![\"debates](\"./docs/assets/political_debates_logo.svg\"){kind=logo}
\n![](\"docs/assets/cloud-infra-logo.svg\"){kind=logo}
\n\n# cloud-infra\n\nCommon declarative infrastructure on the cloud.\n\n## What this repo contains\n\nDeclarative configuration for deploying ORDES Cloud resources (e.g. on SWITCH\nand Azure). This is meant for project-agnostic infrastructure.\n\n- [vm-ordes-main development VM](components/vm-ordes-main)\n- [vm-gitlab-runner](components/vm-gitlab-runner)\n- [vm-nix-cache](components/vm-nix-cache)\n\nThis repo does not contain:\n\n- SWITCH-managed KaaS (SCK)\n- Project-specific VMs and manifests.\n\n## Getting started\n\nTo learn how to use this repository, you may want to read the following:\n\n- [👤 User Guidelines](docs/user.md) to access existing resources.\n- [🧑🔧 Contributing Guidelines](docs/CONTRIBUTING.md) to add your own components\n or contribute to this repository.\n- [🛡️ Security Guidelines](docs/SECURITY.md) for security-related questions.",
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\n\n\n## About\n\nThis repository provides an app that is able to transcribe and translate\ndebates, where speakers take turns. Any such video or audio file in the format\n`mp4` or `wav` can be uploaded via a dashboard for analysis.\n\n- The analysis is performed with the hugging face component\n [odtp-pyannote-whisper](https://github.com/sdsc-ordes/odtp-pyannote-whisper),\n that was developed in the context of this project and can be accessed directly\n via\n [hugging face](https://huggingface.co/spaces/katospiegel/odtp-pyannote-whisper).\n\n- The results of that analysis are loaded into an S3 compatible object store\n (garage).\n\n- From there it will be indexed into the Search Engine Solr. A Mongo db database\n is used to manage the media processing results and status\n\n- A dashboard is provided to make all processing and results available via a\n common interface: it consists of a frontend, a backend and a redis queue for a\n decoupled processing of the long running media analysis jobs on hugging face.\n\n## Authors\n\n- [Sabine Maennel](mailto:sabine.maennel@sdsc.ethz.ch)\n- [Carlos Vivar Rios](mailto:carlos.vivarrios@epfl.ch)\n- [Hannah Casey](mailto:hannah.casey@sdsc.ethz.ch)\n\n## Installation\n\nInstallation and options for the installations are described in the\n[documentation](https://sdsc-ordes.github.io/debates-analytics/installation/overview/)\n\n## Usage\n\nUsage is described in the\n[documentation](https://sdsc-ordes.github.io/debates-analytics/userguide/roles/)\n\n## Development\n\nSee\n[documentation](https://sdsc-ordes.github.io/debates-analytics/development/setup/)\n\n## Acknowledgement\n\nThis work was originally funded by the SNSF Spark Grant number 221139 “Debating\nHuman Rights”\n[SNSF Data Portal . Documentation: Political Debates](https://data.snf.ch/grants/grant/221139).\n\nThe goal of that project was to create specialized components for the analysis\nof videos from United Nations Human Rights Council (UNHRC) debates.\n\n- Sophisticated Transcription: Integrating and optimizing cutting-edge\n transcription models (e.g., Whisper 3.0) to ensure accurate, multilingual\n transcription of UNHRC debates.\n- Multimodal Data Handling: Developing components tailored to video/audio\n processing, scene extraction, and diarization.\n- Specialized Database Integration: Designing and deploying a database structure\n to effectively store debate transcripts, relevant metadata, and extracted\n features.\n\nThis repo was created as a wrapup of that project, to make the processings and\nresults available in a more general form.\n\n## Copyright\n\nCopyright © 2025-2028 Swiss Data Science Center (SDSC),\n[www.datascience.ch](http://www.datascience.ch/). All rights reserved. The SDSC\nis jointly established and legally represented by the École Polytechnique\nFédérale de Lausanne (EPFL) and the Eidgenössische Technische Hochschule Zürich\n(ETH Zürich). This copyright encompasses all materials, software, documentation,\nand other content created and developed by the SDSC.", + "readme_title": "", + "releases_count": 2, + "repo": "debates-analytics", + "repo_name": "debates-analytics", + "stars": 0, + "topics": [ + "debates", + "search-engine", + "transcription", + "translation" + ], + "updated_at": "2026-02-27T07:47:16Z", + "url": "https://github.com/sdsc-ordes/debates-analytics", + "watchers": 0 + }, + "https://github.com/sdsc-ordes/deid-module": { + "commit_count_default_branch": 64, + "contributors_count": 2, + "contributors_top": [ + { + "contributions": 47, + "html_url": "https://github.com/cmdoret", + "login": "cmdoret" + }, + { + "contributions": 17, + "html_url": "https://github.com/vancauwe", + "login": "vancauwe" + } + ], + "created_at": "2025-08-29T10:32:21Z", + "default_branch": "main", + "description": "Text deidentification module.", + "dirs_total_count": 13, + "files_root_entries": [ + { + "path": ".github", + "size": 0, 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"issues_total": 1, + "languages_top": [ + { + "language": "Nix", + "size": 3226 + }, + { + "language": "Just", + "size": 2898 + }, + { + "language": "Smarty", + "size": 1425 + }, + { + "language": "Shell", + "size": 483 + } + ], + "last_commit_date_default_branch": "2025-12-04T14:16:49Z", + "last_commit_oid_default_branch": "7ef8ba1dae7ff2a2167c19ca4d53eeb884513cb7", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "Apache License 2.0", + "license_spdx": "Apache-2.0", + "name_with_owner": "sdsc-ordes/deid-module", + "owner": "sdsc-ordes", + "parent_repo": null, + "parent_url": null, + "primary_language": "Nix", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 8, + "pull_requests_open": 0, + "pull_requests_total": 8, + "pushed_at": "2025-12-04T14:16:49Z", + "readme_length": 2530, + "readme_text": "# deid-module\n\nThis repository defines helm chart to deploy a text de-identification module based on [Microsoft Presidio](https://github.com/microsoft/presidio) to deploy on a kubernetes cluster.\nIt is meant to be used as an HTTP API.\n\n## Goal\n\nThe helm chart in this repository adapts the upstream presidio helm chart with the following goals:\n\n- easily override yaml configs with custom values\n- default support for multi-lingual texts (en/it/fr/de)\n\n## Usage\n\nThe docker images and helm chart are published directly in the github container registry of this repository.\nIt can be installed with:\n\n```shell\nhelm install --create-namespace presidio oci://ghcr.io/sdsc-ordes/deid-module-chart/deid-presidio\n```\n\nThe chart does not declare any ingress, as its meant to be used by other services within the cluster.\nIf you want to experiment with it, create a port-forward:\n\n```shell\nkubectl port-forward svc/presidio-deid-presidio-analyzer 8080:80\n```\n\nThe REST API documentation of Microsoft Presidio is available [here](https://microsoft.github.io/presidio/api-docs/api-docs.html) (or [here](https://web.archive.org/web/20230313231442/https://microsoft.github.io/presidio/api-docs/api-docs.html) via the Internet Archive's Wayback Machine])\n\n## Configuration\n\nYou may configure the installation by providing a custom values.yaml file.\n\n```shell\nhelm install -f custom-values.yaml --create-namespace presidio oci://ghcr.io/sdsc-ordes/deid-module-chart/deid-presidio\n```\n\nNote that the presidio configuration files are exposed through configMaps.\nThe values `analyzerConfigMapName` `defaultConfigMapName` and `recognizersConfigMapName` can be used to point to the name of an existing configMap.\nYou may look at [`configmap-default-recognizers.yaml`](./src/chart/templates/configmap-default-recognizers.yaml) for an example on how to write your own configmap.\n\n## Development\n\n> [!NOTE]\n> The following requires nix to be installed in the system ([install nix](https://docs.determinate.systems/))\n\nThe repository contains a nix flake with all the tooling needed to develop and deploy the charts.\nIf direnv and nix are installed on the system, just run `direnv allow` in the repository to activate the development shell whenever you `cd` into it.