Sharing FAIR Research Objects
to improve reproducibility

 

Stian Soiland-Reyes

eScience lab, The University of Manchester

INDElab, University of Amsterdam

 ZB-Med Seminar
2021-07-15

H2020-INFRAEOSC-2018-2 824087

H2020-INFRAEDI-2018-1 823830

H2020-INFRAIA-2017-1 730976

H2020-INFRADEV-2019-2 871118

H2020-INFRAIA-2018-1 823827

Findable

Accessible

Interoperable

Reusable

To be Findable:

F1. (meta)data are assigned a globally unique and persistent identifier

F2. data are described with rich metadata (defined by R1 below)

F3. metadata clearly and explicitly include the identifier of the data it describes

F4. (meta)data are registered or indexed in a searchable resource

 

To be Findable:

F1. (meta)data are assigned a globally unique and persistent identifier

F2. data are described with rich metadata (defined by R1 below)

F3. metadata clearly and explicitly include the identifier of the data it describes

F4. (meta)data are registered or indexed in a searchable resource

 

To be Accessible:

A1. (meta)data are retrievable by their identifier using a standardized communications protocol

A1.1 the protocol is open, free, and universally implementable

A1.2 the protocol allows for an authentication and authorization procedure, where necessary

A2. metadata are accessible, even when the data are no longer available

To be Findable:

F1. (meta)data are assigned a globally unique and persistent identifier

F2. data are described with rich metadata (defined by R1 below)

F3. metadata clearly and explicitly include the identifier of the data it describes

F4. (meta)data are registered or indexed in a searchable resource

 

To be Accessible:

A1. (meta)data are retrievable by their identifier using a standardized communications protocol

A1.1 the protocol is open, free, and universally implementable

A1.2 the protocol allows for an authentication and authorization procedure, where necessary

A2. metadata are accessible, even when the data are no longer available

To be Interoperable:

I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.

I2. (meta)data use vocabularies that follow FAIR principles

I3. (meta)data include qualified references to other (meta)data

 

To be Findable:

F1. (meta)data are assigned a globally unique and persistent identifier

F2. data are described with rich metadata (defined by R1 below)

F3. metadata clearly and explicitly include the identifier of the data it describes

F4. (meta)data are registered or indexed in a searchable resource

 

To be Accessible:

A1. (meta)data are retrievable by their identifier using a standardized communications protocol

A1.1 the protocol is open, free, and universally implementable

A1.2 the protocol allows for an authentication and authorization procedure, where necessary

A2. metadata are accessible, even when the data are no longer available

To be Interoperable:

I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.

I2. (meta)data use vocabularies that follow FAIR principles

I3. (meta)data include qualified references to other (meta)data

 

To be Reusable:

R1. meta(data) are richly described with a plurality of accurate and relevant attributes

R1.1. (meta)data are released with a clear and accessible data usage license

R1.2. (meta)data are associated with detailed provenance

R1.3. (meta)data meet domain-relevant community standards

To be Findable:

F1. (meta)data are assigned a globally unique and persistent identifier

F2. data are described with rich metadata (defined by R1 below)

F3. metadata clearly and explicitly include the identifier of the data it describes

F4. (meta)data are registered or indexed in a searchable resource

 

To be Accessible:

A1. (meta)data are retrievable by their identifier using a standardized communications protocol

A1.1 the protocol is open, free, and universally implementable

A1.2 the protocol allows for an authentication and authorization procedure, where necessary

A2. metadata are accessible, even when the data are no longer available

To be Interoperable:

I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.

I2. (meta)data use vocabularies that follow FAIR principles

I3. (meta)data include qualified references to other (meta)data

 

To be Reusable:

R1. meta(data) are richly described with a plurality of accurate and relevant attributes

R1.1. (meta)data are released with a clear and accessible data usage license

R1.2. (meta)data are associated with detailed provenance

R1.3. (meta)data meet domain-relevant community standards

 

https://doi.org/10.1038/d41586-019-01307-2

They ride with what I refer to as the four horsemen of the reproducibility apocalypse:
  1. Publication bias
  2. Low statistical power
  3. P-value hacking
  4. HARKing (hypothesizing after results are known)

 

 

Reproducibility?

