Introduction to Darwin Core

Overview

Teaching: 0 min
Exercises: 90 min

Questions

• What is Darwin Core?

• What is a Darwin Core Archive?

• Why do people use Darwin Core for their data?

• What are the required Darwin Core terms for sharing to OBIS?

Objectives

• Understand the purpose of Darwin Core.

• Understand how to map data to Darwin Core.

• Plan for mapping to Darwin Core.

Darwin Core - A global community of data sharing and integration

Darwin Core is a data standard to mobilize and share biodiversity data. Over the years, the Darwin Core standard has expanded to enable exchange and sharing of diverse types of biological observations from citizen scientists, ecological monitoring, eDNA, animal telemetry, taxonomic treatments, and many others. Darwin Core is applicable to any observation of an organism (scientific name, OTU, or other methods of defining a species) at a particular place and time. In Darwin Core this is an occurrence. To learn more about the foundations of Darwin Core read Wieczorek et al. 2012.

Demonstrated Use of Darwin Core

The power of Darwin Core is most evident in the data aggregators that harvest data using that standard. The one we will refer to most frequently in this workshop is the Ocean Biodiversity Information System (learn more about OBIS). Another prominent one is the Global Biodiversity Information Facility (learn more about GBIF). It’s also used by the Atlas of Living Australia, iDigBio, among others.

Darwin Core Archives

Darwin Core Archives are what OBIS and GBIF harvest into their systems. Fortunately the software created and maintained by GBIF, the Integrated Publishing Toolkit, produces Darwin Core Archives for us. Darwin Core Archives are pretty simple. It’s a zipped folder containing the data (one or several files depending on how many extensions you use), an Ecological Metadata Language (EML) XML file, and a meta.xml file that describes what’s in the zipped folder.

Exercise

Challenge: Download this Darwin Core Archive and examine what’s in it. Did you find anything unusual or that you don’t understand what it is?

Solution

 dwca-tpwd_harc_texasaransasbay_bagseine-v2.3
 |-- eml.xml
 |-- event.txt
 |-- extendedmeasurementorfact.txt
 |-- meta.xml
 |-- occurrence.txt

Darwin Core Mapping

Now that we understand a bit more about why Darwin Core was created and how it is used today we can begin the work of mapping data to the standard. The key resource when mapping data to Darwin Core is the Darwin Core Quick Reference Guide. This document provides an easy-to-read reference of the currently recommended terms for the Darwin Core standard. There are a lot of terms there and you won’t use them all for every dataset (or even use them all on any dataset) but as you apply the standard to more datasets you’ll become more familiar with the terms.

Tip

If your raw column headers are Darwin Core terms verbatim then you can skip this step! Next time you plan data collection use the standard DwC term headers!

Exercise

Challenge: Find the matching Darwin Core term for these column headers.

1. SAMPLE_DATE (example data: 09-MAR-21 05.45.00.000000000 PM)
2. lat (example data: 32.6560)
3. depth_m (example data: 6 meters)
4. COMMON_NAME (example data: staghorn coral)
5. percent_cover (example data: 15)
6. COUNT (example data: 2 Females)

Solution

1. eventDate
2. decimalLatitude
3. minimumDepthInMeters and maximumDepthInMeters
4. vernacularName
5. organismQuantity and organismQuantityType
6. This one is tricky- it’s two terms combined and will need to be split. indvidualCount and sex

Tip

To make the mapping step easier on yourself, we recommend starting a mapping document/spreadsheet (or document it as a comment in your script). List out all of your column headers in one column and document the appropriate Dawin Core term(s) in a second column. For example:

my term	DwC term
lat	decimalLatitude
date	eventDate
species	scientificName

What are the required Darwin Core terms for publishing to OBIS?

When doing your mapping some required information may be missing. Below are the Darwin Core terms that are required to share your data to OBIS plus a few that are needed for GBIF.

What other terms should be considered?

While these terms are not required for publishing data to OBIS, they are extremely helpful for downstream users because without them the data are less useful for future analyses. For instance, depth is a crucial piece of information for marine observations, but it is not always included. For the most part the ones listed below are not going to be sitting there in the data, so you’ll have to determine what the values should be and add them in. Really try your hardest to include them if you can.

Other than these specific terms, work through the data that you have and try to crosswalk it to the Darwin Core terms that match best.

Exercise

Challenge: Create some crosswalk notes for your dataset.

Compare your data files to the table(s) above to devise a plan to crosswalk your data columns into the DwC terms.

Key Points

• Darwin Core isn’t difficult to apply, it just takes a little bit of time.

• Using Darwin Core allows datasets from across projects, organizations, and countries to be integrated together.

• Applying certain general principles to the data will make it easier to map to Darwin Core.

• Implementing Darwin Core makes data FAIR-er and means becoming part of a community of people working together to understand species no matter where they work or are based.

Social Break

Overview

Teaching: min
Exercises: min

Questions

Objectives

Image credit: xkcd

Key Points

Data Cleaning

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.

Darwin Core and Extension Schemas

Overview

Teaching: 0 min
Exercises: 90 min

Questions

• What is a core and what is an extension in Darwin Core?

• What is OBIS-ENV-DATA aka (Event Core with Extended Measurement or Fact)?

• How do I create Darwin Core files?

Objectives

• Creating IDs.

• Creating event file, occurrence file, eMOF file.