\n\n> [!TIP]\n> Alternatively, you can enter the devshell manually with\n> `nix develop ./tools/nix#default --accept-flake-config --command zsh`\n\nAdditionally, a justfile contains recipes to work with the manifests. Typs `just` to list available recipes (`just` is included in the nix flake).", + "readme_title": "deid-module", + "releases_count": 0, + "repo": "deid-module", + "repo_name": "deid-module", + "stars": 0, + "topics": [ + "ner", + "nlp", + "privacy" + ], + "updated_at": "2025-12-04T14:16:53Z", + "url": "https://github.com/sdsc-ordes/deid-module", + "watchers": 0 + }, + "https://github.com/sdsc-ordes/demo-biomedit-workflow": { + "commit_count_default_branch": 81, + "contributors_count": 2, + "contributors_top": [ + { + "contributions": 78, + "html_url": "https://github.com/cmdoret", + "login": "cmdoret" + }, + { + "contributions": 3, + "html_url": "https://github.com/supermaxiste", + "login": "supermaxiste" + } + ], + "created_at": "2022-10-26T07:42:13Z", + "default_branch": "main", + "description": "Proof of concept workflow for running tasks in a restricted hospital environment.", + "dirs_total_count": 13, + "files_root_entries": [ + { + "path": ".gitignore", + "size": 5479, + "type": "file" + }, + { + "path": "LICENSE", + "size": 35149, + "type": "file" + }, + { + "path": "README.md", + "size": 5426, + "type": "file" + }, + { + "path": "assets", + "size": 0, + "type": "dir" + }, + { + "path": "conf", + "size": 0, + "type": "dir" + }, + { + "path": "data", + "size": 0, + "type": "dir" + }, + { + "path": "docker", + "size": 0, + "type": "dir" + }, + { + "path": "docs", + "size": 0, + "type": "dir" + }, + { + "path": "main.nf", + "size": 1743, + "type": "file" + }, + { + "path": "modules", + "size": 0, + "type": "dir" + }, + { + "path": "nextflow.config", + "size": 1843, + "type": "file" + }, + { + "path": "scripts", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 20, + "first_commit_date_default_branch": "2022-10-26T07:42:14Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": true, + "has_docs": true, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Nextflow", + "size": 7848 + }, + { + "language": "Shell", + "size": 1265 + }, + { + "language": "Dockerfile", + "size": 57 + } + ], + "last_commit_date_default_branch": "2024-04-30T11:46:40Z", + "last_commit_oid_default_branch": "3fd7e774bfa259053bf3555055c42753d9c3fc5e", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "GNU General Public License v3.0", + "license_spdx": "GPL-3.0", + "name_with_owner": "sdsc-ordes/demo-biomedit-workflow", + "owner": "sdsc-ordes", + "parent_repo": null, + "parent_url": null, + "primary_language": "Nextflow", + "project_foreign_key": null, + "pull_requests_closed": 1, + "pull_requests_merged": 8, + "pull_requests_open": 0, + "pull_requests_total": 9, + "pushed_at": "2024-04-30T11:46:40Z", + "readme_length": 5363, + "readme_text": "# Demo BioMedIT workflow\n\n## Table of contents\n\n* [Context](#context)\n* [Containerized workflow](#containerized-workflow)\n* [Workflow description](#workflow-description)\n* [Usage](#usage)\n* [License](#license)\n\n## Context\n\nThis repository showcases a simple containerized workflow to semantize synthetic patient data using the SPHN framework.\nIt is designed to run on biomedical systems with the following restrictions:\n\n* No internet acccess besides a private container registry\n* Only `podman` and nextflow available\n* No root access\n* keep data provenance information separate from the git repository\n\n## Containerized workflow\n\nThe project is structured to run individual tasks in their own podman container. Nextflow is used as the workflow manager and event-based workflow executor.\n\n\n## Workflow description\n\nThe workflow processes simulated patient data from synthea in JSON format and generates an RDF graph describing patient healthcare appointments (patient, dates and institution). It then validates the resulting graph. In addition to nextflow's native logging capabilities, the workflow produces an interoperable semantic `log` in JSON-LD format for traceability.\n\nThe data is semantized using the [SPHN ontology](https://www.biomedit.ch/rdf/sphn-ontology). Mapping rules are defined in human readable [YARRRML format](https://rml.io/yarrrml/) (see [data/mappings.yml](data/mappings.yml)). The triples are materialized using containerized tools from [rml.io](https://rml.io). The graph validation is done using [pySHACL](https://github.com/RDFLib/pySHACL) with the [SPHN shacl shapes](https://git.dcc.sib.swiss/sphn-semantic-framework/sphn-shacl-generator). An interoperable log is defined in `conf/log.conf` and is obtained by formatting [Nextflow's log](https://www.nextflow.io/docs/latest/tracing.html) using [JSON-LD](https://json-ld.org/) format.\n\nThe workflow definition can be found in [main.nf](main.nf) and its configuration in [nextflow.config](nextflow.config).\n\n```mermaid\nflowchart TD\n p0(( ))\n p1[cat_patients_json]\n p2(( ))\n p3[convert_mappings]\n p4[generate_triples]\n p5(( ))\n p6(( ))\n p7[validate_shacl]\n p8(( ))\n p9[gzip_triples]\n p10(( ))\n p0 -->|json_dir| p1\n p1 --> p4\n p2 -->|yml_mappings| p3\n p3 --> p4\n p4 -->|graph| p7\n p5 -->|ontology| p7\n p6 -->|shapes| p7\n p7 --> p8\n p4 -->|graph| p9\n p9 --> p10\n```\n\n## Usage\n\nFirst clone the repository and move into the folder:\n\n`git clone https://github.com/SDSC-ORD/demo_biomedit_workflow.git && cd demo_biomedit_workflow`\n\n\n### Profiles\n\nTo interact with the workflow for development or production, we use different [Nextflow profiles](https://www.nextflow.io/docs/latest/config.html#config-profiles) as follows:\n\n* `nextflow run -profile standard main.nf`: Run the workflow using the workflow file in the current directory and publicly available containers defined in `conf/containers.yaml`. This is the default profile, and the `-profile` option can therefore be omitted.\n* `nextflow run -profile prod main.nf`: Run the containerized workflow using the latest commit on the repository remote and containers from the private registry.\n\n> [!TIP]\n> A helper script (`scripts/migrate_images.sh`) is provided to automatically migrate images to a custom registry/namespace.\n> It can read container declarations in a nextflow config file and handle the pull/tag/push of images.\n\n### Execution mode\n\nBy default, the workflow will be executed on each zip file present in the input directory.\n\nWhen the option `--listen=true` is provided, the workflow manager will instead listen continuously for filesystem events and trigger execution whenever a new zip file appears in the input directory. In this mode, a log will only be generated when the Nextflow execution is manually interrupted, meaning that the exit code in the log will always be 1.\n\n### Inputs\n\nThis repository includes a small set of example data in `data/raw/test_patients.zip`. If you'd like to use a larger dataset as input, we included `scripts/download_data.sh` to retrieve a synthetic dataset from [Synthea](https://github.com/synthetichealth/synthea) and put it directly in the input folder `data/raw/`.\n\n### Outputs\n\nThe `data/out` is structured as follows:\n\n```\ndata/out\n├── logs\n│ └── 2024-04-24T11:32:52.980140+02:00_infallible_kilby_logs.json\n├── reports\n│ ├── test_patients_1_report.ttl\n│ └── test_patients_2_report.ttl\n└── triples\n ├── test_patients_1.nt.gz\n └── test_patients_2.nt.gz\n```\n\nWhere `triples` contains the semantized data for each input archive, and `reports` contains the shacl validation report, indicating any violation of the schema constraints.\n\nFor each workflow run, a time-stamped log file with a unique name is also saved in `logs`.\n\n## License\n\nThe code in this repository is licensed under [GPLv3](LICENSE).\nThe SPHN ontology and shapes files included in this repository are redistributed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license](https://creativecommons.org/licenses/by-nc-sa/4.0/). 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Feel free to open an issue if you find any error. \n\nPDF-parser for blood samples used for the SwissHeart project.\n\nThis project uses Pydantic AI and LLM models to extract structured data from medical laboratory reports in PDF format. It supports two main report types: IKC and AKH blood analysis reports.\n\n## Features\n\n- **PDF Text Extraction**: Convert PDF reports to structured data\n- **Multiple Report Types**: Support for IKC and AKH laboratory formats\n- **Flexible LLM Integration**: Works with local Ollama models and cloud APIs\n - We encourage to deploy a loca Ollama model provided in `/ollama/Modelfile`. This configuration of`qwen3b:14b` has been tested on a `RTX3090`.