Workflows

Automation

– Automate computational aspects
– Repetitive pipelines, sweep campaigns

Scalingcompute cycles

– Make use of computational infrastructure
– Handle large data

Abstraction⁠—people cycles

– Shield complexity and incompatibilities
– Report, re-usue, evolve, share, compare
– Repeat—Tweak—Repeat
– First-class commodities

Provenancereporting

– Capture, report and utilize log and data lineage
– Auto-documentation
– Tracable evolution, audit, transparency
– Reproducible science

Findable

Accessible

Interoperable

Reusable

(Reproducible)

Why use workflows?

cwlVersion: v1.0
class: Workflow
inputs:
  inp: File
  ex: string

outputs:
  classout:
    type: File
    outputSource: compile/classfile

steps:
  untar:
    run: tar-param.cwl
    in:
      tarfile: inp
      extractfile: ex
    out: [example_out]

  compile:
    run: arguments.cwl
    in:
      src: untar/example_out
    out: [classfile]

Nature 573, 149-150 (2019)
https://doi.org/10.1038/d41586-019-02619-z

cwlVersion: v1.0
class: Workflow

inputs:
  toConvert: File


outputs:
  converted: 
    type: File
    outputSource: convertMethylation/converted
  combined: 
    type: File
    outputSource: mergeSymmetric/combined


steps:
  convertMethylation:
    run: interconverter.cwl
    in:
      toConvert: toConvert
    out: [converted]
  mergeSymmetric:
    run: symmetriccpgs.cwl
    in:
      toCombine: convertMethylation/converted
    out: [combined]
cwlVersion: v1.0
class: CommandLineTool
inputs:
  toConvert:
    type: File
    inputBinding:
      prefix: -i
outputs:
  converted:
    type: File
    outputBinding:
      glob: "*.meth"
baseCommand: interconverter.sh
arguments: ["-d", $(runtime.outdir)]

hints:
  - class: DockerRequirement
    dockerPull: "quay.io/neksa/screw-tool"
cwlVersion: v1.0
class: CommandLineTool
inputs:
  toCombine:
    type: File
    inputBinding:
      prefix: -i
outputs:
  combined:
    type: File
    outputBinding:
      glob: "*.sym"

baseCommand: symmetriccpgs.sh
arguments: ["-d", $(runtime.outdir)]

hints:
  - class: DockerRequirement
    dockerPull: "quay.io/neksa/screw-tool"

What is RO-Crate?

RO-Crate is method for self-decribed datasets as a digital object using a single Linked Data metadata document

Credit: Peter Sefton
Adapted from https://arkisto-platform.github.io/standards/ro-crate/

The dataset may contain any kind of
resource, about anything, in any format
as a file, URL or PID

Credit: Peter Sefton
Adapted from https://arkisto-platform.github.io/standards/ro-crate/

Each resource have a machine readable description in JSON-LD format

Credit: Peter Sefton
Adapted from https://arkisto-platform.github.io/standards/ro-crate/

A human-readable description/preview in an HTML file that lives alongside the metadata

Credit: Peter Sefton
Adapted from https://arkisto-platform.github.io/standards/ro-crate/

Provenance and workflow information can be included
– to assist in re-use of data and research processes

Credit: Peter Sefton
Adapted from https://arkisto-platform.github.io/standards/ro-crate/

RO-Crate Digital Objects may be packaged for distribution eg via Zip, Bagit and OCFL
– or simply be published on the Web

Credit: Peter Sefton
Adapted from https://arkisto-platform.github.io/standards/ro-crate/

Techie deep-dive!

Warning: JSON ahead

RO-Crate for data scientists

Credit: Marco La Rosa, Peter Sefton

Making your own RO-Crate with Describo

FAIR is not just machine-readable!

