Resources for collaborators and data contributors to SharkTraits. This repositoty contains a data entry template and protocol for data collection. To get involved or for any questions regarding data assignments, the resource database, or data template and protocol please contact the SharkTraits Administrators.

The Shark Traits Database is an open source research initiative that aims to make all observations and measurements of chondrichthyans (sharks, rays, and chimaeras) accessible in order to more rapidly advance resesearch. The framework behind the database is heavily based on the open source Coral Trait Database. Anyone collecting chondrichthyan life history data can join and contribute to the growing data compilation. Contributors have control over the privacy of their data and greatly benefit from being able to download complementary public data from the database in a standard format for use in their analyses. We hope that private data will become public once the contributor has published them, which will subsequently be cited when their data are used in analyses by other people. The citation system has been carefully designed to ensure full transparency about the origin of each individual data point as well as larger data compilations of other peoples' data (such as data extracted from literature for meta-analyses).

• Assemble disparate information on life history traits of sharks, rays, and chimaeras
• Provide unrestricted, open-source and easy access to shark trait data
• Avoid redundant data gathering efforts
• Facilitate and encourage the appropriate crediting of original data sources
• Engage the shark and ray research community in the collection and quality control of trait data
• Facilitate reproducible science
• Do the above with a sustainable future for the database in mind, in terms of management and longevity

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Contact the Administrators for any information about the database.

• [email protected]

The database was designed to contain population-level characteristic measurements, which are trait estimates of a species at a given location. These traits are grouped into six use-classes:

• Length
• Age
• Growth
• Reproduction
• Demography
• Relationships (conversions between different units, such as length to weight)

Observations bind related trait measurements of the same population. For example, estimates of age at maturity for males and females of the same population results in one observation with two measurements (each corresponding with a different trait of the population).

Observation-level data include the chondrichthyan species, location and resource (i.e. reference). These data are the same for all measurements corresponding to the observation. When entering or importing trait data, the following is minimally required:

• Contributor
• Access (public or private)
• Species¹
• Location²
• Resource³

¹All observations must be associated with a valid species name. We are using the taxonomy of Weigmann 2016. Note that this taxonomy does not currently include all the taxonomic nomenclatrual revisions in Naylor et al 2016. The list of valid species names is built in to the spreadsheet template and can be accessed as a dropdown menu. First select the appropriate species_superorder (Chimaeriformes, Batoidea, Squalimoroh Sharks, or Galeomorph Sharks) and a reduced taxa set to select from will be provided in the species_name column.

²Location information can be entered in several formats/columns: (1) marine_province is restricted to the desingated marine longhurst provinces, (2) location_name is for general locations provided in the study methods (e.g. Southern Calfifornia Bight, Northern Gulf of Mexico), and (3) lat and long coordinates if provided.

³ The resource ID should be taken from the unique ID found in the references database. This is a shiny database that contains all materials compiled on Shark-Refences.You can filter the reference databse by year and/or search for author, keyword, species, journal, etc. Resource can be left blank for unpublished data, but data must be kept private.

Measurement-level data include the sex, trait, value, standard (unit), methodology, estimates of precision (if applicable), and model used. When entering or importing trait data, the following is minimally required:

• Sex
• Trait
• Value
• Standard

The current list of traits is as follows:

A trait should always have a Standard and a Method. In cases where there are multiple models that could be fitted in a method, there should be a Model specified as well.

Trait Classes

Trait Classes

Given that this trait class deals with model outputs, there is only a single placeholder Standard as the units of each Trait are implicit.

Trait Classes

Trait Classes

Trait Classes

The Relationships Trait Class is a somewhat "catch-all" category that encompasses any time of conversion equations between different types of data, such as length to length conversions (e.g. fork length to total length), and length to weight conversions. The parameters a and b are the most commonly used ones and represent the coefficients on the typical conversion equations of the form . The parameters c and d will probably be seldom used, but are presented in case a conversion equation has more than two coefficients.

Trait Classes

The Shark Traits Database is a research tool, not a meta-data catalog.