\n- **Structured Output**: Validates and outputs JSON data matching Pydantic schemas\n- **CLI Interface**: Easy-to-use command-line tool\n\n## Usage\n\n### Docker Deployment\n\nThe easier way to deploy the tool is via docker. You will need to create a data volume where to place the pdfs to parse. \n\n#### Build the container:\n\n```bash\ndocker build -t didc-pdf-parser .\n```\n\n#### Run with GPU support:\n\n```bash\ndocker run -it --entrypoint bash --gpus all -v /path/to/data:/app/data --env-file .env didc-pdf-parser\n```\n\n#### Run CPU only:\n\n```bash\ndocker run -it --entrypoint bash -v /path/to/data:/app --env-file .env didc-pdf-parser\n```\n\n### Command Line Interface\n\n#### Basic Usage\n\nPlease see the section below to pull or create Ollama models. We tested the tool with the model available in `ollama/Modelfile`.\n\n```bash\n# IKC report with local Ollama\npython didc-pdf-parser/main.py /path/to/report.pdf -a IKC -m \"qwen3-14b-32k\" -u \"http://localhost:11434/v1\" --save-txt --verbose\n\n# AKH report with local Ollama\npython didc-pdf-parser/main.py /path/to/report.pdf -a AKH -m \"qwen3-14b-32k\" -u \"http://localhost:11434/v1\" --save-txt --verbose\n```\n\n#### Cloud API Usage\n\n```bash\n# Using OpenRouter API\npython didc-pdf-parser/main.py /path/to/report.pdf -a IKC -m \"qwen/qwen3-14b\" -u \"https://openrouter.ai/api/v1\" -k \"your-api-key\" --save-txt --verbose\n\n# Using environment variables for API key\nexport API_KEY=\"your-api-key\"\npython didc-pdf-parser/main.py /path/to/report.pdf -a AKH -m \"qwen/qwen3-14b\" -u \"https://openrouter.ai/api/v1\" --save-txt --verbose\n```\n\n#### Batch Usage\n\nThe tool supports batch processing of multiple PDF files in a directory. Analysis types (IKC/AKH) are automatically detected from filename patterns.\n\n```bash\n# Process all PDFs in a directory with auto-detection\npython main.py /path/to/pdf/directory -m \"qwen3-14b-32k\" -u \"http://localhost:11434/v1\" --save-txt --verbose\n\n# Process directory with forced analysis type (overrides auto-detection)\npython main.py /path/to/pdf/directory -a IKC -m \"qwen3-14b-32k\" -u \"http://localhost:11434/v1\"\n\n# Process directory with custom output location\npython main.py /path/to/pdf/directory -o /path/to/output -m \"qwen3-14b-32k\" -u \"http://localhost:11434/v1\"\n```\n\n**Filename Pattern Detection:**\n- Files containing `IKC_` (case-insensitive) → Detected as IKC analysis\n- Files containing `AKH_` (case-insensitive) → Detected as AKH analysis\n- Files without patterns → Default to IKC analysis (with warning)\n\n**Example filenames:**\n- `IKC_patient_001.pdf` → IKC analysis\n- `AKH_bloodwork_2024.pdf` → AKH analysis\n- `regular_report.pdf` → IKC analysis (default)\n\n**Batch Processing Features:**\n- Progress bar showing processing status\n- Individual file error handling (failures don't stop the batch)\n- Summary statistics at completion\n- Automatic output file naming based on input filenames\n\n\n### Command Line Options\n\n| Option | Short | Description | Default |\n|--------|-------|-------------|---------|\n| `--analysis-type` | `-a` | Report type: IKC or AKH | IKC |\n| `--output-dir` | `-o` | Output directory for generated files | PDF directory |\n| `--save-txt` | `-t` | Save intermediate raw text file | False |\n| `--verbose` | `-v` | Enable verbose logging | False |\n| `--model-name` | `-m` | LLM model name | From env: MODEL_NAME |\n| `--base-url` | `-u` | Base URL for LLM API | From env: BASE_URL |\n| `--api-key` | `-k` | API key for LLM service | From env: API_KEY |\n\n### Environment Variables\n\nCreate a `.env` file in the project root:\n\n```bash\nMODEL_NAME=qwen3:14b-q8_0\nBASE_URL=http://localhost:11434/v1\nAPI_KEY=your-api-key-if-needed\n```\n\n### Setting Up Local Models with Ollama\n\n1. **Install Ollama**:\n ``` bash\n # macOS\n brew install ollama\n \n # Linux\n curl -fsSL https://ollama.ai/install.sh | sh\n ```\n\n2. **Download and serve models**:\n ``` bash\n # Create model\n ollama create -f ollama/Modelfile qwen3-14b-32k\n \n # Start Ollama server\n ollama serve\n ```\n\n3. **Verify setup**:\n ```bash\n # Check available models\n curl http://localhost:11434/v1/api/tags\n \n # Test model\n curl http://localhost:11434/v1/api/generate -d '{\n \"model\": \"qwen2.5:14b\",\n \"prompt\": \"Hello, world!\"\n }'\n ```\n\n4. **Use the model with the model we just created**\n\n ```bash\n python main.py /path/to/report.pdf -a IKC -m \"qwen3:14b-q8_0\" -u \"http://localhost:11434/v1\" --save-txt --verbose\n ```\n\n### Supported Report Types\n\nThe data model can be found in `didc-pdf-parser/models.py`.\n\n#### IKC Reports\n\n- Electrolyte and Water Balance\n- Kidney Function\n- Proteins\n- Enzymes\n- Inflammation Markers\n- Heart and Muscle\n- Diabetes and Energy Metabolism\n- Lipid and Arteriosclerosis\n- Iron Metabolism\n- Vitamins\n- Thyroid Function\n- Sexual Hormones\n\n#### AKH Reports\n\n- Hematological Examinations\n - Blood Status\n - Blood Count (Absolute/Relative)\n- Hemostasis Examinations\n - Coagulation Factors\n \n## Contributing\n\nSOON. \n\n## Acknowledgements and Funding\nThe development of the DIDC PDF Parser is being funded by the Personalized Health and Related Technologies (PHRT), the Eidgenössische Technische Hochschule Zürich (ETH Zürich), and the Swiss Data Science Center (SDSC). \n\n\n## Copyright\nCopyright © 2025 Swiss Data Science Center (SDSC), www.datascience.ch. All rights reserved. The SDSC is jointly established and legally represented by the École Polytechnique Fédérale de Lausanne (EPFL) and the Eidgenössische Technische Hochschule Zürich (ETH Zürich). This copyright encompasses all materials, software, documentation, and other content created and developed by the SDSC in the context of the Swiss Heart project.", + "readme_title": "didc-pdf-parser", + "releases_count": 2, + "repo": "didc-pdf-parser", + "repo_name": "didc-pdf-parser", + "stars": 3, + "topics": [], + "updated_at": "2025-09-23T10:06:32Z", + "url": "https://github.com/sdsc-ordes/didc-pdf-parser", + "watchers": 1 + }, + "https://github.com/sdsc-ordes/digiwild": { + "commit_count_default_branch": 134, + "contributors_count": 2, + "contributors_top": [ + { + "contributions": 79, + "html_url": "https://github.com/vancauwe", + "login": "vancauwe" + }, + { + "contributions": 55, + "html_url": "https://github.com/caviri", + "login": "caviri" + } + ], + "created_at": "2024-04-22T08:24:04Z", + "default_branch": "main", + "description": "Digiwild - Citizen Science Wild Life Monitoring", + "dirs_total_count": 20, + "files_root_entries": [ + { + "path": ".github", + "size": 0, + "type": "dir" + }, + { + "path": ".gitignore", + "size": 3152, + "type": "file" + }, + { + "path": ".pre-commit-config.yaml", + "size": 515, + "type": "file" + }, + { + "path": "Dockerfile", + "size": 1723, + "type": "file" + }, + { + "path": "LICENSE", + "size": 34523, + "type": "file" + }, + { + "path": "README.md", + "size": 1499, + "type": "file" + }, + { + "path": "app", + "size": 0, + "type": "dir" + }, + { + "path": "data", + "size": 0, + "type": "dir" + }, + { + "path": "requirements.txt", + "size": 82, + "type": "file" + } + ], + "files_total_count": 94, + "first_commit_date_default_branch": "2024-04-22T08:24:04Z", + "forks": 0, + "has_ci": true, + "has_contributing": false, + "has_docker": true, + "has_docs": false, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": "https://www.fiwi.vetsuisse.unibe.ch/", + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 19, + "issues_open": 5, + "issues_total": 24, + "languages_top": [ + { + "language": "Python", + "size": 147792 + }, + { + "language": "Dockerfile", + "size": 1723 + } + ], + "last_commit_date_default_branch": "2025-02-20T09:34:19Z", + "last_commit_oid_default_branch": "1f4a5713131cae4a586e9488b98b1aa2e98952dc", + "latest_release_date": "2025-02-20T09:16:20Z", + "latest_release_tag": "v1.0.0", + "license_name": "GNU Affero General Public License v3.0", + "license_spdx": "AGPL-3.0", + "name_with_owner": "sdsc-ordes/digiwild", + "owner": "sdsc-ordes", + "parent_repo": null, + "parent_url": null, + "primary_language": "Python", + "project_foreign_key": null, + "pull_requests_closed": 1, + "pull_requests_merged": 14, + "pull_requests_open": 0, + "pull_requests_total": 15, + "pushed_at": "2025-03-13T09:16:17Z", + "readme_length": 1494, + "readme_text": "---\ntitle: Digiwild\nemoji: 🏢\ncolorFrom: purple\ncolorTo: green\nsdk: docker\npinned: false\nshort_description: Digiwild\n---\n\n# Digiwild\n\n# Citizen-Science Wilf Life Monitoring \n## Bird Monitoring Use-Case\n\n## About\n\nThis work stemmed from a fruitful collaboration between [Swiss Data Science Center](https://www.datascience.ch) and [Institute for Fish and Wildlife Health, University of Bern](https://www.fiwi.vetsuisse.unibe.ch).