grantDistillOS_Schema_stable.yaml

# this is a LinkML schema for use with the ontogpt project https://github.com/monarch-initiative/ontogpt
# it's purpose is to provide the framework for distilling the Public Access / Open-Science characteristics of grants in a systematic and automatable fashion

# NOTE: datamodel-codegen alters the casing of your chosen fields.  Goes from camel casing to full camel casing (e.g. first letter)

# we will use examples provided here: https://github.com/monarch-initiative/ontogpt/blob/main/src/ontogpt/templates/
# lets start by initalizing the collection of classes
# note that we are NOT beginning with the w3id / linkml boilerplate for this initial draft
# UPDATE: Ok, fine, we'll include some of this information
#id: http://w3id.org/.../...
#maybe?
id: https://w3id.org/linkml/meta.yaml
name: grant-distill-open-science-schema
title: Grant distillation of Open-Science Characteristics
description: >-
  A framework for distilling the Public Access / Open-Science characteristics of grants in a systematic and automatable fashion
license: https://creativecommons.org/publicdomain/zero/1.0/
prefixes:
  linkml: https://w3id.org/linkml/meta.schema.json 
keywords:
  - united states government
  - federal
  - agency
  - grant
  - open-science
version: 0.1

imports:
  - linkml:types
  - core

default_range: string

classes: 
  grantrecord:
    tree_root: true
    attributes:
      # grant code
      grantid:
        description: The alpha-numeric code corresponding grant-record entry in the corresponding database or grant record-keeping system.  Likely specific to the associated record-keeping system and not valid / functional outside of that system (i.e. across agencies).
        examples:
          # NSF example
          - value: 2138403
          # NSF example
          - value: 2048231
          # NIH example
          - value: 5R21NS125466-02
          # NIH example
          - value: 5R01NS127892-02
        identifier: true
      # grant agency / org
      # TODO: should probably be a separate class with corresponding 'slots' https://linkml.io/linkml/schemas/slots.html
      # TODO: along with later mentioned sub-sub division and / or even specific programs, maybe also include Program officer or responsible / monitoring indiviual.
      # This could all be under something like grant sourcing / provenance 
      # TODO: (FOR WAY LATER) There probably exist controlled ontologies for these (e.g. http://www.oegov.us/)
      grantingagencyorg: 
        description: The top-level organization or agency which is providing / furnishing the grant.  If this information cannot be parsed from the input data source, a null value instead. 
        examples:
          # allow acronyms
          - value: NSF
          - value: NIH
          - value: DOE
          # allow full agency names
          - value: National Science Foundation
          - value: National Institute of Health
          - value: Department of Energy
        ifabsent: null
      # grant sub-division
      grantingsubdivision:
        description: The immediate sub-division, under the 'grantingAgency' organization structure, corresponding to the current grant.  If this information cannot be parsed from the input data source, a null value instead. 
        examples:
        # NIH Examples
        - value: National Cancer Institute
        - value: National Eye Institute
        # NIH acronyms
        - value: NCI
        - value: NEI
        # NSF examples
        - value: Directorate for Computer and Information Science and Engineering
        - value: Directorate for Biological Sciences
        # NSF Acronyms
        - value: CISE
        - value: BIO
        # ifabsent
        ifabsent: null
        # TODO: consider including sub-sub division and / or even specific programs
      # grant value characteristics
      grantvalue: 
        description: The monetary value of grant
        range: integer 
        ifabsent: .NAN
      # grant temporality characteristics
      # TODO: can potentially be its own class
      grantbegindate: 
        description: The date at which the grant went or will go into effect.
        range: datetime 
        ifabsent: null
      grantenddate: 
        description: The date at which the grant did or will conclude.
        range: datetime 
        ifabsent: null
      amendmentdates:
        description: The dates on, for, or at which the grant was modified or ammended.
        multivalued: true
        ifabsent: []
      # grant recipient information
      # TODO: can potentially be its own class
      grantrecipient_individual:
        description: The *individual* (or enumerated persons) who is (are) the designated recipient(s) of the grant.
        multivalued: true
        ifabsent: null
        examples:
        - value: John Doe
        - value: Professor James Moriarty
      # TODO: can be enriched WITHOUT gpt with machine-accessible location information (city, state), contact info, category information (MSI,HBC,etc)
      # TODO: (FOR WAY LATER) There probably exist controlled ontologies for these
      grantrecipient_org:
        description: The relevant (e.g. managing indirect costs) *organization* associated with the grantRecipient_individual.  If not explicitly designated or stated, presumably the grantRecipient_individual's primary organization or affiliation.
        examples:
        - value: Allen Institute
        - value: Indiana University
      # grant topic information
      # TODO: should definitely be several seprate classes
      #grantCharacter:
      #  description: The relevant characteristics of the grant.
      #  attributes:
      worktype: 
        description: The type(s) of work that the grant might seek to support, elicit, or engender.  Lowercased. If it cannot be parsed or determined, then null.
        multivalued: true
        range: worktype
        ifabsent: []
        examples:
        - value: [research]
        - value: [curation]
        - value: [research, curation]
      workdomain: 
        description: The broad categorical / topical domain(s) of the associated grant, across, in, or with which the work is to be done. If it cannot be parsed or determined, then null.
        multivalued: true
        range: workdomain
        ifabsent: []
        examples:
        - value: [biosciences]
        - value: [engineering]
        - value: [biosciences, engineering]
      opensciencetraits: 
        description: The aspects or characteristics of the associated grant which are relevant to Public Access and/or Open-Science. If no such aspects are found, then an empty list, [].
        multivalued: true
        range: openScienceTraits
        ifabsent: []
        examples:
        - value: [data sharing]
        - value: [data storage]
        - value: [data sharing, data storage]  
  # END grantRecord