RO-Crate for digital humanities

Capturing cultural heritage records
as RO-Crates

RO-Crate for repositories

RO-Crate as a
metadata archival format

Metadata held alongside hetereogeneous data

Exchange mechanism (import/export)

Avoid vendor lock-in

RO-Crate for
workflow descriptions

Describing workflows
with RO-Crate

Containers

Describe workflow

Tests

Registry

Workflows

Authors and contributors

Credit: José Mª Fernández, ELIXIR All Hands, 2021-06-11

RO-Crate for
computational tools

Making Canonical Workflow Building Blocks interoperable across workflow languages

RO-Crate for
regulatory sciences

IEEE2791-2020

Alternate metadata views

Domain-specific explanation: BCO

General index: RO-Crate

ro-crate-metadata.json
{
  "@context": [
    "https://w3id.org/ro/crate/1.0/context",
    {
      "@vocab": "https://schema.org/"
    }
  ],
  "@graph": [
    {
      "@id": "ro-crate-metadata.json",
      "@type": "CreativeWork",
      "about": {
        "@id": "./"
      },
      "identifier": "ro-crate-metadata.json",
      "conformsTo": {
        "@id": "https://w3id.org/ro/crate/1.0"
      },
      "license": {
        "@id": "https://creativecommons.org/licenses/by-sa/3.0"
      },
      "description": "Made with Describo: https://uts-eresearch.github.io/describo/"
    },
    {
      "@type": "Dataset",
      "author": {
        "@id": "https://orcid.org/0000-0001-9842-9718"
      },
      "citation": {
        "@id": "https://doi.org/10.5281/zenodo.3966161"
      },
      "contactPoint": {
        "@id": "https://github.com/biocompute-objects/bco-ro-example-chipseq/issues"
      },
      "datePublished": "2020-09-09T23:00:00.000Z",
      "description": "Workflow run of a ChIP-seq peak-calling, QC and differential analysis pipeline",
      "distribution": {
        "@id": "https://github.com/biocompute-objects/bco-ro-example-chipseq/archive/main.zip"
      },
      "hasPart": [
        {
          "@id": "https://raw.githubusercontent.com/nf-core/chipseq/1.2.1/main.nf"
        },
        {
          "@id": "chipseq_20200910.json"
        },
        {
          "@id": "results/"
        },
        {
          "@id": "nextflow.log"
        },
        {
          "@id": ".nextflow.log"
        }
      ],
      "license": {
        "@id": "https://spdx.org/licenses/CC0-1.0"
      },
      "name": "Workflow run of nf-core/chipseq",
      "publisher": {
        "@id": "https://biocomputeobject.org/"
      },
      "@id": "./"
    },
    {
      "@type": "File",
      "dateModified": "2020-09-10T13:10:50.246Z",
      "name": ".nextflow.log",
      "@reverse": {
        "hasPart": [
          {
            "@id": "./"
          }
        ]
      },
      "@id": ".nextflow.log"
    },
    {
      "@type": "File",
      "conformsTo": {
        "@id": "https://w3id.org/ieee/ieee-2791-schema/"
      },
      "dateModified": "2020-09-10T13:50:02.378Z",
      "identifier": {
        "@id": "urn:uuid:dc308d7c-7949-446a-9c39-511b8ab40caf"
      },
      "license": {
        "@id": "https://spdx.org/licenses/CC0-1.0"
      },
      "name": "chipseq_20200910.json",
      "description": "IEEE 2791 description",
      "@reverse": {
        "hasPart": [
          {
            "@id": "./"
          }
        ]
      },
      "@id": "chipseq_20200910.json"
    },
    {
      "@type": "Organization",
      "description": " Two non-overlapping entities work in parallel to help drive BioCompute, the IEEE 2791-2020 Standard, and a Public Private Partnership. Leadership for the Public Private Partnership consists of an Executive Steering Committee and a Technical Steering Committee. The schema that is referenced by the current draft of the IEEE standard is maintained by an IEEE GitLab repository. ",
      "name": "BioCompute Objects",
      "@reverse": {
        "publisher": [
          {
            "@id": "./"
          }
        ]
      },
      "@id": "https://biocomputeobject.org/"
    },
    {
      "@type": "ScholarlyArticle",
      "name": "nf-core/chipseq: nf-core/chipseq v1.2.1 - Platinum Mole",
      "@reverse": {
        "citation": [
          {
            "@id": "./"
          },
          {
            "@id": "https://raw.githubusercontent.com/nf-core/chipseq/1.2.1/main.nf"
          }
        ]
      },
      "@id": "https://doi.org/10.5281/zenodo.3966161"
    },
    {
      "@type": "CreativeWork",
      "identifier": "https://spdx.org/licenses/MIT",
      "name": "MIT License",
      "@reverse": {
        "license": [
          {