\nThe resulting [Gradio App is available to use on Hugging Face](https://huggingface.co/spaces/SDSC/digiwild), which is coupled to the [digiwild Hugging Face Dataset](https://huggingface.co/datasets/SDSC/digiwild-dataset). \nCredits and special thanks for the project can be found in the `About` section of the app. \n\n## How to Contact Us?\n\nFor **code contribution and any technical issues**, please open an `Issue` on this repository, we will address it as soon as possible. For enhancing the app, please open a PR, we welcome any and all enhancements or fixes. \n\nFor **usage of the app and any monitoring related questions**, please reach out to FIWI via [their contacts](https://www.fiwi.vetsuisse.unibe.ch/about_us/team/index_eng.html).\n\n## Development and Local set-up \n\n### Docker\n\n``` bash\ndocker build -t ordes/digiwild .\n```\n\n``` bash\ndocker run -it -p 7860:7860 ordes/digiwild\n```\n\n``` bash\ncd /digiwild/app\npython3 main.py\n```\n\nHow to develop on docker:\n\n``` bash\ndocker run -it -p 7860:7860 -v $(pwd):/home/user/digiwild/ --entrypoint bash ordes/digiwild\n```", + "readme_title": "Digiwild", + "releases_count": 1, + "repo": "digiwild", + "repo_name": "digiwild", + "stars": 0, + "topics": [ + "biodiversity", + "citizen-science", + "gradio", + "huggingface", + "monitoring", + "wildlife" + ], + "updated_at": "2025-02-20T09:34:25Z", + "url": "https://github.com/sdsc-ordes/digiwild", + "watchers": 2 + }, + "https://github.com/sdsc-ordes/dotfiles": { + "commit_count_default_branch": 12, + "contributors_count": 2, + "contributors_top": [ + { + "contributions": 11, + "html_url": "https://github.com/gabyx", + "login": "gabyx" + }, + { + "contributions": 1, + "html_url": null, + "login": "Dotfiles-Work" + } + ], + "created_at": "2024-08-22T11:48:03Z", + "default_branch": "main", + "description": "dotfiles with chezmoi - a starter configuration", + "dirs_total_count": 18, + "files_root_entries": [ + { + "path": ".chezmoiroot", + "size": 7, + "type": "file" + }, + { + "path": ".editorconfig", + "size": 327, + "type": "file" + }, + { + "path": ".envrc", + "size": 30, + "type": "file" + }, + { + "path": ".gitattributes", + "size": 4084, + "type": "file" + }, + { + "path": ".githooks", + "size": 0, + "type": "dir" + }, + { + "path": ".gitignore", + "size": 20, + "type": "file" + }, + { + "path": ".prettierrc", + "size": 180, + "type": "file" + }, + { + "path": ".shellcheckrc", + "size": 22, + "type": "file" + }, + { + "path": "LICENSE", + "size": 35148, + "type": "file" + }, + { + "path": "README.md", + "size": 3249, + "type": "file" + }, + { + "path": "config", + "size": 0, + "type": "dir" + }, + { + "path": "docs", + "size": 0, + "type": "dir" + }, + { + "path": "justfile", + "size": 708, + "type": "file" + }, + { + "path": "scripts", + "size": 0, + "type": "dir" + }, + { + "path": "tools", + "size": 0, + "type": "dir" + } + ], + "files_total_count": 52, + "first_commit_date_default_branch": "2024-08-22T14:53:37Z", + "forks": 0, + "has_ci": false, + "has_contributing": false, + "has_docker": false, + "has_docs": true, + "has_license_file": true, + "has_notebooks": false, + "has_readme_file": true, + "has_tests": false, + "homepage_url": null, + "is_archived": false, + "is_fork": false, + "is_private": false, + "issues_closed": 0, + "issues_open": 0, + "issues_total": 0, + "languages_top": [ + { + "language": "Shell", + "size": 107669 + }, + { + "language": "Nix", + "size": 1810 + }, + { + "language": "Dockerfile", + "size": 1507 + }, + { + "language": "Just", + "size": 708 + } + ], + "last_commit_date_default_branch": "2024-11-27T15:50:44Z", + "last_commit_oid_default_branch": "557fc267d17a3702b115058b8957aa4b5a2eb152", + "latest_release_date": null, + "latest_release_tag": null, + "license_name": "GNU General Public License v3.0", + "license_spdx": "GPL-3.0", + "name_with_owner": "sdsc-ordes/dotfiles", + "owner": "sdsc-ordes", + "parent_repo": null, + "parent_url": null, + "primary_language": "Shell", + "project_foreign_key": null, + "pull_requests_closed": 0, + "pull_requests_merged": 0, + "pull_requests_open": 0, + "pull_requests_total": 0, + "pushed_at": "2024-11-27T15:50:50Z", + "readme_length": 3133, + "readme_text": "
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+ "readme_text": "# imaging-plaza\nThis repository contains an script and docker-compose.yml able to deploy and run all the services needed for an imaging plaza cold start. \n\n1. GraphDB\n2. Gimie\n3. SHACL validator\n4. FAIR Indicator\n5. Frontend\n\nThe following services still rely in third-party providers:\n\n- Firebase Authentication\n- Firebase Database\n\n## How to run it?\n\n1. First add the credentials in `./config/.env.local` file.\n2. Uncompress the initial database (`db.zip` file not shared in this repo) in `./db`.\n - Alternatively you can create an empty directory and initiate graphDB manually. \n3. Execute the following docker compose commands\n\n``` bash\ndocker compose config\ndocker compose build\ndocker compose up\n```\n\n4. Access to the page in [http://0.0.0.0:3000](http://0.0.0.0:3000)\n\nOther services deployed are: \n\n- GraphDB Workbench [http://0.0.0.0:7515](http://0.0.0.0:7515)\n- Fair Indicator GUI [http://0.0.0.0:8502](http://0.0.0.0:8502)\n- SHACL Validator GUI [http://0.0.0.0:8501](http://0.0.0.0:8501)\n\n## How to run the developer mode?\n\n> [!NOTE] \n> This repository makes use of submodules. Therefore, when cloning it you need to include them.\n> \n> `git clone --recurse-submodules https://github.com/sdsc-ordes/imaging-plaza`\n\nIn development mode you can make use of the submodules to change to the development branch you would like to test.\n\n``` bash\ndocker compose -f docker-compose.dev.yml config\ndocker compose -f docker-compose.dev.yml build\ndocker compose -f docker-compose.dev.yml up\n```\n\nIn order to start with a database please add the `db.zip` to `db`. GraphDB will take the file, uncompress it and then load the graph. \n\n\nAlso the submodules app folders are mounted as volumes in the `/app` folders in docker. This is optional, uncomment the volumes lines if you want to activate this option. **Be aware that developing in windows can lead to issues due to different end of the line encodings.**\n\nFor instace:\n\n``` yaml\nvolumes:\n - \"./gimie-api/app:/app\n...\n```\n\nFinally, the entrypoint are replaced by bash so the services are required to be initialized manually. By default only the frontend is in development mode. \n\n- **gimie-api**: `uvicorn app.main:app --host 0.0.0.0 --port 15400`\n- **shacl-validator-api**: `bash /app/entrypoint.sh`\n- **fair-indicator-api**: `bash /app/imaging_plaza_fair_indicator_api/entrypoint.sh`\n- **imaging-plaza-frontend**: `sh entrypoint.sh`\n\nIf you want to disable this for some services, please comment the following lines in the `docker-compose.dev.yml`:\n\n``` yaml\n# entrypoint: bash \n# stdin_open: true\n# tty: true\n```\n\nOnce you have the different services up and running you can open with `vscode` (or any other IDE) the submodule folder and modify the different files. In the case of `imaging-plaza-frontend` those change will be applied automatically. If you want this, please uncomment the docker compose volumes. \n\n\n## Documentation\n\n### How to test a remote microservice?\n\nDocker compose use internal alias for the services. The name of the container plus the port can be used in order to communicate among them. If you want to test a deployed server, please change the host in `./config/.env.local` by the `IP` where the services is deployed:\n\nFor instance, if we want to connect to a remote graphDB deployed in `REMOTE_IP` you can do:\n\n```\nIP_DB_URL=http://REMOTE_IP:7200\nIP_GIMIE_URL=http://gimie-api:15400/gimie/jsonld\nIP_INFERENCE_URL=http://shacl-validator-api:15400/inference-jsonld?jsonInput=true&jsonOutput=true\nIP_FAIR_LEVEL_INDICATOR=http://fair-indicator-api:15400/indicate\n```",