Darwin Core - Cores and Extensions

Now that we have a firm basis for understanding the different terms in Darwin Core the next part to understand is how data tables are organized and the difference between cores and extensions. You will always have a core table (Occurrence core or Event core) with either no extensions or several. What you choose depends on the data you have and how to represent it best. The original Darwin Core core is the Occurrence core. Once people started using that core they began to see that they needed extensions to that core to best represent the data they were trying to share and therefore several extensions have been developed (and are continuing to be developed). As more monitoring data has been shared over time, another core type called Event core was added. Without getting too far into the weeds on the cores and extensions, what’s most important to understand is that you need to pick your core type and once you do that then you pick the extensions to go with it. For example, if your data took place as part of an event (cruise, transects, etc) you will pick Event core. If there was no sampling event, then you will pick Occurrence core.

Different options for sharing the data

Occurrence only

The bare minimum for sharing data to OBIS is to use the Occurrence Core with no extensions. This core type covers datasets that only include observations and/or specimen records where no information on sampling is available. Occurrence core is also used for eDNA or DNA derived data.

The Occurrence core allows you to provide all the required Darwin Core terms detailed in the intro section. You can produce a fully compliant Darwin Core version of your data using only the Occurrence core (see this example by Tylar Murray). On one hand, if the data were collected using some kind of sampling methodology, you will lose much of that information if you use this most simple form of data structuring. However, it is faster and easier to produce.

Thought Experiment

Look at the minimum required fields example. What is possible to do in future reuse? What would not be possible? For instance, note that there is no information about depth or the uncertainty of the coordinates.

For more examples check out the Datasets folder in the IOOS Bio Data Guide.

Occurrence Core + extensions

Using the Occurrence core plus relevant extensions means that you can capture more of the data that’s been recorded. Let’s consider an environmental DNA dataset. eDNA datasets have information that is unique to that method and will not be represented well using Occurrence core only. To document eDNA using Darwin Core you should follow this guide; you will need the Occurrence core plus the DNA derived data extension. Adding the DNA derived data extension allows you to capture information such as the PCR primer used, DNA sequences, and other information specific to this type of data.

Let’s consider another example: a museum dataset that has biological measurements for each individual specimen (e.g. length). All information about each organism’s occurrence (taxonomic information, locality, identification, etc.) will go into the Occurrence core. You can then capture the biotic measurement information (type of measurement, units, accuracy, etc.) by using either the Measurement or Facts extension, or the Extended Measurement or Fact extension (we elaborate on this extension below). Note again here we do not have information on how the organisms were sampled.

Event Core with Extended Measurement or Fact extension

As we have indicated earlier, the Event core is for datasets that include known sampling events - details are known about how, when, and where samples were taken.

An innovation that OBIS made was introducing the Extended Measurement or Fact extension (also sometimes referred to as OBIS-ENV-DATA, or eMoF). This uses the Event core with an Occurrence extension + the extended Measurement or Fact extension. The eMoF extension makes it possible to include measurements for both the events (salinity, temperature, gear type, etc.) and the occurrences (weight, length, etc.). Prior to this you were only able to include measurements of the occurrence (in the Measurement or Facts extension).

It is important to know that the structure of the eMoF table is likely quite a bit different than how the original data are recorded in your dataset. Rather than documenting each measurement in separate columns, measurements will be condensed into one column: measurementValue (e.g. 15). Then, the column measurementType allows free text describing what the measurement actually is (e.g. length). Finally the column measurementUnit is used to indicate the unit of the measurement (e.g. cm).

The unconstrained nature of measurementType allows for flexibility in describing measurements, which can be quite a useful. But what if you wanted to obtain all OBIS records that have “length” measurements? Due to the inevitable heterogeneity in how different people document “length”, you would have to try to account for all these different ways! Fortunately the eMoF table can get around this challenge by providing a way to include Unique Resource Identifiers (URIs). These URIs are used to populate the measurementTypeID field, as well as measurementUnitID and measurementValueID. URIs mean that if you call the measurementType “abundance” but I call it “Abundance per square meter” and we both use the measurementTypeID “http://vocab.nerc.ac.uk/collection/P01/current/SDBIOL02/” then we know this is the same measurement type even if we didn’t use the same words to describe it. Choosing the right URI can be difficult but you can read about finding codes in the OBIS Manual. All you need to know for now is that measurementTypeID should be populated with a URI belonging to the NERC P01 collection, easily accessible through the SeaDataNet P01 facet search, and measurementUnitID should be popualted with a NERC P06 code. OBIS is soon releasing video tutorials to help with choosing URIs, so stay tuned to their manual and YouTube list for updates.

Tip

You can search for measurementTypes that other OBIS data providers have used by using the OBIS mof report. BE CAREFUL though to make sure the definition in the URI matches exactly your measurement type if you want to reuse it for your data.

What’s in an ID?

IDs are the keys in your data that are used to link tables together. For example, an occurenceID in the eMoF table records information about an organism with the same occurrenceID within the Occurrence core table. IDs are also the keys that keep track of each of the records, so that if you notice a mistake or missing information you can keep the record in place in the global aggregators and fix the mistake or add the missing information. For instance, let’s say you have a record with an occurrenceID Station_95_Date_09JAN1997:14:35:00.000_Atractosteus_spatula and after it’s published to OBIS you notice that the latitude was recorded incorrectly. When you fix that record in the data you would keep the occurrenceID Station_95_Date_09JAN1997:14:35:00.000_Atractosteus_spatula, fix the latitude, and republish the data so that the record is still present in OBIS but you have fixed the mistake.

