In EDIorg/emlAssemblyLine: A tool kit for building EML metadata workflows

Overview

Metadata inferred during the templating process should be validated by the user and missing info added. Use spreadsheet and text editors for this process. Template specific guides are listed below.

NOTES:

Tabular templates: Leave empty cells blank, don't fill with NAs.
Free-text templates: Keep template content simple. Complex formatting can lead to errors.

abstract (.docx, .md, .txt)

Describes the salient features of a dataset in a concise summary much like an abstract does in a journal article. It should cover what the data are and why they were created.

Example

methods (.docx, .md, .txt)

Describes the data creation methods. Includes enough detail for future users to correctly use the data. Lists instrument descriptions, protocols, etc.

Example

keywords.txt

Describes the data in a small set of terms. Keywords facilitate search and discovery on scientific terms, as well as names of research groups, field stations, and other organizations. Using a controlled vocabulary or thesaurus vastly improves discovery. We recommend using the LTER Controlled Vocabulary when possible.

Columns:

keyword One keyword per line
keywordThesaurus URI of the vocabulary from which the keyword originates.

Example

personnel.txt

Describes the personnel and funding sources involved in the creation of the data. This facilitates attribution and reporting.

Columns:

givenName First name
middleInitial Middle initial
surName Last name
organizationName Organization the person belongs to
electronicMailAddress Email address
userId Persons research identifier (e.g. ORCID). Links a persons research profile to a data publication.
role Role of the person with respect to the data. Persons serving more than one role are listed on separate lines (e.g. replicate the persons info on separate lines but change the role. Valid options:
- creator Author of the data. Will appear in the data citation.
- PI Principal investigator the data were created under. Will appear with project level metadata.
- contact A point of contact for questions about the data. Can be an organization or position (e.g. data manager). To do this, enter the organization or position name under givenName and leave middleInitial and surName empty.
- Other roles (e.g. Field Technician) will be listed as associated parties to the data.
Funding information is listed with PIs
- projectTitle Title of project the data were created under. If ancillary projects were involved, then add as new lines below the primary project with the PIs info replicated.
- fundingAgency Agency the project was funded by.
- fundingNumber Grant or award number.

Example

intellectual_rights.txt

Describes how the data may be used. Releasing without restriction (CC0) or with minimal attribution (CC BY) maximizes value and future use.

Example

attributes_*.txt

Describes columns of a data table (classes, units, datetime formats, missing value codes).

Columns:

attributeName Column name
attributeDefinition Column definition
class Column class. Valid options are:
- numeric Numeric variable
- categorical Categorical variable (i.e. nominal)
- character Free text character strings (e.g. notes)
- Date Date and time variable
unit Column unit. Required for numeric classes. Select from EML's standard unit dictionary, accessible with view_unit_dictionary(). Use values in the "id" column. If not found, then define as a custom unit (see custom_units.txt).
dateTimeFormatString Format string. Required for Date classes. Valid format string components are:
- Y Year
- M Month
- D Day
- h Hour
- m Minute
- s Second Common separators of format string components (e.g. - / \ :) are supported.
missingValueCode Missing value code. Required for columns containing a missing value code).
missingValueCodeExplanation Definition of missing value code.

Example 1, Example 2

custom_units.txt

Describes non-standard units used in a data table attribute template.

Columns:

id Unit name listed in the unit column of the table attributes template (e.g. feetPerSecond)
unitType Unit type (e.g. velocity)
parentSI SI equivalent (e.g. metersPerSecond)
multiplierToSI Multiplier to SI equivalent (e.g. 0.3048)
description Abbreviation (e.g. ft/s)

Example

catvars_*.txt

Describes categorical variables of a data table (if any columns are classified as categorical in table attributes template).

Columns:

attributeName Column name
code Categorical variable
definition Definition of categorical variable

Example 1, Example 2

geographic_coverage.txt

Describes where the data were collected.

Columns:

geographicDescription Brief description of location.
northBoundingCoordinate North coordinate
southBoundingCoordinate South coordinate
eastBoundingCoordinate East coordinate
westBoundingCoordinate West coordinate

Coordinates must be in decimal degrees and include a minus sign (-) for latitudes south of the equator and longitudes west of the prime meridian. For points, repeat latitude and longitude coordinates in respective north/south and east/west columns.

Example

taxonomic_coverage.txt

Describes biological organisms occuring in the data and helps resolve them to authority systems. If matches can be made, then the full taxonomic hierarchy of scientific and common names are automatically rendered in the final EML metadata. This enables future users to search on any taxonomic level of interest across data packages in repositories.

Columns:

taxa_raw Taxon name as it occurs in the data and as it will be listed in the metadata if no value is listed under the name_resolved column. Can be single word or species binomial.
name_type Type of name. Can be "scientific" or "common".
name_resolved Taxons name as found in an authority system.
authority_system Authority system in which the taxa’s name was found. Can be: "ITIS", "WORMS", "or "GBIF".
authority_id Taxa’s identifier in the authority system (e.g. 168469).

Example

provenance.txt

Describes source datasets. Explicitly listing the DOIs and/or URLs of input data help future users understand in greater detail how the derived data were created and may some day be able to assign attribution to the creators of referenced datasets.

Provenance metadata can be automatically generated for supported repositories simply by specifying an identifier (i.e. EDI) in the systemID column. For unsupported repositories, the systemID column should be left blank.

Columns:

dataPackageID Data package identifier. Supplying a valid packageID and systemID (of supported systems) is all that is needed to create a complete provenance record.
systemID System (i.e. data repository) identifier. Currently supported systems are: EDI (Environmental Data Initiative). Leave this column blank unless specifying a supported system.
url URL linking to an online source (i.e. data, paper, etc.). Required when a source can't be defined by a packageID and systemID.
onlineDescription Description of the data source. Required when a source can't be defined by a packageID and systemID.
title The source title. Required when a source can't be defined by a packageID and systemID.
givenName A creator or contacts given name. Required when a source can't be defined by a packageID and systemID.
middleInitial A creator or contacts middle initial. Required when a source can't be defined by a packageID and systemID.
surName A creator or contacts middle initial. Required when a source can't be defined by a packageID and systemID.
role "creator" and "contact" of the data source. Required when a source can't be defined by a packageID and systemID. Add both the creator and contact as separate rows within the template, where the information in each row is duplicated except for the givenName, middleInitial, surName (or organizationName), and role fields.
organizationName Name of organization the creator or contact belongs to. Required when a source can't be defined by a packageID and systemID.
email Email of the creator or contact. Required when a source can't be defined by a packageID and systemID.

Example

annotations.txt

Adds semantic meaning to metadata (variables, locations, persons, etc.) through links to ontology terms. This enables greater human understanding and machine actionability (linked data) and greatly improves the discoverability and interoperability of data in general.

Columns:

id A unique identifier for the element being annotated.
element The element being annotated.
context The context of the subject (i.e. element value) being annotated (e.g. If the same column name occurs in more than one data tables, you will need to know which table it came from.).
subject The element value to be annotated.
predicate_label The predicate label (a.k.a. property) describing the relation of the subject to the object. This label should be copied directly from an ontology.
predicate_uri The predicate label URI copied directly from an ontology.
object_label The object label (a.k.a. value) describing the subject. This label should be copied directly from an ontology.
object_uri The object URI copied from an ontology.