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+ "readme_text": "# imaging-plaza-fair-indicator-api\n\n## How to deploy it? \n\n1. Fill the .env file containing all the GRAPHDB Credentials. It's important to add `/repositories/imagingplaza` to your graphDB deployment. \n\n2. Build the docker image\n\n``` bash\ndocker build -t imaging-plaza-fair-indicator-api:latest . \n```\n\n3. Run the docker container\n\n``` bash\ndocker run -it --rm -p 8000:15400 -p 8501:8501 --env-file .env imaging-plaza-fair-indicator-api:latest\n```\n\nAfter this command, the API endpoint will be in port 8000 while the GUI in port 8501\n\n## How to use the API? \n\nThe endpoint expect the uid in the path: `0.0.0.0:8000/indicate/?uri={URI}&graph{GRAPH}`\n\n## How to use the GUI? \n\nGo to `0.0.0.0:8501`\n\n## Robin's notes\n\n```\n//1. First, run the SHACL validate on the current graph, using shacl shapes specific to fair level calc\n//2. Then, the result of that call should be uploaded into a graph on GraphDB - and given a temporary Graph URI.\n//3. Then, the query below should be run on that temporary Graph URI, after which the graph can be deleted.\n//4. The result of the query should be parsed and presented to the user.\n\n//Note: query could be further improved after to include labels rather than path URI's,\n// but lets first make it work before we start pulling results from multiple graphs\n```\n\n## DEV\n\nThese are useful command for development and testing.\n\n```\npoetry install\npoetry shell\npython3 -m uvicorn app.main:app --host 0.0.0.0 --port 15400\n```\n\nHow to run an example call?\n\n```\nhttp://0.0.0.0:15400/indicate/?uri=https://github.com/SDSC-ORD/gimie&graph=https://epfl.ch/example/temporaryGraph\n```\n\nHow to use it as package\n```\nfrom fairindicator import *\ngraph=\"https://epfl.ch/example/temporaryGraph\"\nuri=\"https://github.com/SDSC-ORD/gimie\"\nsuggestions = indicate_fair(uri, graph)\n```\n\n## Things to improve\n\n1. Include the shapesfile as a parameter variables instead of a hardcoded path within the scripts.\n\n\n## Changelog\n\n- v0.0.10 - Update to ontology v0.9\n- v0.0.9 - Update to ontology v0.8\n- v0.0.8 - Dockerfile simplified\n- v0.0.7 - Update to ontology v0.7\n- v0.0.6 - Initial prod version. Ontology v0.6",
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+}
\ No newline at end of file
\n\n mava-api\n
\n![\"project](\"./docs/assets/mava_logo.svg\"){kind=logo}
\n\n mava-exchange\n
\n![\"modos](\"./docs/img/modos.svg\")
\n\nUse the `make` command to generate project artefacts:\n\n* `make all`: make everything\n* `make deploy`: deploys site\n\n\n## Credits\n\nThis project was made with\n[linkml-project-cookiecutter](https://github.com/linkml/linkml-project-cookiecutter).\n\n## Acknowledgements and Funding\n\nThe development of the Multi-Omics Digital Object System (MODOS) is being funded by the Personalized Health Data Analysis Hub, a joint initiative of the Personalized Health and Related Technologies ([PHRT](https://www.sfa-phrt.ch)) and the Swiss Data Science Center ([SDSC](https://datascience.ch)), for a period of three years from 2023 to 2025. The SDSC leads the development of MODOS, bringing expertise in complex data structures associated with multi-omics and imaging data to advance privacy-centric clinical-grade integration. The PHRT contributes its domain expertise of the Swiss Multi-Omics Center ([SMOC](http://smoc.ethz.ch)) in the generation, analysis, and interpretation of multi-omics data for personalized health and precision medicine applications.\nWe gratefully acknowledge the [Health 2030 Genome Center](https://www.health2030genome.ch/) for their substantial contributions to the development of MODOS by providing test data sets, deployment infrastructure, and expertise. \n\n## Copyright\nCopyright © 2023-2024 Swiss Data Science Center (SDSC), [www.datascience.ch](http://www.datascience.ch/). All rights reserved. The SDSC is jointly established and legally represented by the École Polytechnique Fédérale de Lausanne (EPFL) and the Eidgenössische Technische Hochschule Zürich (ETH Zürich). This copyright encompasses all materials, software, documentation, and other content created and developed by the SDSC in the context of the Personalized Health Data Analysis Hub.",
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+ "readme_text": "## Context\n\nThis repository includes code from a Swiss [National Data Stream (NDS)](https://sphn.ch/services/funding_old/nds/): LUCID. The general goal of the NDS initiative is to collect clinical data across five Swiss University Hospitals and share it with researchers. In the case of LUCID, research focuses on low-value care: services that provide little or no benefit to patients. If you're interested, check the official [project page](https://sphn.ch/network/projects/project-page_nds_lucid/).\n\n## Introduction\n\n**graphdb-loader** is a pipeline for loading RDF data into a GraphDB instance, handling backups and failures gracefully.\n\nThe pipeline is built using [Nextflow](https://www.nextflow.io), a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. The [Nextflow DSL2](https://www.nextflow.io/docs/latest/dsl2.html) implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies.\n\n## Requirements\n\nThe workflow assumes that:\n* [Nextflow](https://www.nextflow.io/docs/latest/install.html#installation) is installed (>=22.10.1)\n* Basic UNIX tools are installed: `xargs`, `sed`, `awk`, `curl` \n* A running GraphDB server is accessible through the network.\n* A directory (`graphdb_dir`) is shared between the graphdb server and the workflow (either it is a shared volume, or they run on the same host).\n* [Nextflow secrets](https://www.nextflow.io/docs/latest/secrets.html) `GRAPHDB_USER` and `GRAPHDB_PASS` are set to provide GraphDB authentication credentials.\n\nSee [usage instructions](docs/usage.md) for more information.\n\n\n## Pipeline summary\n\n\n\n1. Check if new RDF files are present in source directory\n2. Attempt to load files in target GraphDB instance.\n3. In case of failure, retry operation up to 3 times\n4. Backup successfully imported RDF files to backup directory.\n5. Log all imports, their target graph, success status and the GraphDB server response.\n\n```mermaid\nflowchart TD\n p0((Channel.fromPath))\n p1(( ))\n p2[copy_to_graphdb]\n p3(( ))\n p4(( ))\n p5[graphdb_auth]\n p6[graphdb_import_file]\n p7([map])\n p8([filter])\n p9([map])\n p10(( ))\n p11[copy_to_backup]\n p12(( ))\n p13([map])\n p14([collectFile])\n p15([view])\n p16(( ))\n p0 -->|ch_input_files| p2\n p1 -->|graphdb_dir| p2\n p2 --> p6\n p3 -->|username| p5\n p4 -->|password| p5\n p5 -->|-| p6\n p6 --> p7\n p7 -->|ch_log| p8\n p8 --> p9\n p9 -->|ch_imported_files| p11\n p10 -->|backup_dir| p11\n p11 --> p12\n p7 -->|ch_log| p13\n p13 --> p14\n p14 --> p15\n p15 --> p16\n```\n\n## Quick Start\n\nSee [usage docs](./docs/usage.md) for all of the available options when running the pipeline.\n\n1. Install [`Nextflow`](https://www.nextflow.io/docs/latest/getstarted.html#installation) (`>=22.10.1`)\n\n2. Install [`Podman`](https://podman.io/) for full pipeline reproducibility (tested on 4.9.4).\n\n3. Download the pipeline and test it on a minimal dataset with a single command:\n\n ```bash\n nextflow run sdsc-ordes/nds-lucid-graphdb-loader -profile test --outdir ![\"LUCID](\"./static/logos/lucid_logo.svg\"){kind=logo}
\n\n LUCID Web Application\n
\n\n
\n\n> [!CAUTION]\n>\n> This workshop is currently under development and is not yet complete.\n\nThis workshop is structured in 2 parts\n\n- Part 1: Learn what a Nix DevShell is and how to extend it. It is designed to\n provide a **minimal, hands-on** introduction to how Nix works. While\n explanations are concise, they aim to be precise enough to help you grasp the\n objectives of this part.\n\n - Gain a basic understanding of the Nix language.\n - Learn what a `flake.nix` file is.\n - Understand what a Nix derivation is and how it materializes.\n\n- Part 2: Building and running a basic NixOS system.\n - Requires Part 1.\n - Configure and build a NixOS system.\n - Deploy it to a cloud VM.\n\n\n\n\n\n- [Requirements](#requirements)\n- [Part 1 - Nix & Nix DevShell](#part-1-nix-nix-devshell)\n- [Part 2 - NixOS](#part-2-nixos)\n - [What is NixOS?](#what-is-nixos)\n - [The `nixosSystem` Function](#the-nixossystem-function)\n - [Build/Run & Understand a Simple VM](#buildrun-understand-a-simple-vm)\n - [Understand the Configuration](#understand-the-configuration)\n\n\n\n\n\n> [!TIP]\n>\n> Due to the lack of well-structured and centralized official documentation on\n> Nix topics, many external links to additional reading materials and videos are\n> provided. Learning Nix can be challenging, but we prioritize linking to\n> official documentation whenever relevant. Some useful resources include:\n>\n> - [Nix Packages Search](https://search.nixos.org/packages?)