"
   

3. 1612112400 in seconds since 1970

   This uses the units of seconds since 1970 which is common when working with data in netCDF.

   library(lubridate)
   date_str <- '1612112400'
   date_str <- as.numeric(date_str)
   date <- lubridate::as_datetime(date_str, origin = lubridate::origin, tz = "UTC")
   date <- lubridate::format_ISO8601(date)
   date <- paste0(date, "Z")
   date
   

   [1] "2021-01-31T17:00:00Z"
   

4. 44227.708333333333

   This is the numerical value for dates in Excel because Excel stores dates as sequential serial numbers so that they can be used in calculations. In some cases, when you export an Excel spreadsheet to CSV, the dates are preserved as a floating point number.

   library(openxlsx)
   library(lubridate)
   date_str <- 44227.708333333333
   date <- as.Date(date_str, origin = "1899-12-30") # If you're only interested in the YYYY-MM-DD
   fulldate <- openxlsx::convertToDateTime(date_str, tz = "UTC")
   fulldate <- lubridate::format_ISO8601(fulldate)
   fulldate <- paste0(fulldate, "Z")
   print(date)
   print(fulldate)
   

   [1] "2021-01-31"
   [1] "2021-01-31T17:00:00Z"
   

5. Observations with a start date of 2021-01-30 and an end date of 2021-01-31. For added complexity, consider adding in a 4-digit deployment and retrieval time.

   Here we store the date as a duration following the ISO 8601 convention. In some cases, it is easier to use a regular expression or simply paste strings together:

   library(lubridate)
   event_start <- '2021-01-30'
   event_finish <- '2021-01-31'
   deployment_time <- 1002
   retrieval_time <- 1102
   # Time is recorded numerically (1037 instead of 10:37), so need to change these columns:
   deployment_time <- substr(as.POSIXct(sprintf("%04.0f", deployment_time), format = "%H%M"), 12, 16)
   retrieval_time <- substr(as.POSIXct(sprintf("%04.0f", retrieval_time, format = "%H%M"), 12, 16)
   # If you're interested in just pasting the event dates together:
   eventDate <- paste(event_start, event_finish, sep = "/") 
   # If you're interested in including the deployment and retrieval times in the eventDate:
   eventDateTime_start <- lubridate::format_ISO8601(as.POSIXct(paste(event_start, deployment_time), tz = "UTC"))
   eventDateTime_start <- paste0(eventDateTime_start, "Z")
   eventDateTime_finish <- lubridate::format_ISO8601(as.POSIXct(paste(event_finish, retrieval_time), tz = "UTC"))
   eventDateTime_finish <- paste0(eventDateTime_finish, "Z")
   eventDateTime <- paste(eventDateTime_start, eventDateTime_finish, sep = "/") 
   print(eventDate)
   print(eventDateTime)
   

   [1] "2021-01-30/2021-01-31"
   [1] "2021-01-30T10:02:00Z/2021-01-31T11:02:00Z"
   

Tip

When all else fails, treat the dates as strings and use substitutions/regular expressions to manipulate the strings into ISO 8601.

Matching your scientific names to WoRMS

OBIS uses the World Register of Marine Species (WoRMS) as the taxonomic backbone for its system. GBIF uses the Catalog of Life. Since WoRMS contributes to the Catalog of Life and WoRMS is a requirement for OBIS we will teach you how to do your taxonomic lookups using WoRMS. The key Darwin Core terms that we need from WoRMS are scientificNameID also known as the WoRMS LSID which looks something like this "urn:lsid:marinespecies.org:taxname:105838" and kingdom but you can grab the other parts of the taxonomic hierarchy if you want as well as such as taxonRank.

There are two ways to grab the taxonomic information necessary. First, you can use the WoRMS Taxon Match Tool. The tool accepts lists of scientific names (each unique name as a separate row in a .txt, .csv, or .xlsx file) up to 1500 names and provides an interface for selecting the match you want for ambiguous matches. A brief walk-through using the service is included below. A more detailed step-by-step guide on using the WoRMS Taxon Match Tool for the MBON Pole to Pole can be found here.