# begin enums (https://linkml.io/linkml/schemas/models.html#enums)
enums:
  worktype:
    description: The type of work that a grant might seek to support, elicit, or engender.  Lowercased.  If it cannot be parsed or determined, then null.
    # generated with the help of chatGPT
    permissible_values:
      research: 
        description: Endeavors which are designed for the development of knowledge or insight from existing data sources, or from yet undetermined information sources.
      curation: 
        description: Endeavors which are designed or intended for the collecton, derevation, provision, and/or organizaton of data, artifacts, and other scholarly resources.  Does not necessarily entail the use of the resources for research by the organization performing the curation.
      service provision:
        description: Endeavors which are designed or intended for the ongoing provision of services, such as training, outreach, education, and/or intervention (e.g. medical) to a specific community or group.
      coordination: 
        description: Endeavors which are designed or intended for the coordination and/or management the work of individuals, institutions, and/or organizations.
      infrastructure devops: 
        description: Endeavors which are designed or intended for the development and/or provision of infrastructural resources, such as facilities, equipment, software, procedures, systems, and/or protocols.
      implementation: 
        description: Endeavors which are designed or intended for the transition from plans, prototypes, and proofs of concept to actual implementations that can scale to substantially broad(er) use-cases, applications, consumer-groups, and/or needs.
  #END worktype
  #BEGIN workdomain
  workdomain:
    description: The broad categorical / topical domain across, in, or with which the work is to be done.
    # TODO: There's almost certianly an ontology for this somewhere.
    permissible_values:
      biosciences:  
        description: Fields such as biology, genetics, biotechnology, biochemistry, neuroscience, and ecology.
      pysical sciences: 
        description: Fields such as physics, chemistry, astronomy, geology, and materials science.
      engineering:
        description: Fields such as mechanical engineering, electrical engineering, civil engineering, chemical engineering, and computer engineering.  Typically concerned with the controlled and intentional instantiation of structures, states, and processes.
      mathematics: 
        description: Fields such as mathematics, statistics, and applied mathematics.
      social sciences:
        description: Fields such as psychology, sociology, anthropology, political science, economics, and communication studies.
      interdisciplinary:
        description: Fields that necessarily and definitionally span multiple disciplines such as environmental science, data science, computational science, cognitive science, and global studies.  Typically characteried by a tacit or explicit substrate neutrality or independence.
  #END workdomain
  #BEGIN openScienceTraits
  opensciencetraits:
    description: The aspects or characteristics which are relevant to Public Access and/or Open-Science.
    # TODO: OS ontology goes here.
    # TODO: deduplicate this.
    permissible_values:
      data sharing:
        description: Data sharing refers to the practice of making research data available to others for reuse or validation of research findings.
      data storage:
        description: Data storage refers to the physical or virtual space where data is stored, organized, and managed.
      metadata standards:
        description: Metadata standards are sets of guidelines and rules that define the format, content, and structure of metadata used to describe and manage research data.
      metadata:
        description: Metadata refers to the descriptive information about research data, including information about its content, context, structure, and other characteristics.
      data repositories:
        description: Data repositories are online platforms or databases that provide long-term storage and access to research data.
      data management:
        description: Data management refers to the planning, organizing, storing, and sharing of research data throughout its lifecycle.
      data curation:
        description: Data curation involves managing and preserving research data throughout its lifecycle, including data selection, acquisition, appraisal, preservation, and sharing.
      data citation:
        description: Data citation refers to the practice of citing research data in scholarly publications to acknowledge and give credit to the data creators.
      data reuse:
        description: Data reuse refers to the practice of reusing research data for different purposes, such as further analysis, validation of research findings, or the development of new research questions.
      metadata schema:
        description: A metadata schema is a specific set of rules, standards, and guidelines used to describe and manage research data.
      data quality:
        description: Data quality refers to the accuracy, completeness, consistency, and reliability of research data.
      data privacy:
        description: Data privacy refers to the ethical and legal obligations to protect the confidentiality and anonymity of research participants and their personal information.
      data security:
        description: Data security refers to the measures and protocols used to protect research data from unauthorized access, loss, theft, or damage.
      data access:
        description: Data access refers to the ability of researchers to access and use research data for their own research purposes.
      open access:
        description: Open access refers to the practice of making research outputs, such as publications and data, freely and openly accessible to the public.
      public access:
        description: Public access refers to the practice of making research outputs, such as publications and data, openly accessible to the public.
      open data:
        description: Open data refers to the practice of making research data openly accessible, reusable, and shareable by anyone.
      open source:
        description:  Open source refers to the practice of making software and code openly accessible, reusable, and shareable by anyone.
      data sharing agreement:
        description: A data sharing agreement is a formal agreement or contract between data providers and users that outlines the terms and conditions of data sharing and use.
      data science:
        description: An interdisciplinary field that combines aspects of computer science, statistics, and other disciplines to analyze and interpret complex data sets.
      database:
        description: An organized collection of data, usually stored electronically in a computer system and designed to be easily accessed, managed, and updated.
      python:
        description: A high-level programming language that is widely used in scientific computing and data analysis.
      workflow:
        description: A set of interrelated tasks that are performed in a specific order to accomplish a particular goal.
      research data:
        description: The digital or physical data that is collected, observed, or created in the course of conducting research.
      academic libraries:
        description: Libraries located within academic institutions, typically with specialized collections and services that support the research and educational needs of the institution's faculty, staff, and students.
      citizen science:
        description: A form of collaboration in which members of the public participate in scientific research projects, often by collecting or analyzing data.
      repositories:
        description: Online platforms or databases that allow for the storage, organization, and sharing of research outputs such as publications, data, and software.

        #