            "@id": "https://raw.githubusercontent.com/nf-core/chipseq/1.2.1/main.nf"
          }
        ]
      },
      "@id": "https://github.com/nf-core/chipseq/blob/1.2.1/LICENSE"
    },
    {
      "@type": "CreativeWork",
      "description": "\nMIT License\n\nCopyright (c) 2018 nf-core\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.",
      "name": "MIT License",
      "@reverse": {
        "license": [
          {
            "@id": "results/"
          }
        ]
      },
      "@id": "https://github.com/nf-core/test-datasets/blob/atacseq/LICENSE"
    },
    {
      "@type": "DataDownload",
      "path": "https://github.com/biocompute-objects/bco-ro-example-chipseq/archive/main.zip",
      "license": {
        "@id": "https://spdx.org/licenses/CC0-1.0"
      },
      "name": "GitHub download of biocompute-objects/bco-ro-example-chipseq",
      "@reverse": {
        "distribution": [
          {
            "@id": "./"
          }
        ]
      },
      "@id": "https://github.com/biocompute-objects/bco-ro-example-chipseq/archive/main.zip"
    },
    {
      "@type": "ContactPoint",
      "name": " bco-ro-example-chipseq GitHub issue tracker",
      "url": "https://github.com/biocompute-objects/bco-ro-example-chipseq/issues",
      "@reverse": {
        "contactPoint": [
          {
            "@id": "./"
          }
        ]
      },
      "@id": "https://github.com/biocompute-objects/bco-ro-example-chipseq/issues"
    },
    {
      "@type": "Person",
      "name": "Stian Soiland-Reyes",
      "@reverse": {
        "author": [
          {
            "@id": "./"
          },
          {
            "@id": "results/"
          }
        ]
      },
      "@id": "https://orcid.org/0000-0001-9842-9718"
    },
    {
      "@type": [
        "ComputationalWorkflow",
        "File"
      ],
      "author": [
        {
          "@id": "#714de175-aa77-47f1-9f99-6a4fba65530a"
        },
        {
          "@id": "#bfb876e7-e767-4209-ad66-e1e1379c249f"
        },
        {
          "@id": "#0164006f-bd58-4ebc-9a50-b8bd4ac3025c"
        },
        {
          "@id": "#556c747c-376a-4a85-82a1-9b99520d24fd"
        },
        {
          "@id": "#93c23523-03b5-41dc-be4c-6a9a2e0e221d"
        },
        {
          "@id": "#781b9b5a-dc06-4709-8f14-65ee08b8c543"
        },
        {
          "@id": "#f652b13e-0ba2-4394-a990-7304f54c7b9a"
        },
        {
          "@id": "#a58abf42-751d-49bd-a477-1d5065ac70c6"
        },
        {
          "@id": "#e11af59b-8e24-4cc8-8f5e-cef411ab0823"
        },
        {
          "@id": "#f262954b-a218-480d-8a01-0e0b1ca20ffc"
        },
        {
          "@id": "#64bd387d-60ad-4df8-804e-1f6b9ea72de5"
        }
      ],
      "citation": {
        "@id": "https://doi.org/10.5281/zenodo.3966161"
      },
      "description": "nfcore/chipseq is a bioinformatics analysis pipeline used for Chromatin ImmunopreciPitation sequencing (ChIP-seq) data",
      "license": {
        "@id": "https://github.com/nf-core/chipseq/blob/1.2.1/LICENSE"
      },
      "name": "nf-core/chipseq",
      "@reverse": {
        "hasPart": [
          {
            "@id": "./"
          }
        ],
        "about": [
          {
            "@id": "results/pipeline_info/pipeline_dag.svg"
          },
          {
            "@id": "#fcb32545-04bd-474d-9b6e-0fb7321c38b4"
          }
        ]
      },
      "@id": "https://raw.githubusercontent.com/nf-core/chipseq/1.2.1/main.nf"
    },
    {
      "@type": "File",
      "creator": {
        "@id": "#db65dfb7-4867-400e-a12f-a1652d46a333"
      },
      "dateModified": "2020-09-10T13:10:50.250Z",
      "name": "nextflow.log",
      "@reverse": {
        "hasPart": [
          {
            "@id": "./"
          }
        ]
      },
      "@id": "nextflow.log"
    },
    {
      "@type": "Dataset",
      "author": {
        "@id": "https://orcid.org/0000-0001-9842-9718"
      },
      "creator": {
        "@id": "#db65dfb7-4867-400e-a12f-a1652d46a333"
      },
      "dateModified": "2020-09-10T13:20:49.143Z",
      "description": "Nextflow outputs from examplar run of nf-core/ pipeline workflow.",
      "hasPart": [
        {
          "@id": "results/bwa/"
        },
        {
          "@id": "results/fastqc/"
        },
        {
          "@id": "results/genome/"
        },
        {
          "@id": "results/igv/"
        },
        {
          "@id": "results/multiqc/"
        },
        {
          "@id": "results/pipeline_info/"
        },
        {
          "@id": "results/trim_galore/"
        }
      ],
      "license": {