With that in mind what is the best way to create an eventID, occurrenceID, or measurementID? Until we have a system that mints Persistent Identififers for individual records then the best way we have seen is to build the ID from information in the data itself. That way if you need to update or fix a record you simply use the same information again to build the same ID for the same record. Take our example above Station_95_Date_09JAN1997:14:35:00.000_Atractosteus_spatula. This is a concatenation of information from the original source data of the Station number + Verbatim Date + Scientific name. Because this is unique for each row in the occurrence file and we have kept the original data in its original format we can always rebuild this ID by concatenating this same information together again.

It is very important that these IDs do not change over time. So if an ID for a museum specimen is built from e.g. the institution the specimen is being held at, but then the specimen changes institutions - its ID should not change to reflect the move. If the ID changes then the record will be duplicated in the global database and record information could be lost over time!

The Darwin Core file structure, demonstrating how an Event core table can be connected to extension tables through the use of identifiers. Image credit: OBIS

Event Core

Darwin Core Term	Definition	Comment	Example
eventID	An identifier for the set of information associated with an Event (something that occurs at a place and time). May be a global unique identifier or an identifier specific to the data set.	To construct a globally unique identifier for each event (tow, quadrat, station, transect, etc) you can usually concatenate station + date but you’ll need to check this is unique for each row in your data.	INBO:VIS:Ev:00009375 Station_95_Date_09JAN1997:14:35:00.000 FFS-216:2007-09-21:A:replicateID1024
eventDate	The date-time or interval during which an Event occurred. For occurrences, this is the date-time when the event was recorded. Not suitable for a time in a geological context.	Must follow ISO 8601. See more information on dates in the Data Cleaning section of the workshop.	2009-02-20T08:40Z
decimalLatitude	The geographic latitude (in decimal degrees, using the spatial reference system given in geodeticDatum) of the geographic center of a Location. Positive values are north of the Equator, negative values are south of it. Legal values lie between -90 and 90, inclusive.	For OBIS and GBIF the required geodeticDatum is WGS84. Uncertainty around the geographic center of a Location (e.g. when sampling event was a transect) can be recorded in coordinateUncertaintyInMeters. See more information on coordinates in the Data Cleaning section of the workshop.	-41.0983423
decimalLongitude	The geographic longitude (in decimal degrees, using the spatial reference system given in geodeticDatum) of the geographic center of a Location. Positive values are east of the Greenwich Meridian, negative values are west of it. Legal values lie between -180 and 180, inclusive	For OBIS and GBIF the required geodeticDatum is WGS84. See more information on coordinates in the Data Cleaning section of the workshop.	-121.1761111
countryCode	The standard code for the country in which the location occurs.	Use an ISO 3166-1-alpha-2 country code. Not required for OBIS but GBIF needs this for their system.	US
geodeticDatum	The ellipsoid, geodetic datum, or spatial reference system (SRS) upon which the geographic coordinates given in decimalLatitude and decimalLongitude as based.	Must be WGS84 for data shared to OBIS and GBIF but it’s best to state explicitly that it is.	WGS84

Occurrence extension

Darwin Core Term	Definition	Comment	Example
eventID	An identifier for the set of information associated with an Event (something that occurs at a place and time). May be a global unique identifier or an identifier specific to the data set.	This will be constructed in the event core but you need to reference it here in the occurrence extension so the files can link to each other correctly.	INBO:VIS:Ev:00009375 Station_95_Date_09JAN1997:14:35:00.000 FFS-216:2007-09-21:A:replicateID1024
occurrenceID	An identifier for the Occurrence (as opposed to a particular digital record of the occurrence). In the absence of a persistent global unique identifier, construct one from a combination of identifiers in the record that will most closely make the occurrenceID globally unique.	To construct a globally unique identifier for each occurrence you can usually concatenate station + date + scientific name (or something similar) but you’ll need to check this is unique for each row in your data.	Station_95_Date_09JAN1997:14:35:00.000_Atractosteus_spatula
basisOfRecord	The specific nature of the data record.	Pick from these controlled vocabulary terms: HumanObservation, MachineObservation, PreservedSpecimen, LivingSpecimen, FossilSpecimen, MaterialSample, Occurrence, MaterialCitation	HumanObservation
scientificName	The full scientific name, with authorship and date information if known. When forming part of an Identification, this should be the name in lowest level taxonomic rank that can be determined. This term should not contain identification qualifications, which should instead be supplied in the identificationQualifier term.	Note that cf., aff., etc. need to be parsed out to the identificationQualifier term.	Atractosteus spatula
scientificNameID	An identifier for the nomenclatural (not taxonomic) details of a scientific name.	Must be a WoRMS LSID for sharing to OBIS. Note that the numbers at the end are the AphiaID from WoRMS.	urn:lsid:marinespecies.org:taxname:218214
kingdom	The full scientific name of the kingdom in which the taxon is classified.	Not required for OBIS but GBIF needs this to disambiguate scientific names that are the same but in different kingdoms.	Animalia
occurrenceStatus	A statement about the presence or absence of a Taxon at a Location.	For OBIS, only valid values are present and absent.	present

Extended Measurement or Fact Extension

Nothing is required from the OBIS perspective but if you are using this extension then eventID is required to be able to link the extension back to the Event core. You can also use occurrenceID to link to occurrence records in the Occurrence core or extension. See here for all potential fields in the extension and what goes in them. See below of the most relevant terms to be included in the eMoF table.

