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Repository for the MIDSCalculator app, written mostly by @LynnDelgat during an internship at Meise Botanic Garden.

The latest self-contained windows installer (generated using Inno Setup) can be found here: https://drive.google.com/drive/folders/1ioRhHIvdYI88yoPTsYLG_k5-8n-05CiP

## Towards the MIDS data standard: developing a Shiny app to calculate digitization levels of natural history specimens
## Minimum Information about a Digital Specimen

Minimum Information about a Digital Specimen (MIDS) is a data standard which aims to describe four different levels of digitization status for biological and geological specimens from natural history collections, each level requiring certain information elements (referred to as MIDS elements from here on) to be digitally available. With this standard, users of collection data, as well as funders and collection curators will have a better overview of how comprehensively digitized a collection or any other set of specimens really is. The standard is currently still under development by a Task Group of the global Biodiversity Information Standards (“TDWG”) organization. While MIDS elements for digitization levels 0 and 1 have been specified, MIDS elements for levels 2 and 3 are still under discussion.
For more details: https://github.com/tdwg/mids


## MIDSCalculator App
### Introduction
The MIDSCalculator is a Shiny app which allows users to calculate MIDS scores for each record in a submitted dataset, and explore the results.

Minimum Information about a Digital Specimen (MIDS) is a data standard which aims to describe four different levels of digitization status for biological and geological specimens from natural history collections, based on the availability of data in digital records of these specimens. With this standard, users of collection data, as well as funders and collection curators will have a better overview of how comprehensively digitized a collection or any other set of specimens really is. The standard is currently still under development by a Task Group of the global Biodiversity Information Standards (“TDWG”) organization. While properties for the different levels have been proposed, no definite implementation of MIDS has been made yet.
#### Mapping MIDS elements: use of a JSON schema

In this internship, a Shiny app is constructed which allows users to calculate MIDS scores for each record in a submitted dataset. We focus on the commonly used Darwin Core Archive as a data standard for the sourced data. As the MIDS standard is agnostic to the data model of the sourced data, the app includes a JSON schema mapping the sourced data to the different MIDS properties. This schema is editable through the app’s interface and may facilitate testing variations in the implementation of MIDS itself, aiding further development of the standard.
As the MIDS standard is agnostic to the data model of the sourced data, the app uses a JSON schema to map MIDS elements to properties of the sourced data. A schema mapping to GBIF annotated DwC data is included in the app. Other JSON schemas can be uploaded. In addition the schema is editable through the app’s interface.

### Installation

The latest self-contained windows installer (generated using Inno Setup) can be found here: https://drive.google.com/drive/folders/1ioRhHIvdYI88yoPTsYLG_k5-8n-05CiP

## About the App
### Submit data

On this page a (zipped) GBIF annotated Darwin Core Archive can be uploaded. In addition, the MIDS implementation can be specified and viewed. To specify the MIDS implementation you can either choose the default schema (included in the app) or upload your own file. It is also possible to choose to edit this schema interactively. The interactive editing opens in a pop-up window, where in a first tab, MIDS elements can be added, removed, or moved to another MIDS level. In addition, mappings can be removed or added by clicking the “edit” icon of a MIDS element. In a second tab, the Unknown or Missing section of the schema can be edited, i.e. new properties and new values can be added. This interactively edited schema can be saved to file (JSON). The schema (be it default, custom or interactive) can be viewed by clicking “Show MIDS implementation”, which opens a human-friendly visualization of the MIDS schema, so that it is not necessary to read the JSON file to be able to understand the specifics of the MIDS schema used. Once a dataset and a MIDS implementation have been chosen, calculations can be started by clicking “Start MIDS score calculations”.
On this page a zipped GBIF annotated Darwin Core Archive or a comma or tab separated occurrence file can be uploaded (max 5GB). In addition, the MIDS implementation can be specified and viewed. To specify the MIDS implementation you can either choose the default schema (included in the app) or upload a schema from file. It is also possible to choose to edit this schema interactively. The interactive editing opens in a pop-up window, where in a first tab, MIDS elements can be added, removed, or moved to another MIDS level. In addition, mappings can be removed or added by clicking the "edit" icon of a MIDS element. In a second tab, the Unknown or Missing section of the schema can be edited, i.e. new properties and new values can be added. This interactively edited schema can be saved to file (JSON). The schema (be it default, custom or interactive) can be viewed by clicking the eye icon, which opens a human-friendly visualization of the MIDS schema, so that it is not necessary to read the JSON file to be able to understand the specifics of the MIDS schema used. Once a dataset and a MIDS implementation have been chosen, calculations can be started by clicking "Start MIDS score calculations".