        "@id": "https://github.com/nf-core/test-datasets/blob/atacseq/LICENSE"
      },
      "name": "results",
      "@reverse": {
        "hasPart": [
          {
            "@id": "./"
          }
        ]
      },
      "@id": "results/"
    },
    {
      "@type": "Dataset",
      "dateModified": "2020-09-10T12:00:09.238Z",
      "hasPart": {
        "@id": "results/bwa/mergedLibrary/"
      },
      "name": "bwa",
      "@reverse": {
        "hasPart": [
          {
            "@id": "results/"
          }
        ]
      },
      "@id": "results/bwa/"
    },
    {
      "@type": "Dataset",
      "dateModified": "2020-09-10T12:02:59.495Z",
      "hasPart": [
        {
          "@id": "results/bwa/mergedLibrary/bigwig/"
        },
        {
          "@id": "results/bwa/mergedLibrary/deepTools/"
        },
        {
          "@id": "results/bwa/mergedLibrary/macs/"
        },
        {
          "@id": "results/bwa/mergedLibrary/phantompeakqualtools/"
        },
        {
          "@id": "results/bwa/mergedLibrary/picard_metrics/"
        }
      ],
      "name": "mergedLibrary",
      "@reverse": {
        "hasPart": [
          {
            "@id": "results/bwa/"
          }
        ]
      },
      "@id": "results/bwa/mergedLibrary/"
    },
    {
      "@type": "Dataset",
      "dateModified": "2020-09-10T12:04:31.692Z",
      "hasPart": {
        "@id": "results/bwa/mergedLibrary/bigwig/scale/"
      },
      "name": "bigwig",
      "@reverse": {
        "hasPart": [
          {
            "@id": "results/bwa/mergedLibrary/"
          }
        ]
      },
      "@id": "results/bwa/mergedLibrary/bigwig/"
    },
    {
      "@type": "Dataset",
      "dateModified": "2020-09-10T12:04:31.696Z",
      "name": "scale",
      "@reverse": {
        "hasPart": [
          {
            "@id": "results/bwa/mergedLibrary/bigwig/"
          }
        ]
      },
      "@id": "results/bwa/mergedLibrary/bigwig/scale/"
    },
    {
      "@type": "Dataset",
      "dateModified": "2020-09-10T12:11:43.943Z",
      "hasPart": [
        {
          "@id": "results/bwa/mergedLibrary/deepTools/plotFingerprint/"
        },
        {
          "@id": "results/bwa/mergedLibrary/deepTools/plotProfile/"
        }
      ],
      "name": "deepTools",
      "@reverse": {
        "hasPart": [
          {
            "@id": "results/bwa/mergedLibrary/"
          }
        ]
      },
      "@id": "results/bwa/mergedLibrary/deepTools/"
    },
    {
      "@type": "Dataset",
      "dateModified": "2020-09-10T12:05:17.700Z",
      "name": "plotFingerprint",
      "@reverse": {
        "hasPart": [
          {
            "@id": "results/bwa/mergedLibrary/deepTools/"
          }
        ]
      },
      "@id": "results/bwa/mergedLibrary/deepTools/plotFingerprint/"
    },
    {
      "@type": "Dataset",
      "dateModified": "2020-09-10T12:26:12.375Z",
      "name": "plotProfile",
      "@reverse": {
        "hasPart": [
          {
            "@id": "results/bwa/mergedLibrary/deepTools/"
          }
        ]
      },
      "@id": "results/bwa/mergedLibrary/deepTools/plotProfile/"
    },
    {
      "@type": "Dataset",
      "dateModified": "2020-09-10T12:02:33.471Z",
      "name": "macs",
      "@reverse": {
        "hasPart": [
          {
            "@id": "results/bwa/mergedLibrary/"
          }
        ]
      },
      "@id": "results/bwa/mergedLibrary/macs/"
    },
    {
      "@type": "Dataset",
      "dateModified": "2020-09-10T12:04:26.336Z",
      "name": "phantompeakqualtools",
      "@reverse": {
        "hasPart": [
          {
            "@id": "results/bwa/mergedLibrary/"
          }
        ]
      },
      "@id": "results/bwa/mergedLibrary/phantompeakqualtools/"
    },
    {
      "@type": "Dataset",
      "dateModified": "2020-09-10T12:04:45.952Z",
      "name": "picard_metrics",
      "@reverse": {
        "hasPart": [
          {
            "@id": "results/bwa/mergedLibrary/"
          }
        ]
      },
      "@id": "results/bwa/mergedLibrary/picard_metrics/"
    },
    {
      "@type": "Dataset",
      "dateModified": "2020-09-10T11:58:56.905Z",
      "hasPart": {
        "@id": "results/fastqc/zips/"
      },
      "name": "fastqc",
      "@reverse": {
        "hasPart": [
          {
            "@id": "results/"
          }
        ]
      },
      "@id": "results/fastqc/"
    },
    {
      "@type": "Dataset",
      "dateModified": "2020-09-10T11:58:56.909Z",
      "name": "zips",
      "@reverse": {
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}