Darwin Core Term	Definition	Comment	Example
eventID	An identifier for the set of information associated with an Event (something that occurs at a place and time). May be a global unique identifier or an identifier specific to the data set.	This will be constructed in the Event core but you need to reference it here in the extension so the files can link to each other correctly.	INBO:VIS:Ev:00009375 Station_95_Date_09JAN1997:14:35:00.000 FFS-216:2007-09-21:A:replicateID1024
occurrenceID	The identifier of the occurrence the measurement or fact refers to. If not applicable, it should be left empty.	Only needed if you have measurements that you need to link back to an occurrence like weight, length, abundance, etc.	urn:catalog:UWBM:Bird:89776 Station_95_Date_09JAN1997:14:35:00.000_Atractosteus_spatula FFS-216:2007-09-21:A:replicateID1024:objectID1345330
measurementID	An identifier for the MeasurementOrFact (information pertaining to measurements, facts, characteristics, or assertions). May be a global unique identifier or an identifier specific to the data set.	This is not necessarily required when creating the eMoF but it can be useful, especially if you would like a way to identify a specific measurement in the data	9c752d22-b09a-11e8-96f8-529269fb1459
measurementType	The nature of the measurement, fact, characteristic, or assertion. Recommended best practice is to use a controlled vocabulary.		temperature salinity length device type
measurementTypeID	An identifier for the measurementType (global unique identifier, URI). The identifier should reference the measurementType in a vocabulary.	OBIS uses NERC to provide these but you can use other vocabularies or ontologies like ENVO or others. When using NERC vocabulary terms, you must choose a term from the P01 collection.	http://vocab.nerc.ac.uk/collection/P01/current/TEMPPR01/ http://vocab.nerc.ac.uk/collection/P01/current/ODSDM021/ http://vocab.nerc.ac.uk/collection/P01/current/OBSINDLX/ ``
measurementValue	The value of the measurement, fact, characteristic, or assertion		20 61.5 121 Van Veen grab
measurementValueID	An identifier for facts stored in the column measurementValue (global unique identifier, URI). This identifier can reference a controlled vocabulary (e.g. for sampling instrument names, methodologies, life stages) or reference a methodology paper with a DOI. When the measurementValue refers to a value and not to a fact, the measurementvalueID has no meaning and should remain empty.		http://vocab.nerc.ac.uk/collection/L22/current/TOOL0653/
measurementAccuracy	The description of the potential error associated with the measurementValue.		0.01 C 0.03 5 mm
measurementUnit	The units associated with the measurementValue. Recommended best practice is to use the International System of Units (SI).		C PPT mm
measurementUnitID	An identifier for the measurementUnit (global unique identifier, URI). The identifier should reference the measurementUnit in a vocabulary.	Recommended practice is populate this field with a term from NERC vocablary’s P06 collection	http://vocab.nerc.ac.uk/collection/P06/current/UPAA/ http://vocab.nerc.ac.uk/collection/P06/current/UPPT/ http://vocab.nerc.ac.uk/collection/P06/current/UXMM/

Over at the IOOS Bio Data Guide repository you can see a script that was used to take data in its original form and align it to Darwin Core Event Core with Extended Measurement or Fact. More information on how to organize data fields into Event and Measurement or Fact can be found in the OBIS Manual *A screenshot of the script available for aligning data to Darwin Core.

Key Points

• Darwin Core uses cores and extensions to model the multitude of biological observation data that exists.

• OBIS uses the Event (or Occurrence) Core with the Extended Measurement or Fact extension to make sure no information is lost.

• Additional fields are required and put into different files when using a Core with the Extended Measurement or Fact extension.

• ID fields are important keys in your data and we recommend building them from the information in your data.

Social Break

Overview

Teaching: min
Exercises: min

Questions

Objectives

Image credit: xkcd

Key Points

QA/QC

Overview

Teaching: 0 min
Exercises: 120 min

Questions

• How can I QC my data?

Objectives

• Data enhancement and quality control

Data enhancement and quality control

OBIS performs a number of quality checks on the data it receives. Red quality flags are attached to occurrence records if errors are encountered, and records may also be rejected if they do not meet minimum requirements. The checks that OBIS performs are documented here and a python implementation is available here. Therefore, prior to publishing your data to OBIS and/or GBIF, it is important to perform quality control on your standardized data. This can help identify any outliers or “faulty” data. It will also help with ensuring that your data is compatible and interoperable with other datasets published to OBIS. There are numerous functions within the obistools R packages that can serve to identify outliers, inspect quality or ensure that the dataset structure fits the required format for both the Event and Occurrence tables.

Recommended initial checks on your data

• Check that all the required Darwin Core terms are present and contain the correct information.
• Make a map from your data to ensure the coordinates are valid and within your expected range.
• Run basic statistics on each column of numeric data (min, max, mean, std. dev., etc.) to identify potential issues.
• Look at unique values of columns containing string entries to identify potential issues (eg. spelling).
• Check for uniqueness of occurrenceID field.
• Check for uniqueness of eventID for each event, if applicable.
• Check that dates are following ISO-8601.
• Check that the scientificNameID is/are valid.