\n> - [Nix Packages Search for Version Pinning](https://nixhub.io)\n> - [NixOS Manual](https://nixos.org/manual/nixos/stable)\n> - [NixOS Options Search](https://search.nixos.org/options?)\n> - [NixOS With Flakes](https://nixos-and-flakes.thiscute.world)\n> - [NixOS Status](https://status.nixos.org)\n> - [Nixpkgs Pull Request Tracker](https://nixpk.gs/pr-tracker.html)\n> - [Nixpkgs-Lib Function Search](https://noogle.dev)\n\n## Requirements\n\nThe basic requirements for working with this repository by using one of the\nfollowing approaches:\n\n- **Use your machine** [recommended]\n\n Ensure that you have installed\n [`Nix`](https://swissdatasciencecenter.github.io/best-practice-documentation/docs/dev-enablement/nix-and-nixos#installing-nix)\n and optionally\n [`direnv`](https://swissdatasciencecenter.github.io/best-practice-documentation/docs/dev-enablement/nix-and-nixos#installing-direnv).\n\n- **Use the container**\n\n You can run most of the workshop inside the provided container by running in\n the root of this repository\n\n ```bash\n git clean -df\n\n podman run -it \\\n --userns=keep-id \\\n -v \"$(pwd):/workspace\" \\\n -w /workspace \\\n ghcr.io/sdsc-ordes/nix-workshop:latest\n ```\n\n or\n\n ```bash\n git clean -df\n\n docker run -it \\\n --user root \\\n -v \"$(pwd):/workspace\" \\\n -w /workspace \\\n ghcr.io/sdsc-ordes/nix-workshop:latest\n ```\n\n- **Use the `devcontainer`** [\n\n A `devcontainer` setup is given in [`.devcontainer`](./.devcontainer). You can\n start this with `code`. **Adjust the\n [`devontainer.json`](./.devcontainer/devcontainer.json) if you are using\n `docker` instead of `podman`**.\n\n## Part 1 - Nix, Flakes & Nix DevShell\n\n[Check the slides here.](https://sdsc-ordes.github.io/technical-presentation/gh-pages/nix-workshop/part-1)\n\n## Part 2 - NixOS\n\n#### What is NixOS?\n\nA NixOS derivation is created by a function `inputs.nixpkgs.lib.nixosSystem`\nwhich comes from the [Nixpkgs Flake](https://github.com/NixOS/nixpkgs). The\n`nixpkgs` repository is the mono-repository which maintains more than 130'000\nsoftware packages. These packages are defined - by nothing more than (you\nguessed it) - Nix functions which return derivations.\n\n##### The `nixosSystem` Function\n\nThe `inputs.nixpkgs.lib.nixosSystem` function will produce a derivation which\nyou can evaluate. For example - in this repository's\n[`flake.nix`](./flake.nix) - the attribute\n`nixosConfigurations.x86_64-linux.vm-simple` is such a function invocation and\nto evaluate it you can run:\n\n```bash\nnix eval \".#nixosConfigurations.vm-simple.config.system.build.toplevel\"\n```\n\nor you **better build** it with\n\n```bash\nnix build \".#nixosConfigurations.vm-simple.config.system.build.toplevel\"\n```\n\nwhich will take some time and the resulting Nix store path linked by `./result`\nthen contains the built OS:\n\n```bash\ntree -L 2 ./result\n\n> ./result\n> ├── activate\n> ├── append-initrd-secrets -> /nix/store/ccyny0nr3a4dn5f864caj1nmvhkyz938-append-initrd-secrets/bin/append-initrd-secrets\n> ├── bin\n> │ └── switch-to-configuration\n> ├── boot.json\n> ├── configuration-name\n> ├── dry-activate\n> ├── etc -> /nix/store/34f7cw087r9js5c1f3snwra466p03rjv-etc/etc\n> ├── extra-dependencies\n> ├── firmware -> /nix/store/28a2j3qirvnzjrzwahgqpl6d83p4dibb-firmware/lib/firmware\n> ├── init\n> ├── init-interface-version\n> ├── initrd -> /nix/store/rhk5xymxhaap08abmvy8fspcfm3ghyf1-initrd-linux-6.6.82/initrd\n> ├── kernel -> /nix/store/7c8qjbmxyyyscapv46cprwg3h1i0ry8b-linux-6.6.82/bzImage\n> ├── kernel-modules -> /nix/store/5r9yn7mgianbqlqdn649k647v7zn6k9w-linux-6.6.82-modules\n> ├── kernel-params\n> ├── nixos-version\n> ├── specialisation\n> ├── sw -> /nix/store/v71rhhllsl7av7nrkzb58snsb2c7fal1-system-path\n> ├── system\n> └── systemd -> /nix/store/w9qcpyhjrxsqrps91wkz8r4mqvg9zrxc-systemd-256.10\n```\n\nThe above (to my best knowledge) in simple terms contains everything needed to\nboot into this NixOS system. An\n[activation script `activate`](https://nixos.org/manual/nixos/stable/#sec-activation-script)\nprepares symlinks into the immutable `/nix/store` on folders like\n\n- `/boot` (kernel and bootloader),\n- `/etc` (system configuration files), -`/var` (variable data) ,\n- `/run` (ephemeral runtime data),\n- `/home` (home directories)\n- etc.\n\n[`switch-to-configuration`](https://nixos.org/manual/nixos/stable/#sec-switching-systems)\nscript also takes care to switch to the new system on a running system and\nrestart the appropriate `systemd` services.\n\nThe next section explains how to build/run & understand a simple NixOS VM setup.\n\n### Build/Run & Understand a Simple VM\n\nFirst familiar yourself with the commands on the [`justfile`](./justfile) with\nby running `just`. The `build` and `repl` commands, we have covered in the\nintroduction.\n\nIn this example we are going to build a simple VM with just a user `nixos` and\nwithout a graphical desktop environment.\n\n```bash\ncp ./.env.tmpl .env\n## Modify the .env file to have `NIXOS_HOST=vm-simple`\n```\n\n#### Understand the Configuration\n\nThe [`./nixos/hosts/vm-simple/default.nix`](./nixos/hosts/vm-simple/default.nix)\ncontains one function which returns the NixOS system:\n\n```nix\n{ inputs, outputs, ... }:\ninputs.nixpkgs.lib.nixosSystem {\n system = \"x86_64-linux\";\n modules = [\n ./hosts/vm/configuration.nix\n ];\n specialArgs = {\n inherit inputs outputs;\n };\n}\n```\n\nThe function call `inputs.nixpkgs.lib.nixosSystem {...}` is a function defined\nin [`nixpgs`](https://github.com/NixOS/nixpkgs) which will generate a Nix\nderivation.\n\nWhen we instruct Nix to build this derivation, the derivation gets realized in\nthe Nix store (e.g. in `/nix/store`).",
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+ "readme_text": "# odtp-component-client\n\nClient for ODTP Components. This repository is aimed to be used as a submodule in the different components repositories. \n\n## Changelog\n\n- v0.1.0\n - Logging in `logs` collection as individual document. \n - Updating `output` document\n - Updating `result` document\n\n- v0.0.1\n - First version\n - Inluding conditional to make snapshot optionals\n - Adding quiet flag to compressing odtp-output\n\n# Acknowledgments, Copyright, and Licensing\n## Acknowledgments and Funding\nThis work is part of the broader project **O**pen **D**igital **T**win **P**latform of the **S**wiss **M**obility **S**ystem (ODTP-SMS) funded by Swissuniversities CHORD grant Track B - Establish Projects. ODTP-SMS project is a joint endeavour by the Center for Sustainable Future Mobility - CSFM (ETH Zürich) and the Swiss Data Science Center - SDSC (EPFL and ETH Zürich). \nThe Swiss Data Science Center (SDSC) develops domain-agnostic standards and containerized components to manage digital twins. This includes the creation of the Core Platform (both back-end and front-end), Service Component Integration Templates, Component Ontology, and the Component Zoo template. \nThe Center for Sustainable Future Mobility (CSFM) develops mobility services and utilizes the components produced by SDSC to deploy a mobility digital twin platform. CSFM focuses on integrating mobility services and collecting available components in the mobility zoo, thereby applying the digital twin concept in the realm of mobility.\n \n## Copyright\nCopyright © 2023-2024 Swiss Data Science Center (SDSC), www.datascience.ch. All rights reserved.\nThe SDSC is jointly established and legally represented by the École Polytechnique Fédérale de Lausanne (EPFL) and the Eidgenössische Technische Hochschule Zürich (ETH Zürich). This copyright encompasses all materials, software, documentation, and other content created and developed by the SDSC.\n\n## Intellectual Property (IP) Rights\nThe Open Digital Twin Platform (ODTP) is the result of a collaborative effort between ETH Zurich (ETHZ) and the École Polytechnique Fédérale de Lausanne (EPFL). Both institutions hold equal intellectual property rights for the ODTP project, reflecting the equitable and shared contributions of EPFL and ETH Zürich in the development and advancement of this initiative. \n \n## Licensing\nThe Service Component Integration Templates within this repository are licensed under the BSD 3-Clause \"New\" or \"Revised\" License. This license allows for broad compatibility and standardization, encouraging open use and contribution. For the full license text, please see the LICENSE file accompanying these templates.\n\n### Distinct Licensing for Other Components\n- **Core Platform**: Open-source under AGPLv3.\n- **Ontology**: Creative Commons Attribution-ShareAlike (CC BY-SA).\n- **Component Zoo Template**: BSD-3 license.\n\n### Alternative Commercial Licensing\nAlternative commercial licensing options for the core platform and other components are available and can be negotiated through the EPFL Technology Transfer Office (https://tto.epfl.ch) or ETH Zürich Technology Transfer Office (https://ethz.ch/en/industry/transfer.html).\n\n## Ethical Use and Legal Compliance Disclaimer\nPlease note that this software should not be used to deliberately harm any individual or entity. Users and developers must adhere to ethical guidelines and use the software responsibly and legally. This disclaimer serves to remind all parties involved in the use or development of this software to engage in practices that are ethical, lawful, and in accordance with the intended purpose of the software.",