The other way to get the taxonomic information you need is to use worrms (yes there are two r’s in the package name) or pyworms.

Using the WoRMS Taxon Match Tool

1. Create a CSV (comma separated value) file with the scientific name of the species of interest. Here we are showing some of the contents of the file species.csv.

2. Upload that file to the WoRMS Taxon match service
   • make sure the option LSID is checked
   • for the example file, make sure you select LineFeed as the row delimiter and Tab as the column delimiter

3. Identify which columns to match to which WoRMS term.

4. Click Match

5. Hopefully, a WoRMS exact match will return

   1. In some cases you will have ambiguous matches. Resolve these rows by using the pull down menu to select the appropriate match.
   2. Non-matched taxa will appear in red. You will have to go back to your source file and determine what the appropriate text should be.
      
6. Download the response as an XLS, XLSX, or text file and use the information when building the Darwin Core file(s). The response from the example linked above can be found here. A screenshot of the file can be seen below:

Using the worrms R package

1. Carcharodon carcharias (White shark)

   library(worrms)
   worms_record <- worrms::wm_records_taxamatch("Carcharodon carcharias", fuzzy = TRUE, marine_only = TRUE)[[1]]
   worms_record$lsid;  worms_record$rank; worms_record$kingdom
   

   [1] "urn:lsid:marinespecies.org:taxname:105838"
   [1] "Species"
   [1] "Animalia"
   

Using the pyworms python package

1. Bringing in species.csv and collecting appropriate information from WoRMS using the pyworms package.

   Note some of the responses have multiple matches, so the user needs to evaluate which match is appropriate.

   import pandas as pd
   import pyworms
   import pprint
      
   fname = 'https://ioos.github.io/bio_mobilization_workshop/data/species.csv'
      
   # Read in the csv data to data frame
   df = pd.read_csv(fname)
      
   # Iterate row by row through the data frame and query worms for each ScientificName term.
   for index, row in df.iterrows():
      resp = pyworms.aphiaRecordsByMatchNames(row['ORIGINAL_NAME'], marine_only=True)
         
      # When no matches are found, print the non-matching name and move on
      if len(resp[0]) == 0:
         print('\nNo match for name "{}"'.format(row['ORIGINAL_NAME']))
         continue
      
      # When more than 1 match is found, the user needs to take a look. But tell the user which one has multiple matches
      elif len(resp[0]) > 1:
         print('\nMultiple matches for name "{}":'.format(row['ORIGINAL_NAME']))
         pprint.pprint(resp[0], indent=4)
         continue
      
      # When only 1 match is found, put the appropriate information into the appropriate row and column
      else:
         worms = resp[0][0]
         df.loc[index, 'scientificNameID'] = worms['lsid']
         df.loc[index, 'taxonRank'] = worms['rank']
         df.loc[index, 'kingdom'] = worms['kingdom']
      
   # print the first 10 rows
   df.head()
   

   No match for scientific name "Zygophylax  doris (Faxon, 1893)"
     
   Multiple matches for scientific name "Acanthephyra brevirostris Smith, 1885"
      
                                    ORIGINAL_NAME                           scientificNameID taxonRank   kingdom
   0              Zygophylax  doris (Faxon, 1893)                                        NaN       NaN       NaN
   1       Bentheogennema intermedia (Bate, 1888)  urn:lsid:marinespecies.org:taxname:107086   Species  Animalia
   2  Bentheogennema stephenseni Burkenroad, 1940  urn:lsid:marinespecies.org:taxname:377419   Species  Animalia
   3       Bentheogennema pasithea (de Man, 1907)  urn:lsid:marinespecies.org:taxname:377418   Species  Animalia
   4        Acanthephyra brevirostris Smith, 1885                                        NaN       NaN       NaN
   

Getting lat/lon to decimal degrees

Latitude (decimalLatitude) and longitude (decimalLongitude) are the geographic coordinates (in decimal degrees north and east, respectively), using the spatial reference system given in geodeticDatum of the geographic center of a location.