One method for reviewing your data is to use the r package Hmisc and the function describe. Expand the example below using output from this notebook to see how it works.

Hmisc::describe

# pull in the occurrence file from https://www.sciencebase.gov/catalog/item/53a887f4e4b075096c60cfdd
url <- "https://www.sciencebase.gov/catalog/file/get/53a887f4e4b075096c60cfdd?f=__disk__32%2F24%2F80%2F322480c9bcbad19030e29c9ec5e2caeb54cb4a08&allowOpen=true"

occurrence <- read.csv(url)

head(occurrence,n=1)       
  vernacularName                                eventID occurrenceStatus
1  Alligator gar Station_95_Date_09JAN1997:14:35:00.000           Absent
     basisOfRecord       scientificName
1 HumanObservation Atractosteus spatula
                           scientificNameID  kingdom   phylum       class
1 urn:lsid:marinespecies.org:taxname:279822 Animalia Chordata Actinopteri
             order        family        genus scientificNameAuthorship
1 Lepisosteiformes Lepisosteidae Atractosteus        (LacepA"de, 1803)
  taxonRank organismQuantity organismQuantityType
1   Species                0   Relative Abundance
                                                 occurrenceID
1 Station_95_Date_09JAN1997:14:35:00.000_Atractosteus_spatula
         collectionCode
1 Aransas Bay Bag Seine

Hmisc::describe(occurrence)

occurrence 

 18  Variables      334341  Observations
--------------------------------------------------------------------------------
vernacularName
       n  missing distinct
  334341        0       61

lowest : Alligator gar        Arrow shrimp         Atlantic brief squid Atlantic bumper      Atlantic croaker
highest: Striped mullet       Thinstripe hermit    Threadfin shad       White mullet         White shrimp
--------------------------------------------------------------------------------
eventID
       n  missing distinct
  334341        0     5481

lowest : Station_10_Date_04DEC1991:13:59:00.000 Station_10_Date_04SEP2002:13:17:00.000 Station_10_Date_05JUN1991:15:20:00.000 Station_10_Date_07APR1995:12:54:00.000 Station_10_Date_07APR2000:11:16:00.000
highest: Station_99_Date_21APR1998:18:24:00.000 Station_99_Date_22OCT2001:13:12:00.000 Station_99_Date_25JUN1990:13:48:00.000 Station_99_Date_25NOV2003:11:11:00.000 Station_99_Date_27JUN1988:12:45:00.000
--------------------------------------------------------------------------------
occurrenceStatus
       n  missing distinct
  334341        0        2

Value       Absent Present
Frequency   294469   39872
Proportion   0.881   0.119
--------------------------------------------------------------------------------
basisOfRecord
               n          missing         distinct            value
          334341                0                1 HumanObservation 

Value      HumanObservation
Frequency            334341
Proportion                1
--------------------------------------------------------------------------------
scientificName
       n  missing distinct
  334341        0       61

lowest : Adinia xenica               Anchoa mitchilli            Archosargus probatocephalus Ariopsis felis              Atractosteus spatula
highest: Stomatopoda                 Stomolophus meleagris       Syngnathus scovelli         Tozeuma carolinense         Trichiurus lepturus
--------------------------------------------------------------------------------
scientificNameID 
       n  missing distinct
  334341        0       61

lowest : urn:lsid:marinespecies.org:taxname:105792 urn:lsid:marinespecies.org:taxname:107034 urn:lsid:marinespecies.org:taxname:107379 urn:lsid:marinespecies.org:taxname:126983 urn:lsid:marinespecies.org:taxname:127089
highest: urn:lsid:marinespecies.org:taxname:367528 urn:lsid:marinespecies.org:taxname:396707 urn:lsid:marinespecies.org:taxname:421784 urn:lsid:marinespecies.org:taxname:422069 urn:lsid:marinespecies.org:taxname:443955

--------------------------------------------------------------------------------
kingdom
       n  missing distinct    value
  334341        0        1 Animalia

Value      Animalia
Frequency    334341
Proportion        1
--------------------------------------------------------------------------------
phylum
       n  missing distinct
  328860     5481        4

Value      Arthropoda   Chordata   Cnidaria   Mollusca
Frequency       71253     246645       5481       5481
Proportion      0.217      0.750      0.017      0.017
--------------------------------------------------------------------------------
class
       n  missing distinct
  328860     5481        5

lowest : Actinopteri    Cephalopoda    Elasmobranchii Malacostraca   Scyphozoa     
highest: Actinopteri    Cephalopoda    Elasmobranchii Malacostraca   Scyphozoa

Value         Actinopteri    Cephalopoda Elasmobranchii   Malacostraca
Frequency          235683           5481          10962          71253
Proportion          0.717          0.017          0.033          0.217

