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adding pip install and code documentation
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Jutta Schnabel committed Oct 29, 2024
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51 changes: 51 additions & 0 deletions .github/workflows/build_docs.yml
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# Sample workflow for building and deploying a Jekyll site to GitHub Pages
name: Deploy Jekyll with GitHub Pages dependencies preinstalled

on:
# Runs on pushes targeting the default branch
push:
branches: ["main"]

# Allows you to run this workflow manually from the Actions tab
workflow_dispatch:

# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages
permissions:
contents: read
pages: write
id-token: write

# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued.
# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete.
concurrency:
group: "pages"
cancel-in-progress: false

jobs:
# Build job
build:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Setup Pages
uses: actions/configure-pages@v5
- name: Build with Jekyll
uses: actions/jekyll-build-pages@v1
with:
source: ./
destination: ./_site
- name: Upload artifact
uses: actions/upload-pages-artifact@v3

# Deployment job
deploy:
environment:
name: github-pages
url: ${{ steps.deployment.outputs.page_url }}
runs-on: ubuntu-latest
needs: build
steps:
- name: Deploy to GitHub Pages
id: deployment
uses: actions/deploy-pages@v4
2 changes: 2 additions & 0 deletions README.md
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[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/YouSchnabel/EVERSE-landscape/HEAD)

# EVERSE-landscape

This repository serves to evaluate the outcomes of the landscaping survey from WP2 of the EVERSE project.
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43 changes: 43 additions & 0 deletions codemeta.js
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{
"@context": "https://doi.org/10.5063/schema/codemeta-2.0",
"@type": "SoftwareSourceCode",
"license": "https://spdx.org/licenses/BSD-3-Clause",
"codeRepository": "https://github.com/YouSchnabel/EVERSE-landscape",
"dateCreated": "2024-10-29",
"datePublished": "2024-10-30",
"name": "EVERSE landscaping survey",
"version": "0.1",
"description": "Outcome and analysis of the EVERSE software quality survey.",
"readme": "https://github.com/YouSchnabel/EVERSE-landscape/blob/main/README.md",
"softwareVersion": "0.1",
"author": [
{
"@type": "Organization",
"givenName": "Jutta",
"familyName": "Schnabel",
"email": "[email protected]"
},
{
"@type": "Person",
"givenName": "Jutta",
"familyName": "Schnabel",
"email": "[email protected]",
"affiliation": {
"@type": "Organization",
"name": "FAU Erlangen, ECAP"
}
}
],
"maintainer": [
{
"@type": "Person",
"givenName": "Jutta",
"familyName": "Schnabel",
"email": "[email protected]",
"affiliation": {
"@type": "Organization",
"name": "FAU Erlangen, ECAP"
}
}
]
}
47 changes: 0 additions & 47 deletions data/README.md
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# Survey data

## Goal of the survey

The overall goal of the EVERSE project is to evaluate and improve the quality of research software. This can be facilitated by collecting best practices on the one hand, and providing and sharing experiences especially in those areas where quality guidance and support is needed.

To this end, the goal of the survey should lay the ground to

* Identify communities and resources providing and well applying best practices (lighthouse practices)
* Identify commonalities throughout the research clusters (common practices)
* Identify gaps and deficits in software research practices (areas of improvement)

## Questionnaire setup

### Respondents

For the initial landscaping, the research areas and practices should be covered as widely as possible to be able to identify the areas of improvements. This will, due to limited responses, increase the inaccuracy of the survey. On the other hand, allowing more specific responses from experts in the fields would help to identify lighthouse practices.

Therefore, the respondents are asked to distinguish between two answering modes:

1. Reporting on their own practices, or their group and institution's practices (single view),
2. Assuming a general view on the research field they represent and estimating from best knowledge how the practices in the research field are established (representative view).

While everyone can answer the survey from a single view, a dedicated list of respondents is requested to fill the survey from a representative view, as well as specific experts. This information is captured in the first part (P).

### Software levels

In order to differentiate practices at different levels of software applicability, three software levels are introduced (analysis code, prototype tools, research software infrastructure). Participants are asked to exemplify these software levels for their area and give descriptions (D) and evaluate practices then according to these levels (L1-L3).

### Sections
Elements of the survey are annotated according to the section and functionality:

* Personal information: used to categorize the answers and evaluate the profile of the survey participant
* Software level evaluation: evaluation of the applicability of software levels
* Software levels: categorization per software level (L1 - L3)
* Software description: description of the software per level
* Guidelines: Guidelines and financing of quality practices
* Technical software quality practices: elements of technical software practices
* Management practices: aspects of software project management and incentivisation

### Question types

* Free text
* References
* Choice
* Selection
* Rating
* Ranking

## Export from EU survey

The export from the EU survey ("Results"-tab) should include to "Show Assigned Values" in the Settings, and be performed to the xls format.
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7 changes: 6 additions & 1 deletion docs/_config.yml
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theme: minima
theme: minima

include:
- about
- pages
- pydocs
37 changes: 37 additions & 0 deletions docs/about/.ipynb_checkpoints/thesurvey-checkpoint.md
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## Goal of the survey

The overall goal of the EVERSE project is to evaluate and improve the quality of research software. This can be facilitated by collecting best practices on the one hand, and providing and sharing experiences especially in those areas where quality guidance and support is needed.