A meta-data repository captures dataset-level information about your data set, so that people can easily find it. Examples of meta-data repositories include DRYAD, Ecological Archives and Figshare. You are encouraged to submit data sets to meta-data repositories to help ensure their longevity and the reproducibility of the results for which the data were originally collected.

The Shark Traits Database captures data-level information so that measurements from multiple data sets can be integrated, extracted, compared and analyzed.

One way to think about The Shark Traits Database is as a very large data-set being cobbled together by the elasmobranch community for everyone to use, avoiding redundant efforts, identifying knowledge gaps, informing management, and speeding up science.

The database currently accepts -level measurements of chondrichthyans. Data must be associated with a species name (i.e., not genus- or family-level data) and public data must be associated with a published resource (e.g., paper, monograph, technical report, or book).

Data accepted:

• Species-level model-derived data that has been published
• Species-level expert or group opinion data that has been published
• Unpublished data if kept private until published

Data not accepted:

• Future predictions
• Data for other taxonomic resolutions (e.g., genus or family).

Unpublished data can also be imported into the database if it is kept private. Private data can be made public once associated with a published resource. Benefits of accepting unpublished data include:

• Being able to easily download the data alongside useful public data for analysis
• Being more likely to contribute the data at end of project and improve its longevity
• Identify duplicate research efforts

To contribute data, you need to email the database Administrator to become a contributor.

Having a primary, peer-reviewed resource is essential for maintaining data quality, contributor recognition and scientific rigor.

Even if only entering small amounts of data, the spreadsheet submission process is recommended. We have developed a fillable template which can be accessed here. A spreadsheet import is the fastest way to get data into the database. The import function accepts csv-formatted spreadsheets and runs a number of tests to make sure your data fit the database correctly (note that you can export csv-formatted files from Excel using "Save as..."). Any errors will reject the entire import and the system will attempt to tell you where the errors occur, so you can fix these errors and try the import again.

The spreadsheet you import must have a header with at least the following column names:

observation_id, access, user_id, resource_id, species_name, location_id, trait_name, standard_name, methodology_name, value, value_type, precision, precision_type, precision_upper, sample_size, notes

species_id, trait_id, method_id, and model_id can be included instead of species_name, trait_name, method_name, and model_name, respectively (or you can include both ids and names). Having the names can be useful for navigating large spreadsheets. These names must exactly reflect the names in the database, so it is best to copy and paste names directly from the database.

resource_id is reserved for the original data resource (i.e., the paper that reports the original collection of the measurement). You can credit papers that compiled large datasets from the literature by adding a column named resource_secondary_id. resource_id and resource_secondary_id may be substituted with resource_doi and resource_secondary_doi, respectively (the doi should start with "10.", not "doi:"). The resource will automatically be added using Crossref if the doi is not already in the database.

We have collated all the references from shark-references.com and generated a unique resource_id for each reference, so the easiest way to populate this field is by searching for the resource_id in the reference list provided.

user_id must by your own database user id (i.e., you cannot import data for other people). You can find your user id by clicking on your name in the top right corner and selecting "My Observations".

Copy and paste the above header into a text file and save as import_trait_author_year.csv, where author and year correspond with the resource (paper). Alternatively, download a CSV or Excel template.

The first six required columns are associated with the observation.

observation_id is an unique integer that groups a set of measurements into one observation. In the example below, the first two rows belong to the same observation of a shark.

access is a boolean value indicating if the observation should be accessible (0 denotes private and 1 denotes public). In the example below, the data are public.

user_id is the unique ID (name or integer) of the person entering the data.

species_name and/or species_id is the unique name or ID of the species of which the observation was taken. IDs occur in grey to the left of species or at the top of a given species' observation page.

marine_province is the large marine regions (e.g. longhurst province) where the study was conducted. You can see a global map of provinces and associated names here

location_id is the unique ID of the location where the observation took place.

resource_id is the unique ID of the resource (paper) where the observation was published. resource_id can be empty for unpublished data, in which case access must be private (0) until the data are published and the published resource is entered. In the example the resource_id 606 is Gleason et al. (2009).