Value           Scyphozoa
Frequency            5481
Proportion          0.017
--------------------------------------------------------------------------------
order
       n  missing distinct
  328860     5481       22

lowest : Atheriniformes            Batrachoidiformes         Carangaria incertae sedis Carangiformes             Carcharhiniformes        
highest: Rhizostomeae              Scombriformes             Siluriformes              Syngnathiformes           Tetraodontiformes
--------------------------------------------------------------------------------
family
       n  missing distinct
  328860     5481       36

lowest : Ariidae        Atherinopsidae Batrachoididae Carangidae     Carcharhinidae
highest: Stromateidae   Syngnathidae   Tetraodontidae Trichiuridae   Triglidae
--------------------------------------------------------------------------------
genus
       n  missing distinct
  328860     5481       52

lowest : Adinia       Anchoa       Archosargus  Ariopsis     Atractosteus
highest: Sphoeroides  Stomolophus  Syngnathus   Tozeuma      Trichiurus  
--------------------------------------------------------------------------------
scientificNameAuthorship
       n  missing distinct
  328860     5481       52

lowest : (Baird & Girard, 1853)        (Baird & Girard, 1855)        (Blainville, 1823)            (Bosc, 1801)                  (Burkenroad, 1939)
highest: Rathbun, 1896                 Say, 1817 [in Say, 1817-1818] Shipp & Yerger, 1969          Valenciennes, 1836            Winchell, 1864
--------------------------------------------------------------------------------
taxonRank
       n  missing distinct
  334341        0        3

Value        Genus   Order Species
Frequency     5481    5481  323379
Proportion   0.016   0.016   0.967
--------------------------------------------------------------------------------
organismQuantity
       n  missing distinct     Info     Mean      Gmd      .05      .10
  334341        0     8696    0.317  0.01639  0.03141  0.00000  0.00000
     .25      .50      .75      .90      .95
 0.00000  0.00000  0.00000  0.01005  0.07407

lowest : 0.0000000000 0.0000917684 0.0001835370 0.0002136300 0.0002241650
highest: 0.9969931270 0.9974226800 0.9981570220 0.9982300880 1.0000000000
--------------------------------------------------------------------------------
organismQuantityType 
                 n            missing           distinct              value
            334341                  0                  1 Relative Abundance

Value      Relative Abundance
Frequency              334341
                    n               missing              distinct
              334341                     0                     1
                value
Aransas Bay Bag Seine

Value      Aransas Bay Bag Seine
Frequency                 334341
Proportion                     1
--------------------------------------------------------------------------------

Exercise

Challenge: Perform the following minimal quality assurance and control checks:

1. Run a diagnostics report for the data quality.
2. Ensure that the eventIDs are unique.
3. Make sure that the eventDates follow ISO-8601 standards.
4. Determine whether reported depths are accurate.

The event core data used in the checks below can be found in this Excel file.

Solution in R

Install obistools R packages. Use readxl package to read the Excel file.

1. Run a diagnostics report for the data quality

library(readxl)
library(obistools)

trawl_fish <- readxl::read_excel('data/trawl_fish.xlsx')
report <- obistools::report(trawl_fish)
report

1. Check to make sure eventID are unique

   eventid <- obistools::check_eventids(trawl_fish)
   head(eventid)
   

   # A tibble: 6 x 4
    field         level   row message                                                    
    <chr>         <chr> <int> <chr>                                                      
    1 eventID       error     7 eventID IYS:GoA2019:Stn6:trawl is duplicated               
    2 eventID       error     8 eventID IYS:GoA2019:Stn6:trawl is duplicated               
    3 parentEventID error     1 parentEventID IYS:GoA2019:Stn1 has no corresponding eventID
    4 parentEventID error     2 parentEventID IYS:GoA2019:Stn2 has no corresponding eventID
    5 parentEventID error     3 parentEventID IYS:GoA2019:Stn3 has no corresponding eventID
    6 parentEventID error     4 parentEventID IYS:GoA2019:Stn4 has no corresponding eventID
   

2. Check for proper eventDate to ensure they follow ISO 8601 standards:

    eventDate <- obistools::check_eventdate(trawl_fish)
    print(eventDate)  
   

    # A tibble: 3 x 4
     level   row field     message                                                       
     <chr> <int> <chr>     <chr>                                                         
    1 error    10 eventDate eventDate 2019-02-24T07u40 does not seem to be a valid date   
    2 error    13 eventDate eventDate 2019-02-25 11h25min does not seem to be a valid date
    3 error    15 eventDate eventDate 2019-26-2 does not seem to be a valid date    
   

3. From the report generated under exercise 1, you can already see that there’s measurements made on land. This information can also be gathered by plotting the map separately or using the check_onland() or check_depth() functions in the obistools package.

    depth <- obistools::check_depth(trawl_fish)
    onland <- obistools::check_onland(trawl_fish) # Gives the same output.           
    print(depth)  
   

    # A tibble: 1 x 16
     eventID parentEventID eventDate  year month   day decimalLatitude decimalLongitude footprintWKT coordinateUncer~ minimumDepthInM~
     <chr>   <chr>         <chr>     <dbl> <dbl> <dbl>           <dbl>            <dbl> <chr>                   <dbl>            <dbl>
    1 IYS:Go~ IYS:GoA2019:~ 2019-02-~  2019     2    22            67.4            -140. LINESTRING ~            2313.                0
    # ... with 5 more variables: maximumDepthInMeters <dbl>, samplingProtocol <chr>, locality <chr>, locationID <chr>, type <chr>    
   

Solution in Python

Install the pandas, cartopy, and geopandas Python packages. Use pandas to read the Excel file.

import pandas as pd
url = 'https://ioos.github.io/bio_mobilization_workshop/data/trawl_fish.xlsx'
df = pd.read_excel(url) # might need to install openpyxl
df['row'] = df.index.to_numpy()+1 # python starts at zero