• decimalLatitude, positive values are north of the Equator, negative values are south of it. All values lie between -90 and 90, inclusive.
• decimalLongitude, positive values are east of the Greenwich Meridian, negative values are west of it. All values lie between -180 and 180, inclusive.

Note, that the requirement for decimalLatitude and decimallLongitude is they must be in decimal degrees in WGS84. Since this is the requirement for Darwin Core, OBIS and GBIF will assume data shared using those Darwin Core terms are in the geodetic datum WGS84. We highly recommend checking the coordinate reference system (CRS) of your observations to confirm they are using the same datum and documenting it in the geodeticDatum Darwin Core term. If your coordinates are not using WGS84, they will need to be converted in order to share the data to OBIS and GBIF since decimalLatitude and decimalLongitude are required terms.

Helpful packages for managing CRS and geodetic datum:

• python: GeoPandas has a utility.
• R: terra and sf.

Tip

If at all possible, it’s best to extract out the components of the information you have in order to compile the appropriate field. For example, if you have the coordinates as one lone string 17° 51' 57.96" S 149° 39' 13.32" W, try to split it out into its component pieces: 17, 51, 57.96, S, 149, 39, 13.32, and W just be sure to track which values are latitude and which are longitude.

Image credit: xkcd

Examples in Python

1. 17° 51' 57.96" S 149° 39' 13.32" W
   • This example assumes you have already split the two strings into discrete components (as shown in the table). An example converting the full strings 17° 51' 57.96" S 149° 39' 13.32" W to decimal degrees can be found here.

   lat_degrees	lat_minutes	lat_seconds	lat_hemisphere	lon_degrees	lon_minutes	lon_seconds	lon_hemisphere
   17	51	57.96	S	149	39	13.32	W

   df = pd.DataFrame({'lat_degrees':[17],
                      'lat_minutes':[51],
                      'lat_seconds':[57.96],
                      'lat_hemisphere':['S'],
                      'lon_degrees': [149], 
                      'lon_minutes': [39], 
                      'lon_seconds':[13.32], 
                      'lon_hemisphere': ['W'],
                     })
      
   df['decimalLatitude'] = df['lat_degrees'] + ( (df['lat_minutes'] + (df['lat_seconds']/60) )/60)
   df['decimalLongitude'] = df['lon_degrees'] + ( (df['lon_minutes'] + (df['lon_seconds']/60) )/60)
   
   # Convert hemisphere S and W to negative values as units should be `degrees North` and `degrees East`
   df.loc[df['lat_hemisphere']=='S','decimalLatitude'] = df.loc[df['lat_hemisphere']=='S','decimalLatitude']*-1
   df.loc[df['lon_hemisphere']=='W','decimalLongitude'] = df.loc[df['lon_hemisphere']=='W','decimalLongitude']*-1
         
   df[['decimalLatitude','decimalLongitude']]
   

      decimalLatitude  decimalLongitude
             -17.8661         -149.6537
   

2. 33° 22.967' N 117° 35.321' W
   • Similar to above, this example assumes you have already split the two strings into discrete components (as shown in the table).

   lat_degrees	lat_dec_minutes	lat_hemisphere	lon_degrees	lon_dec_minutes	lon_hemisphere
   33	22.967	N	117	35.321	W

   df = pd.DataFrame({'lat_degrees':[33],
                      'lat_dec_minutes':[22.967],
                      'lat_hemisphere':['N'],
                      'lon_degrees': [117], 
                      'lon_dec_minutes': [35.321], 
                      'lon_hemisphere': ['W'],
                     })
      
   df['decimalLatitude'] = df['lat_degrees'] + (df['lat_dec_minutes']/60)
   df['decimalLongitude'] = df['lon_degrees'] + (df['lon_dec_minutes']/60)
      