The remaining columns are associated with measurement-level data. All measurements corresponding to the same observation should have exactly the same observation-level data.

Warning All measurements corresponding to the same observation should have exactly the same observation-level data. Use copy and paste or fill down to avoid making errors.

trait_name and/or trait_id is the unique name or ID (integer) of the species-level characteristic that was measured.

standard_name is the unique ID of the standard (measurement unit) that was used to measure the trait.

method_name is the unique ID of the methodology used to measure the trait.

model_name is the unique name of the model being used for the given methodology to measure the trait.

value is the actual measured value (number, text, true/false, etc.). If the value is an option of a categorical trait (e.g., growth form), then the value must exactly match the value options for the trait (e.g., massive).

value_type describes the type of value. Current options are:

• raw_value for a direct measurement,
• mean if the value represents the mean of more than one value,
• median if the value represents the median of more than one value,
• maximum if the value represents the maximum of more than one value,
• minimum if the value represents the minimum of more than one value,
• model_derived if the value is derived from a model,
• expert_opinion if the actual value has not been measured directly, but an expert feels confident of the value, perhaps based on phylogenetic relatedness or an indirect observation,
• group_opinion if the actual value has not been measured directly, but a group of experts feel confident of the value.

precision is the level of uncertainty associated with the value if it is made up from more than one measurement (e.g., mean).

precision_type is the kind of uncertainty that the precision estimate (above) corresponds with. Current options are:

• standard_error
• standard_deviation
• 95_ci
• range

precision_upper is used to capture the maximum (upper) value if range is used (above).

sample_size is the number of data points that were used to calculate the value. Leave this field blank if equal to one (e.g., a raw_value).

Optional fields include:

notes is an optional field for reporting useful information about how the measurement was made.

dubious is a optional boolean field noting whether the data being reported is classified as dubious (1) or not (0 or blank) by the user entering the data.

validated is a boolean field to be used only for Growth Trait Class measurements, indicating whether any validation of the growth model has been attempted (1) or not (0).

validation_type denotes the method used for validating the growth model. At the moment, this is a text field with pre-determined values.

If your data is well-managed, you can ask a database programmer to upload it for you. The data will be associated with your name and made private. You are required to make the data public yourself (if desired).

Entering published data not already in the database in strongly encouraged to improve the data's longevity and augment data analysis. A case in which this might occur is a meta-analysis. The data enterer can keep the data they submit private until their study is published.

The key objective is to extract data from resources in such a way as to avoid people ever needing to go back to the that resource again.

For example, extracting only the mean value of a trait measurement from a paper, without extracting any measure of variation or the context in which the trait was measured, will mean that the data may not be useful for other purposes. Someone else might need to go back and extract the information again, and there is a chance your initial efforts won't be cited.

• Primary resources only. Often people enter data from summary tables in papers that come from other (primary) resources. It is important to enter the data from the primary resource for two reasons: (1) so that the primary resource's author is credited for their work, and (2) to avoid data duplication, where the same data are entered from both the primary and secondary resource. Secondary resources, such as meta-analyses, can be credited for large data compilations.

• Careful extraction. Copy values from tables carefully and double check. Extracting data from figures can be done with software like ImageJ or DataThief, where a scale can be set based on axis values and measurements of plotted data made, including error bars.

There are three levels of data review.

1. Contributor-level review at time of submission, Once submitted, data are tagged as pending.
2. Editor-level review. The relevant Editor/s for traits in your submission are automatically notified by email. The contributor may be contacted by the Editor if there are any issues with the submission. The Editor will approve the submission once satisfied.
3. User-level review. Anyone signed-in as a database user can report an issue with an observation record, and the submitter and the Editor will be notified by email.

Basic error checking will ensure data submissions fit into the database. Error checking will improve as different issues arise. Measurement records with the same species, location, resource and value will be flagged as potential duplicates.

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