1. Run a diagnostics report for the data quality.

   import cartopy.io.shapereader as shpreader
   import geopandas as gpd
   import shapely.geometry as sgeom
   from shapely.ops import unary_union
   from shapely.prepared import prep
   import matplotlib.pyplot as plt
      
   gdf = gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.decimalLongitude, df.decimalLatitude))
      
   land_shp_fname = shpreader.natural_earth(resolution='50m',
                                          category='physical', name='land')
      
   land_geom = unary_union(list(shpreader.Reader(land_shp_fname).geometries()))
   land = prep(land_geom)
      
   for index, row in gdf.iterrows():
       gdf.loc[index, 'on_land'] = land.contains(row.geometry)
      
   fig, axs = plt.subplots(ncols=1,nrows=2)
   # Make a map:
   xlim = ([gdf.total_bounds[0]-2,  gdf.total_bounds[2]+2])
   ylim = ([gdf.total_bounds[1]-2,  gdf.total_bounds[3]+2])
   
   axs[0].set_xlim(xlim)
   axs[0].set_ylim(ylim)
   
   gpd.read_file(land_shp_fname).plot(ax=axs[0])
      
   gdf.loc[gdf['on_land']==False].plot(ax=axs[0], color='green', markersize=1)
   gdf.loc[gdf['on_land']==True].plot(ax=axs[0], color='red', markersize=1)
      
   # Collect some informational material about potential issues w/ data:
   invalid_coord = []
   if len(gdf.loc[gdf['on_land']==True]) > 0:
      invalid_coord.append('Row {} coordinates on land.'.format(gdf.loc[gdf['on_land'] == True,'row'].tolist()[0]))
       
   req_cols = ['eventDate', 'decimalLongitude', 'decimalLatitude', 'scientificName', 'scientificNameID', 'occurrenceStatus', 'basisOfRecord']
   missing_cols = []
   for col in req_cols:
    if col not in gdf.columns:
      missing_cols.append('Column {} is missing.'.format(col))
      
   # Add the information to the figure
   axs[1].text(0.25,0.25,'\n'.join(['\n'.join(missing_cols),'\n'.join(invalid_coord)]))
   axs[1].axis('off')
   plt.show()
   

   

2. Check to make sure eventID are unique

   dup_events = df.loc[df['eventID'].duplicated()]
   print('Duplicated eventID:\n',dup_events[['eventID','row']])
      
   parent_not_event = df.loc[~df['eventID'].isin(df['parentEventID'].unique())]
   print('\nparentEventID missing corresponding eventID:\n', parent_not_event[['parentEventID','row']])
   

   Duplicated eventID:
                      eventID  row
   6  IYS:GoA2019:Stn6:trawl    7
   7  IYS:GoA2019:Stn6:trawl    8
      
   parentEventID missing corresponding eventID:
            parentEventID  row
   0    IYS:GoA2019:Stn1    1
   1    IYS:GoA2019:Stn2    2
   2    IYS:GoA2019:Stn3    3
   3    IYS:GoA2019:Stn4    4
   4    IYS:GoA2019:Stn5    5
   ..                ...  ...
   59  IYS:GoA2019:Stn60   60
   60  IYS:GoA2019:Stn61   61
   61  IYS:GoA2019:Stn62   62
   62  IYS:GoA2019:Stn63   63
   63  IYS:GoA2019:Stn64   64
   [64 rows x 2 columns]
   

3. Check for proper eventDate to ensure they follow ISO 8601 standards:

   for date in df['eventDate']:
       try:
           pd.to_datetime(date)
       except:
           print("Date",date,"might not follow ISO 8601")
   

4. From the report generated under exercise 1, you can already see that there’s measurements made on land. Now let’s check the depths are within reason for the points. Let’s use the GEBCO bathymetry dataset served in the coastwatch ERDDAP.

   import time
   import numpy as np
      
   df['bathy'] = np.nan # initialize column
      
   for index, row in df.iterrows():
       base_url = 'https://coastwatch.pfeg.noaa.gov/erddap/griddap/GEBCO_2020.csvp?'
       query_url = 'elevation%5B({})%5D%5B({})%5D'.format(row['decimalLatitude'],row['decimalLongitude'])
       url = base_url+query_url
       bathy = pd.read_csv(url)
       df.at[index,'bathy'] = bathy['elevation (m)'] # insert bathymetry value
       time.sleep(0.5) # to not ping erddap too much
      
   # make new column for depth in meters as negative because GEBCO is Elevation relative to sea level
   df['neg_maximumDepthInMeters'] = -1*df['maximumDepthInMeters']
      
   print('maximumDepthInMeters deeper than GEBCO bathymetry:')
   if len( df.loc[df['neg_maximumDepthInMeters'] < df['bathy']] ) > 0:
      print(df.loc[df['neg_maximumDepthInMeters'] < df['bathy']].T)
   else:
      print('None')
   

   maximumDepthInMeters deeper than GEBCO bathymetry:
                                                                                 4
   eventID                                                  IYS:GoA2019:Stn5:trawl
   parentEventID                                                  IYS:GoA2019:Stn5
   eventDate                             2019-02-22T09:49:00Z/2019-02-22T10:49:00Z
   year                                                                       2019
   month                                                                         2
   day                                                                          22
   decimalLatitude                                                       67.399004
   decimalLongitude                                                    -139.552501
   footprintWKT                   LINESTRING ( -139.583 67.397 , -139.522 67.401 )
   coordinateUncertaintyInMeters                                       2313.094678
   minimumDepthInMeters                                                          0
   maximumDepthInMeters                                                       33.2
   samplingProtocol                                                 midwater trawl
   locality                                                                    NaN
   locationID                                                                  NaN
   type                                                             midwater trawl
   row                                                                           5
   bathy                                                                     306.0
   neg_maximumDepthInMeters                                                  -33.2
   

Tip

• In some cases you’ll want to ensure the values are representative of the entity you are reporting.
• For example, individualCount should be an integer. So, checking that column for integer values would be good.

