De novo digitisation
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Run on GalaxyAn example workflow to allow users to run the Specimen Data Refinery tools on data provided in an input CSV file.
Inputs
| ID | Name | Description | Type |
|---|---|---|---|
| DLA Region - Training Model | DLA Region - Training Model | n/a | n/a |
| DLA Text - Training Model | DLA Text - Training Model | n/a | n/a |
Steps
| ID | Name | Description |
|---|---|---|
| 0 | DLA Region - Training Model | |
| 1 | DLA Text - Training Model | |
| 2 | Input dataset | |
| 3 | Remove header | Remove beginning1 |
| 4 | Split file | toolshed.g2.bx.psu.edu/repos/bgruening/split_file_to_collection/split_file_to_collection/0.5.0 |
| 5 | Create specimen data object from csv | sdr_create_sdo_from_csv |
| 6 | Filter failed datasets | __FILTER_FAILED_DATASETS__ |
| 7 | Extract file name | toolshed.g2.bx.psu.edu/repos/iuc/jq/jq/1.0 |
| 8 | DLA - detect regions | sdr_teklia_worker_dla |
| 9 | DLA - detect text lines | sdr_teklia_worker_dla |
| 10 | SDR Teklia worker-htr | sdr_teklia_worker_htr |
| 11 | SDR Teklia worker-ner | sdr_teklia_worker_ner |
| 12 | Bundle Collection | toolshed.g2.bx.psu.edu/repos/nml/bundle_collections/bundle_collection/1.3.0 |
Outputs
| ID | Name | Description | Type |
|---|---|---|---|
| out_file1 | out_file1 | n/a | |
| list_output_txt | list_output_txt | n/a | |
| output | output | n/a | |
| output | output | n/a | |
| output | output | n/a | |
| output | output | n/a | |
| output | output | n/a | |
| output | output | n/a | |
| output | output | n/a | |
| html_file | html_file | n/a |
Version History
Version 2 (latest) Created 14th Dec 2022 at 16:05 by Oliver Woolland
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master916e4cb
Version 1 (earliest) Created 8th Jul 2022 at 14:00 by Oliver Woolland
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Creators and Submitter
Views: 3834 Downloads: 383 Runs: 0
Created: 8th Jul 2022 at 14:00
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Related items
Teams: Specimen Data Refinery
Organizations: The University of Manchester
Teams: Specimen Data Refinery
Organizations: The Natural History Museum London
https://orcid.org/0000-0002-7341-1842
Teams: Specimen Data Refinery, Air Quality Prediction
Organizations: The University of Manchester
The Distributed System of Scientific Collections is a new world-class Research Infrastructure (RI) for Natural Science Collections. The DiSSCo RI aims to create a new business model for one European collection that digitally unifies all European natural science assets under common access, curation, policies and practices that ensure that all the data is easily Findable, Accessible, Interoperable and Reusable (FAIR principles).
DiSSCo represents the largest ever formal agreement between natural ...
Teams: Specimen Data Refinery
Web page: https://www.dissco.eu/
The SDR is concerned with digitisation pipelines for digital access to natural history collections
The SDR integrate machine learning, Artificial Intelligence, and human approaches to extract, enhance, and annotate data from digital images and records at scale. Many collections-holding institutions still need to digitise the bulk of their collections. Digitisation takes time and resources. One of the major challenges in digitising massive collections is finding ways of ensuring high-quality ...
Space: DISSCo - Distributed System of Scientific Collections
Public web page: https://www.synthesys.info/
Start date: 1st Feb 2019
End date: 31st Dec 2023
An example input file for the Specimen Data Refinery workflow
Creators: None
Submitter: Oliver Woolland
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