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+ "readme_text": "# odtp-llm-parser\n\nAdd here your badges:\n[](http://localhost:8501/launch-component)\n[](\"\")\n\n> [!NOTE] \n> This repository makes use of submodules. Therefore, when cloning it you need to include them.\n> \n> `git clone --recurse-submodules https://github.com/odtp-org/odtp-llm-parser`\n\nThe component is a Python script that extracts structured information from a text input using the OpenAI API based on a provided JSON schema. It accepts command-line arguments for the OpenAI API key, the JSON schema file path, the text input (either from a file or direct input), and an optional output file path. The script processes the text, calls the OpenAI API to extract information according to the schema, and writes the extracted data to a JSON file.\n\n## Table of Contents\n\n- [Tools Information](#tools-information)\n- [How to add this component to your ODTP instance](#how-to-add-this-component-to-your-odtp-instance)\n- [Data sheet](#data-sheet)\n - [Parameters](#parameters)\n - [Secrets](#secrets)\n - [Input Files](#input-files)\n - [Output Files](#output-files)\n- [Tutorial](#tutorial)\n - [How to run this component as docker](#how-to-run-this-component-as-docker)\n - [Development Mode](#development-mode)\n - [Running with GPU](#running-with-gpu)\n - [Running in API Mode](#running-in-api-mode)\n- [Credits and References](#credits-and-references)\n\n## Tools Information\n\n| Tool | Semantic Versioning | Commit | Documentation |\n| --- | --- | --- | --- |\n| [openai](https://pypi.org/project/openai/) | v1.54.4 | --- | [API Reference](https://platform.openai.com/docs/api-reference/introduction?lang=python) |\n\n## How to add this component to your ODTP instance\n\nIn order to add this component to your ODTP CLI, you can use. If you want to use the component directly, please refer to the docker section. \n\n``` bash\nodtp new odtp-component-entry \\\n--name odtp-llm-parser \\\n--component-version v0.0.1 \\\n--repository https://github.com/sdsc-ordes/odtp-llm-parser \n```\n\n## Data sheet\n\n### Parameters\n\nNo Parameters\n\n### Secrets\n\n| Secret Name | Description | Type | Required | Default Value | Constraints | Notes |\n| | OPENAI_KEY | OpenAI API key used for authentication | string | yes | None | Must be a valid OpenAI API key | Required for accessing OpenAI API |\n\n### Input Files\n\n### Input Files\n\n| File/Folder | Description | File Type | Required | Format | Notes |\n| --- | --- | --- | --- | --- | --- |\n| schema.json | JSON schema file that defines the structure for information extraction | .json | Yes | JSON | Must be a valid JSON schema |\n| input.txt | Text file containing the content to extract information from | .txt | Yes | Text | Plain text file |\n\n### Output Files\n\n| File/Folder | Description | File Type | Contents | Usage |\n| --- | --- | --- | --- | --- |\n| output.json | Extracted structured information | .json | JSON data following schema | Contains extracted data |\n\n## Tutorial\n\n### How to run this component as docker\n\nBuild the dockerfile.\n\n``` bash\ndocker build -t odtp-llm-parser .\n```\n\nRun the following command. Mount the correct volumes for input/output/logs folders.\n\n``` bash\ndocker run -it --rm \\\n-v {PATH_TO_YOUR_INPUT_VOLUME}:/odtp/odtp-input \\\n-v {PATH_TO_YOUR_OUTPUT_VOLUME}:/odtp/odtp-output \\\n-v {PATH_TO_YOUR_LOGS_VOLUME}:/odtp/odtp-logs \\\n--env-file .env odtp-llm-parser\n```\n\n### Development Mode\n\nTo run the component in development mode, mount the app folder inside the container:\n\n``` bash\ndocker run -it --rm \\\n-v {PATH_TO_YOUR_INPUT_VOLUME}:/odtp/odtp-input \\\n-v {PATH_TO_YOUR_OUTPUT_VOLUME}:/odtp/odtp-output \\\n-v {PATH_TO_YOUR_LOGS_VOLUME}:/odtp/odtp-logs \\\n-v {PATH_TO_YOUR_APP_FOLDER}:/odtp/app \\\n--env-file .env odtp-llm-parser\n```\n\n### Running with GPU \n\nTo run the component with GPU support, use the following command:\n\n``` bash\ndocker run -it --rm \\\n--gpus all \\\n-v {PATH_TO_YOUR_INPUT_VOLUME}:/odtp/odtp-input \\\n-v {PATH_TO_YOUR_OUTPUT_VOLUME}:/odtp/odtp-output \\\n-v {PATH_TO_YOUR_LOGS_VOLUME}:/odtp/odtp-logs \\\n--env-file .env odtp-llm-parser\n```\n\n### Running in API Mode\n\nTo run the component in API mode and expose a port, use the following command:\n\n``` bash\ndocker run -it --rm \\\n-v {PATH_TO_YOUR_INPUT_VOLUME}:/odtp/odtp-input \\\n-v {PATH_TO_YOUR_OUTPUT_VOLUME}:/odtp/odtp-output \\\n-v {PATH_TO_YOUR_LOGS_VOLUME}:/odtp/odtp-logs \\\n-p 7860:7860 \\\n--env-file .env \\\n--entrypoint python3 \\\nodtp-llm-parser \\\n/odtp/odtp-app/gradio_api.py\n```\n\n## Credits and references\n\nSDSC\n\nThis component has been created using the `odtp-component-template` `v0.5.0`. \n\n\ndocker run -it --rm -p 7860:7860 --env-file .env --entrypoint python3 odtp-llm-parser /odtp/odtp-app/gradio_app.py",
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+ "readme_text": "---\ntitle: odtp-pyannote-whisper\nsdk: docker\npinned: false\n---\n\n# odtp-pyannote-whisper\n\n[](\"\") [](https://huggingface.com/spaces/katospiegel/odtp-pyannote-whisper)\n\n> [!NOTE] \n> This repository makes use of submodules. Therefore, when cloning it you need to include them.\n> \n> `git clone --recurse-submodules https://github.com/sdsc-ordes/odtp-pyannote-whisper`\n\nThis pipeline processes a `.wav` or `mp4` media file by detecting the number of speakers present in the recording using `pyannote.audio`. For each detected speaker segment, it employs `OpenAI's Whisper model` to transcribe or translate the speech individually. This approach ensures accurate and speaker-specific transcriptions or translations, providing a clear understanding of who said what throughout the audio.\n\nNote: This application utilizes `pyannote.audio` and OpenAI's Whisper model. You must accept the terms of use on Hugging Face for the `pyannote/segmentation` and `pyannote/speaker-diarization` models before using this application.\n\n- [Speaker-Diarization](https://huggingface.co/pyannote/speaker-diarization-3.1)\n- [Speaker-Segmentation](https://huggingface.co/pyannote/segmentation-3.0)\n\nAfter accepting these terms and conditions for those models. You can obtain you HuggingFace API Key to allow the access to these models: \n\n- [Hugging Face Access Keys](https://huggingface.co/settings/tokens)\n\nThis token should be provided to the component via the `ENV` variables or by the corresponding text field in the web app interface ([Here](https://huggingface.com/spaces/katospiegel/odtp-pyannote-whisper)).