Key Points

• Several packages (e.g. obistools, Hmisc, pandas) can be used to QA/QC data.

Metadata and publishing

Overview

Teaching: 0 min
Exercises: 120 min

Questions

• How are my data published?

• What metadata are required for publishing?

Objectives

• Showing data publishing pipeline

• Introducing the IPT

• Introduction to EML

Data Publishing Pipeline

After going through QAQC and being standardized to Darwin Core, the dataset are uploaded to an IPT. Metadata is added in the form of EML and the dataset published as a Darwin Core Archive (DwC-A). The data are then pushed to central OBIS. Each dataset also has the option of being pushed to GBIF through the OBIS IPT.

Data publishing pipeline. Image credit: Enrique Montes

Integrated Publishing Toolkit

The Integrated Publishing Toolkit (IPT) is an open-source web application developed and maintained by the Global Biodiversity Information Facility (GBIF) for publishing biodiversity data. The IPT makes it easy to share four types of biodiversity-related information:

• primary taxon occurrence data
• sampling event data
• general metadata about data sources
• taxon checklists

GBIF maintains a very detailed IPT manual and the OBIS Manual offers OBIS-specific details on publishing through the IPT. You can choose to download and install your own instance of the IPT but it might be complicated to register it with OBIS. Instead it’s recommended to work with one of the OBIS nodes to publish your data through their IPT. OBIS nodes publish data that are logical for their region or theme. After publishing the data through their IPT the data are harvested by central OBIS.

The requirements for publishing via an OBIS node IPT are that:

1. you have contacted the node to ensure the data are a good fit for that node
2. the data follows Darwin Core (DwC) and Ecological Metadata Language (EML)
3. includes the required Darwin Core and EML metadata elements

Ecological Metadata Language (EML)

Both OBIS and GBIF use Ecological Metadata Language (EML) as the metadata standard associated with the data. For the purposes of this workshop we will not dive into the world of EML. However, we should note that when publishing your data through the IPT, the IPT helps you create an EML file as part of the Darwin Core Archive (DwC-A). As such, if you publish your own data through the IPT, there is no need for innate knowledge on the EML format. But there are a minimum required number of fields that would need to be filled out in the IPT: title, abstract, citation, and several contacts.

More information on EML can be found at the EML standard page, and in the bio data guide. There are also a number of R packages for working with EML, reviewed here.

Tip

Try to collect as much of this information as possible before and during the Darwin Core alignment process. It will significantly reduce the amount of time it takes to load the data into the IPT.

Required EML metadata fields for sharing to OBIS

Best practices for these fields are explained in detail in the OBIS manual

Other EML fields to consider

Key Points

• The IPT is a well-documented and flexible system for publishing data to OBIS

• Some Darwin Core and Ecological Metadata Language fields are required for publishing to OBIS.

• Strive to write more than the minimal metadata

Continuing the Conversation

Overview

Teaching: 0 min
Exercises: 120 min

Questions

• How do I continue my work after the workshop?

• Where do I go to look for help with DwC alignment?

• How do I provide feedback about this workshop?

Objectives

• Learn how to interact with the SMBD group.

• Complete the (optional) post-workshop survey.

Thank you for attending the workshop!

Our hope is that you were already able to submit your data to OBIS but often work needs to continue beyond the workshop. This page lists some resources to help get your data the rest of the way into OBIS.

The Standardizing Marine Bio Data Group

One of the primary goals of the “Standardizing Marine Bio Data” (SMBD) exists to provide a space for individuals to help each other put data into OBIS. Many from this workshop meet monthly at the SMBD and interact asynchronously to offer help for those aligning to DwC. There are two primary ways to interact with the SMBD:

Monthly SMBD Meetings

Information about monthly SMBD meetings can be found on the SMBD github page here. Anyone is welcome to attend - we are a small community of individuals who are passionate about getting data into OBIS, DwC, other standard forms.

SMBD GitHub Issue Tracker

You are also welcome to open an “issue” in the SMBD GitHub Issue tracker. This issue tracker is not just for “issue reporting” - please use it to ask questions or start discussions about your data.

Add Your Dataset as an Example

You can upload your dataset into the /datasets/ directory so that the SMBD community can learn from your work and help finish up any remaining work.

Tip

More information about the SMBD group can be found at https://github.com/ioos/bio_data_guide.

Post-Workshop Survey

If you wish to provide feedback please use this post-workshop survey.

OceanTeacher Global Academy (OTGA) courses

If you are interested in continuing to learn about data fornatting and mobilization, there are courses you can enroll for on OTGA. A self-paced OBIS course that covers similar content as this workshop is currently in development and should become accessible in the coming months! Note that you do need to create an account to access the OTGA platform.

Key Points

• The Standardizing Marine Bio Data (SBMD) group is available to help.

• The SMBD meets monthly and you are welcome to join.

• The SMBD github issue tracker is the best place to reach out for help.