RO-Crate for enabling a
large number of
data citations

Credit: Paolo Manghi
AGU Data Citation Workshop

https://doi.org/10.5281/zenodo.4916734

RO-Crate as aggregation:
data citation reliquary

4-dimensional RO-Crates?

Credit: Oscar Corcho, Carole Goble
https://doi.org/10.5281/zenodo.4913285

RO-Crate profiles

Credit: Carole Goble
Dataverse Community Meeting 2021

https://www.slideshare.net/carolegoble/

FAIR Digital Objects

…with RO-Crate as metadata object

RO-Crate as FAIR Digital Object (FDO)

+ FAIR Signposting

Credit:

Herbert van de Sompel
FAIR Signposting: A KISS Approach to a Burning Issue

https://www.slideshare.net/hvdsomp/

FAIR Signposting

Credit:

Herbert van de Sompel
FAIR Signposting: A KISS Approach to a Burning Issue

https://www.slideshare.net/hvdsomp/

FAIR Signposting

HEAD https://workflowhub.eu/workflows/29?version=2

200 OK
Link: <https://doi.org/10.48546/workflowhub.workflow.29.2>;rel=cite-as
Link: <https://workflowhub.eu/workflows/29/ro_crate?version=2>;rel=describedby
Link: <https://orcid.org/0000-0003-0513-0288>;rel=author
…


 

Resolving an RO-Crate with FAIR Signposting

rel=author
rel=type
ComputationalWorkflow
rel=license
rel=item
rel=describedby;
type="application/ld+json;profile=https://w3id.org/ro/crate"
rel=cite-as
rel=item;
type="application/zip"

Self-publishing RO-Crates

Using Zenodo and GitHub Pages

Use case: Rescuing and FAIRifying old research outputs

Using Describo

to make an RO-Crate

 

RO-Crate Community

The RO-Crate Community is open for anyone to join us!

https://www.researchobject.org/ro-crate/community

2021-07-15 Sharing FAIR Research Objects to improve reproducibility

By Stian Soiland-Reyes

2021-07-15 Sharing FAIR Research Objects to improve reproducibility

Presented 2021-07-02 at joint meeting of The Canonical Workflow Frameworks for Research (CWFR) and Semantics (SEM) working groups of the FAIR Digital Object Forum initiative.

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