### Results

The results of each analysis are visualized on a new page, where it is possible to explore summaries of the results of both MIDS levels and MIDS elements, either as plots or as tables. It is also possible to explore the complete records table with the MIDS results for each record, and to download it as a csv file. In addition, the data can be filtered to see how MIDS results change when filtering on properties such as country code /taxonomic group/ collection date. The filename of the dataset is shown, as well as the used MIDS implementation, to make the provenance of the calculations clear.
The results of each analysis are visualized on a new page, where it is possible to explore summaries of the results of both MIDS levels and MIDS elements, either as plots or as tables. The MIDS element plot can be clicked to get more details on the results of the mappings of that element. It is also possible to explore the complete records table with the MIDS results for each record, and to download it as a csv file. In addition, the data can be filtered to see how MIDS results change when filtering on properties such as country code /taxonomic group/ collection date. The filename of the dataset is shown, as well as the used MIDS implementation, to make the provenance of the calculations clear.'



## Code
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* GBIF Occurrence Download [10.15468/dl.e8jnan](http://doi.org/10.15468/dl.e8jnan) can be found as a zip file in the data folder for quick testing.

### Schemas
* [fourthschema.json](https://github.com/AgentschapPlantentuinMeise/MIDSCalculator/blob/main/data/schemas/fourthschema.json): This schema is based on the most recent MIDS specification for levels 0 and 1. As levels 2 and 3 are still under discussion, the Schema offers a basic interpretation of several potential properties.
* [DwC-GBIF_schema.json](https://github.com/AgentschapPlantentuinMeise/MIDSCalculator/blob/main/data/schemas/DwC-GBIF_schema.json): This schema is based on the most recent MIDS specification for levels 0 and 1. As levels 2 and 3 are still under discussion, the schema offers a basic interpretation of several potential properties.