To this end, the goal of the survey should lay the ground to

* Identify communities and resources providing and well applying best practices (lighthouse practices)
* Identify commonalities throughout the research clusters (common practices)
* Identify gaps and deficits in software research practices (areas of improvement)

## Questionnaire setup

### Respondents

For the initial landscaping, the research areas and practices should be covered as widely as possible to be able to identify the areas of improvements. This will, due to limited responses, increase the inaccuracy of the survey. On the other hand, allowing more specific responses from experts in the fields would help to identify lighthouse practices.

Therefore, the respondents are asked to distinguish between two answering modes:

1. Reporting on their own practices, or their group and institution's practices (single view),
2. Assuming a general view on the research field they represent and estimating from best knowledge how the practices in the research field are established (representative view).

While everyone can answer the survey from a single view, a dedicated list of respondents is requested to fill the survey from a representative view, as well as specific experts. This information is captured in the first part (P).

### Software levels

In order to differentiate practices at different levels of software applicability, three software levels are introduced (analysis code, prototype tools, research software infrastructure). Participants are asked to exemplify these software levels for their area and give descriptions (D) and evaluate practices then according to these levels (L1-L3).

### Sections
Elements of the survey are structured according to the section and functionality:

* Personal information: used to categorize the answers and evaluate the profile of the survey participant
* Software level evaluation: evaluation of the applicability of software levels
* Software levels: categorization per software level (L1 - L3)
* Software description: description of the software per level
* Guidelines: Guidelines and financing of quality practices
* Technical software quality practices: elements of technical software practices
* Management practices: aspects of software project management and incentivisation
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38 changes: 38 additions & 0 deletions docs/about/thesurvey.md
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# Survey setup
## Goal of the survey

The overall goal of the EVERSE project is to evaluate and improve the quality of research software. This can be facilitated by collecting best practices on the one hand, and providing and sharing experiences especially in those areas where quality guidance and support is needed.

To this end, the goal of the survey should lay the ground to

* Identify communities and resources providing and well applying best practices (lighthouse practices)
* Identify commonalities throughout the research clusters (common practices)
* Identify gaps and deficits in software research practices (areas of improvement)

## Questionnaire setup

### Respondents

For the initial landscaping, the research areas and practices should be covered as widely as possible to be able to identify the areas of improvements. This will, due to limited responses, increase the inaccuracy of the survey. On the other hand, allowing more specific responses from experts in the fields would help to identify lighthouse practices.

Therefore, the respondents are asked to distinguish between two answering modes:

1. Reporting on their own practices, or their group and institution's practices (single view),
2. Assuming a general view on the research field they represent and estimating from best knowledge how the practices in the research field are established (representative view).

While everyone can answer the survey from a single view, a dedicated list of respondents is requested to fill the survey from a representative view, as well as specific experts. This information is captured in the first part (P).

### Software levels

In order to differentiate practices at different levels of software applicability, three software levels are introduced (analysis code, prototype tools, research software infrastructure). Participants are asked to exemplify these software levels for their area and give descriptions (D) and evaluate practices then according to these levels (L1-L3).

### Sections
Elements of the survey are structured according to the section and functionality:

* Personal information: used to categorize the answers and evaluate the profile of the survey participant
* Software level evaluation: evaluation of the applicability of software levels
* Software levels: categorization per software level (L1 - L3)
* Software description: description of the software per level
* Guidelines: Guidelines and financing of quality practices
* Technical software quality practices: elements of technical software practices
* Management practices: aspects of software project management and incentivisation
21 changes: 21 additions & 0 deletions docs/pages/.ipynb_checkpoints/chap_finance-checkpoint.md
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## Financial support

### Sources of financing
| | Type | Lifecycle stage |
|---:|:---------------------------------------------------------------------------|:------------------|
| 6 | Many projects are also EU funded | project funding |
| 10 | another national example: the funding via the Netherlands eScience Centre: | project funding |
| | https://www.esciencecenter.nl | |
| | Type | Lifecycle stage |
|---:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------|
| 10 | recently, the EU OSCARS project https://oscars-project.eu had funding opportunities for research including software - this is an example for side funding: | side funding |
| | Description | Lifecycle stage |
|---:|:---------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 6 | nan | Increase the recognition of research software engineering work and research software engineers (it's probably close to reputation but different in the sense that funders and high level management need to recognize the importance of this part of a scientific experiment) |
| 9 | nan | Pride, vanity |
| 10 | Planning; Development; Archiving | Sharing of code with others |
| 11 | nan | Gamification (Badges etc.) |
| 12 | nan | no |
| 21 | nan | Get experience in using best practices for professional development |
| 25 | nan | Because the RSE is actually genuinely interested in the result/science |
| 27 | nan | Increase user base, consider options for technology transfer |
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