   # Convert hemisphere S and W to negative values as units should be `degrees North` and `degrees East`
   df.loc[df['lat_hemisphere']=='S','decimalLatitude'] = df.loc[df['lat_hemisphere']=='S','decimalLatitude']*-1
   df.loc[df['lon_hemisphere']=='W','decimalLongitude'] = df.loc[df['lon_hemisphere']=='W','decimalLongitude']*-1
      
   df[['decimalLatitude','decimalLongitude']]
   

   decimalLatitude  decimalLongitude
   0        33.382783       -117.588683
   

Examples in R

1. 17° 51' 57.96" S 149° 39' 13.32" W

   lat_degrees	lat_minutes	lat_seconds	lat_hemisphere	lon_degrees	lon_minutes	lon_seconds	lon_hemisphere
   17	51	57.96	S	149	39	13.32	W

   library(tibble)
   tbl <- tibble(lat_degrees = 17,
                 lat_minutes = 51,
                 lat_seconds = 57.96,
                 lat_hemisphere = "S",
                 lon_degrees = 149,
                 lon_minutes = 39, 
                 lon_seconds = 13.32, 
                 lon_hemisphere = "W")
      
   tbl$decimalLatitude <- tbl$lat_degrees + ( (tbl$lat_minutes + (tbl$lat_seconds/60)) / 60 )
   tbl$decimalLongitude <- tbl$lon_degrees + ( (tbl$lon_minutes + (tbl$lon_seconds/60)) / 60 )
      
   tbl$decimalLatitude = as.numeric(as.character(tbl$decimalLatitude))*(-1)
   tbl$decimalLongitude = as.numeric(as.character(tbl$decimalLongitude))*(-1)
   

   > tbl$decimalLatitude
   [1] -17.8661
   > tbl$decimalLongitude
   [1] -149.6537
   

2. 33° 22.967' N 117° 35.321' W

   lat_degrees	lat_dec_minutes	lat_hemisphere	lon_degrees	lon_dec_minutes	lon_hemisphere
   33	22.967	N	117	35.321	W

   library(tibble)
   tbl <- tibble(lat_degrees = 33,
                 lat_dec_minutes = 22.967,
                 lat_hemisphere = "N",
                 lon_degrees = 117, 
                 lon_dec_minutes = 35.321, 
                 lon_hemisphere = "W")
      
   tbl$decimalLatitude <- tbl$lat_degrees + ( tbl$lat_dec_minutes/60 )
   tbl$decimalLongitude <- tbl$lon_degrees + ( tbl$lon_dec_minutes/60 )
      
   tbl$decimalLongitude = as.numeric(as.character(tbl$decimalLongitude))*(-1)
   

   > tbl$decimalLatitude
   [1] 33.38278
   > tbl$decimalLongitude
   [1] -117.5887
   

3. 33° 22.967' N 117° 35.321' W

   • Using the measurements package the conv_unit() can work with space separated strings for coordinates.

   lat	lat_hemisphere	lon	lon_hemisphere
   33 22.967	N	117 35.321	W

   tbl <- tibble(lat = "33 22.967",
                 lat_hemisphere = "N",
                 lon = "117 35.321", 
                 lon_hemisphere = "W")
  
  tbl$decimalLongitude = measurements::conv_unit(tbl$lon, from = 'deg_dec_min', to = 'dec_deg')
  tbl$decimalLongitude = as.numeric(as.character(tbl$decimalLongitude))*(-1)
  
  tbl$decimalLatitude = measurements::conv_unit(tbl$lat, from = 'deg_dec_min', to = 'dec_deg')

   > tbl$decimalLatitude
   [1] 33.38278
   > tbl$decimalLongitude
   [1] -117.5887

Key Points

• When doing conversions it’s best to break out your data into it’s component pieces.

• Dates are messy to deal with. Some packages have easy solutions, otherwise use regular expressions to align date strings to ISO 8601.

• WoRMS LSIDs are a requirement for OBIS.

• Latitude and longitudes are like dates, they can be messy to deal with. Take a similar approach.