## Session info
Code was run with the following packages and versions:
Expand All @@ -70,29 +81,31 @@ Running under: Windows 10 x64 (build 19044)
Matrix products: default
locale:
[1] LC_COLLATE=English_Belgium.1252 LC_CTYPE=English_Belgium.1252 LC_MONETARY=English_Belgium.1252
[4] LC_NUMERIC=C LC_TIME=English_Belgium.1252
[1] LC_COLLATE=English_Belgium.1252 LC_CTYPE=English_Belgium.1252
[3] LC_MONETARY=English_Belgium.1252 LC_NUMERIC=C
[5] LC_TIME=English_Belgium.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] magrittr_2.0.1 purrr_0.3.4 data.table_1.14.2 dplyr_1.0.7 jsonlite_1.7.3
[6] RColorBrewer_1.1-2 sortable_0.4.5 shinyjs_2.1.0 DT_0.21 ggplot2_3.3.5
[11] shinyBS_0.61.1 shiny_1.7.1
[1] shinybusy_0.3.1 RColorBrewer_1.1-3 jsonlite_1.8.0 magrittr_2.0.1 purrr_0.3.4
[6] data.table_1.14.2 dplyr_1.0.8 sortable_0.4.5 shinyjs_2.1.0 DT_0.23
[11] ggplot2_3.3.5 shinyBS_0.61.1 shiny_1.7.2
loaded via a namespace (and not attached):
[1] bslib_0.3.1 tidyselect_1.1.1 xfun_0.29 learnr_0.10.1 colorspace_2.0-2
[6] vctrs_0.3.8 generics_0.1.1 htmltools_0.5.2 yaml_2.2.1 utf8_1.2.2
[11] XML_3.99-0.8 rlang_0.4.12 jquerylib_0.1.4 later_1.3.0 pillar_1.6.4
[16] glue_1.6.0 withr_2.4.3 DBI_1.1.2 lifecycle_1.0.1 stringr_1.4.0
[21] munsell_0.5.0 gtable_0.3.0 fontawesome_0.2.2 htmlwidgets_1.5.4 evaluate_0.14
[26] labeling_0.4.2 knitr_1.37 fastmap_1.1.0 crosstalk_1.2.0 httpuv_1.6.5
[31] markdown_1.1 fansi_1.0.2 Rcpp_1.0.8 xtable_1.8-4 promises_1.2.0.1
[36] scales_1.1.1 cachem_1.0.6 farver_2.1.0 mime_0.12 digest_0.6.29
[41] stringi_1.7.6 grid_4.0.3 rprojroot_2.0.2 tools_4.0.3 sass_0.4.0
[46] tibble_3.1.6 crayon_1.4.2 pkgconfig_2.0.3 ellipsis_0.3.2 rsconnect_0.8.25
[51] xml2_1.3.3 assertthat_0.2.1 rmarkdown_2.11 R6_2.5.1 compiler_4.0.3
[1] Rcpp_1.0.8.3 assertthat_0.2.1 rprojroot_2.0.3 digest_0.6.29 utf8_1.2.2
[6] mime_0.12 R6_2.5.1 evaluate_0.15 pillar_1.6.4 rlang_1.0.2
[11] rstudioapi_0.13 fontawesome_0.3.0 jquerylib_0.1.4 learnr_0.10.1 rmarkdown_2.14
[16] labeling_0.4.2 stringr_1.4.0 htmlwidgets_1.5.4 munsell_0.5.0 compiler_4.0.3
[21] httpuv_1.6.5 xfun_0.29 pkgconfig_2.0.3 htmltools_0.5.2 sourcetools_0.1.7
[26] tidyselect_1.1.2 tibble_3.1.6 fansi_1.0.3 crayon_1.5.1 withr_2.5.0
[31] later_1.3.0 grid_4.0.3 xtable_1.8-4 gtable_0.3.0 lifecycle_1.0.1
[36] DBI_1.1.3 scales_1.2.0 cli_3.2.0 stringi_1.7.6 cachem_1.0.6
[41] farver_2.1.0 promises_1.2.0.1 bslib_0.4.0 ellipsis_0.3.2 generics_0.1.3
[46] vctrs_0.4.1 tools_4.0.3 glue_1.6.0 markdown_1.1 crosstalk_1.2.0
[51] rsconnect_0.8.27 fastmap_1.1.0 yaml_2.2.1 colorspace_2.0-3 memoise_2.0.1
[56] knitr_1.39 sass_0.4.1
```

## Installer creation using RInno
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4 changes: 2 additions & 2 deletions src/Shiny_MIDS/MIDScalcApp.R
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Expand Up @@ -98,9 +98,9 @@ server <- function(input, output, session) {
showModal(modalDialog(
title = "About",
HTML(paste0(h4('Submit data'), '
On this page a (zipped) GBIF annotated Darwin Core Archive can be uploaded. In addition, the MIDS implementation can be specified and viewed. To specify the MIDS implementation you can either choose the default schema (included in the app) or upload your own file. It is also possible to choose to edit this schema interactively. The interactive editing opens in a pop-up window, where in a first tab, MIDS elements can be added, removed, or moved to another MIDS level. In addition, mappings can be removed or added by clicking the "edit" icon of a MIDS element. In a second tab, the Unknown or Missing section of the schema can be edited, i.e. new properties and new values can be added. This interactively edited schema can be saved to file (JSON). The schema (be it default, custom or interactive) can be viewed by clicking "Show MIDS implementation", which opens a human-friendly visualization of the MIDS schema, so that it is not necessary to read the JSON file to be able to understand the specifics of the MIDS schema used. Once a dataset and a MIDS implementation have been chosen, calculations can be started by clicking "Start MIDS score calculations".
On this page a zipped GBIF annotated Darwin Core Archive or a comma or tab separated occurrence file can be uploaded (max 5GB). In addition, the MIDS implementation can be specified and viewed. To specify the MIDS implementation you can either choose the default schema (included in the app) or upload a schema from file. It is also possible to choose to edit this schema interactively. The interactive editing opens in a pop-up window, where in a first tab, MIDS elements can be added, removed, or moved to another MIDS level. In addition, mappings can be removed or added by clicking the "edit" icon of a MIDS element. In a second tab, the Unknown or Missing section of the schema can be edited, i.e. new properties and new values can be added. This interactively edited schema can be saved to file (JSON). The schema (be it default, custom or interactive) can be viewed by clicking the eye icon, which opens a human-friendly visualization of the MIDS schema, so that it is not necessary to read the JSON file to be able to understand the specifics of the MIDS schema used. Once a dataset and a MIDS implementation have been chosen, calculations can be started by clicking "Start MIDS score calculations".
', br(), h4('Results'), '
The results of each analysis are visualized on a new page, where it is possible to explore summaries of the results of both MIDS levels and MIDS elements, either as plots or as tables. It is also possible to explore the complete records table with the MIDS results for each record, and to download it as a csv file. In addition, the data can be filtered to see how MIDS results change when filtering on properties such as country code /taxonomic group/ collection date. The filename of the dataset is shown, as well as the used MIDS implementation, to make the provenance of the calculations clear.'
The results of each analysis are visualized on a new page, where it is possible to explore summaries of the results of both MIDS levels and MIDS elements, either as plots or as tables. The MIDS element plot can be clicked to get more details on the results of the mappings of that element. It is also possible to explore the complete records table with the MIDS results for each record, and to download it as a csv file. In addition, the data can be filtered to see how MIDS results change when filtering on properties such as country code /taxonomic group/ collection date. The filename of the dataset is shown, as well as the used MIDS implementation, to make the provenance of the calculations clear.'
)),
easyClose = TRUE,
footer = tagList(
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