\n\n\n\n\n## Table of Contents\n\n- [Tools Information](#tools-information)\n- [How to add this component to your ODTP instance](#how-to-add-this-component-to-your-odtp-instance)\n- [Data sheet](#data-sheet)\n - [Parameters](#parameters)\n - [Secrets](#secrets)\n - [Input Files](#input-files)\n - [Output Files](#output-files)\n- [Tutorial](#tutorial)\n - [How to run this component as docker](#how-to-run-this-component-as-docker)\n - [Development Mode](#development-mode)\n - [Running with GPU](#running-with-gpu)\n - [Running in API Mode](#running-in-api-mode)\n- [Credits and References](#credits-and-references)\n\n## Tools Information\n\n| Tool | Version | Commit Hash | Documentation |\n|-------------------------------------------------|------------|-------------|--------------------------------------------------------------------|\n| [OpenAI Whisper](https://github.com/openai/whisper) | Latest | [Commit History](https://github.com/openai/whisper/commits/main) | [Whisper Documentation](https://github.com/openai/whisper#readme) |\n| [pyannote.audio](https://github.com/pyannote/pyannote-audio) | Latest | [Commit History](https://github.com/pyannote/pyannote-audio/commits/master) | [pyannote.audio Documentation](https://pyannote.github.io/pyannote-audio/) |\n\n## How to add this component to your ODTP instance\n\nThis component can be run directly with Docker, however it is designed to be run with [ODTP](https://odtp-org.github.io/odtp-manuals/). In order to add this component to your ODTP CLI, you can use. If you want to use the component directly, please refer to the docker section. \n\n``` bash\nodtp new odtp-component-entry \\\n--name odtp-pyannote-whisper \\\n--component-version v0.1.1 \\\n--repository https://github.com/sdsc-ordes/odtp-pyannote-whisper \n```\n\n## Data sheet\n\n### Parameters\n\n| Parameter | Description | Type | Required | Default Value | Possible Values | Constraints |\n|--------------|--------------------------------------------------------|--------|----------|---------------|-------------------------------------------------------------------|------------------------------------|\n| `MODEL` | Whisper model to use for transcription or translation | String | Yes | `large-v3` | `tiny`, `base`, `small`, `medium`, `large`, `large-v2`, `large-v3` | Must be a valid Whisper model name |\n| `TASK` | Task to perform on the audio input | String | Yes | `transcribe` | `transcribe`, `translate` | Must be `transcribe` or `translate` |\n| `LANGUAGE` | Source language code for the audio input | String | No | `auto` | `auto`, `en`, `es`, `fr`, `de`, `it`, `pt`, `nl`, `ja`, `zh`, `ru` | Must be a supported language code |\n| `INPUT_FILE` | Path to the input `.wav` audio file | String | Yes | N/A | Any valid file path to a `.wav` file | File must exist and be accessible |\n| `OUTPUT_FILE`| Base name for the output files (without extension) | String | Yes | `output` | Any valid file name | Should not contain invalid characters |\n\n### Secrets\n\n| Secret Name | Description | Type | Required | Default Value | Constraints | Notes |\n|-------------|-----------------------------------------|--------|----------|---------------|----------------|---------------------------------------------------------------|\n| HF_TOKEN | Hugging Face API token for model access | String | Yes | None | Valid API Token | Obtain from your Hugging Face account settings |\n\n### Input Files\n\n| File/Folder | Description | File Type | Required | Format | Notes |\n|-----------------|-----------------------------------|-----------|----------|-------------|--------------------------------------------------|\n| `INPUT_FILE` | Input audio file for processing | `.wav` | Yes | WAV format | Path specified by `INPUT_FILE` parameter |\n\n### Output Files\n\n| File/Folder | Description | File Type | Contents | Usage |\n|----------------------|--------------------------------------|-----------|------------------------------|--------------------------------------------------|\n| `OUTPUT_FILE.srt` | Transcribed subtitles in SRT format | `.srt` | Transcribed text with timings | Use with video players to display subtitles |\n| `OUTPUT_FILE.json` | Transcription data in JSON format | `.json` | Detailed transcription data | For programmatic access and data analysis |\n\n## Tutorial\n\n### How to run this component as docker\n\nBuild the dockerfile.\n\n``` bash\ndocker build -t odtp-pyannote-whisper .\n```\n\nThen create `.env` file similar to `.env.dist` and fill the variables values. Like on this example:\n\n```\nMODEL=base\nHF_TOKEN=hf_xxxxxxxxxxx\nTASK=transcribe\nINPUT_FILE=HRC_20220328T0000.mp4\nOUTPUT_FILE=HRC_20220328T0000\nVERBOSE=TRUE\n```\n\nThen create 3 folders: \n\n- `odtp-input`, where your input data should be located.\n- `odtp-output`, where your output data will be stored.\n- `odtp-logs`, where the logs will be shared. \n\nAfter this, you can run the following command and the pipeline will execute.\n\n``` bash\ndocker run -it --rm \\\n-v {PATH_TO_YOUR_INPUT_VOLUME}:/odtp/odtp-input \\\n-v {PATH_TO_YOUR_OUTPUT_VOLUME}:/odtp/odtp-output \\\n-v {PATH_TO_YOUR_LOGS_VOLUME}:/odtp/odtp-logs \\\n--env-file .env \\\nodtp-pyannote-whisper\n```\n\n### Development Mode\n\nTo run the component in development mode, mount the app folder inside the container:\n\n``` bash\ndocker run -it --rm \\\n-v {PATH_TO_YOUR_INPUT_VOLUME}:/odtp/odtp-input \\\n-v {PATH_TO_YOUR_OUTPUT_VOLUME}:/odtp/odtp-output \\\n-v {PATH_TO_YOUR_LOGS_VOLUME}:/odtp/odtp-logs \\\n-v {PATH_TO_YOUR_APP_FOLDER}:/odtp/app \\\n--env-file .env odtp-pyannote-whisper\n```\n\n### Running with GPU\n\nTo run the component with GPU support, use the following command:\n\n``` bash\ndocker run -it --rm \\\n--gpus all \\\n-v {PATH_TO_YOUR_INPUT_VOLUME}:/odtp/odtp-input \\\n-v {PATH_TO_YOUR_OUTPUT_VOLUME}:/odtp/odtp-output \\\n-v {PATH_TO_YOUR_LOGS_VOLUME}:/odtp/odtp-logs \\\n--env-file .env odtp-pyannote-whisper\n```\n\nOn Windowss this is the command to execute.\n\n``` powershell\ndocker run -it --rm `\n--gpus all `\n-v ${PWD}/odtp-input:/odtp/odtp-input `\n-v ${PWD}/odtp-output:/odtp/odtp-output `\n-v ${PWD}/odtp-logs:/odtp/odtp-logs `\n--env-file .env odtp-pyannote-whisper\n```\n\n### Running in API Mode\n\nTo run the component in API mode and expose a port, you need to use the following environment variables: \n\n```\nODTP_API_MODE=TRUE\nODTP_GRADIO_SHARE=FALSE #Only if you want to share the app via the gradio tunneling\n```\n\nAfter the configuration, you can run:\n\n``` bash\ndocker run -it --rm \\\n--platform linux/amd64 \\\n-p 7860:7860 \\\n--env-file .env \\\nodtp-pyannote-whisper \n```\n\nAnd access to the web interface on `localhost:7860` in your browser.\n\n\n\n\n## Credits and references\n\nThis component has been created using the `odtp-component-template` `v0.5.0`. \n\nThe development of this repository has been realized by SDSC.",
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+ "readme_text": "# odtp-shacl-maker\nSHACL schema maker for odtp compatible components.\n\nSHACL Maker is a command-line tool written in Python that uses the rdflib and typer libraries to convert CSV files into SHACL (Shapes Constraint Language) graphs.\n\n## Installation\nThis project uses Poetry for dependency management. If you haven't installed Poetry yet, you can do so by following the instructions on their official documentation.\n\nOnce you have Poetry installed, you can install the dependencies for this project by navigating to the project's root directory and running:\n\n```\npoetry install\n```\n\nThis will create a virtual environment and install the necessary dependencies.\n\n## Usage\nThe tool provides a command-line interface. You can run the tool with the following command:\n\n```\npoetry run python shacl-maker.py make_shacl
\n\n open-pulse-hackathon-analysis\n
\n\n Hackathon Data scraper and Analyzer\n
\n\n[](https://opensource.org/licenses/Apache-2.0)\n\n**Authors**:\n- [Eisha Tir Raazia](mailto:eisha.mazhar19@gmail.com)\n \n\n## Setup\n\n```bash\npython -m venv .venv\nsource .venv/bin/activate\npip install -r requirements.txt\n```\n\n## Usage\n\n**1. Scrape hackathon data:**\n\n```bash\npython lauzhack_scraper.py\n```\n\nOutputs: `data/lauzhack_projects.csv`, `data/lauzhack_projects.json`, `data/lauzhack_hackathons.csv`, `data/lauzhack_hackathons.json`\n\n**2. Enrich with GitHub data:**\n\n```bash\npython enrich_github_data.py --token YOUR_GITHUB_TOKEN\n```\n\nOutputs: `data/lauzhack_projects_with_github.csv`, `data/github_repos_data.json`\n\nGet your GitHub token: [github.com/settings/tokens](https://github.com/settings/tokens)\n\n## Data Fields\n\n**Projects CSV:**\n\n- Basic: year, name, awards, description, team, link, tags\n- GitHub: stars, forks, language, contributors, commit dates, README\n\n**Hackathons CSV:**\n\n- year, url, date, location, schedule\n\n## Files\n\n**`lauzhack_scraper.py`**\n\n- Scrapes LauZHack websites (2023-2025) to extract project information and hackathon metadata\n- Outputs: `data/lauzhack_projects.csv`, `data/lauzhack_projects.json`, `data/lauzhack_hackathons.csv`, `data/lauzhack_hackathons.json`\n\n**`github_extractor.py`**\n\n- Helper module with functions to extract GitHub repo data (stars, forks, commits, contributors, README)\n- Used by `enrich_github_data.py` (no need to run directly)\n\n**`enrich_github_data.py`**\n\n- Takes the scraped projects and enriches them with GitHub data for all repos\n- Outputs: `data/lauzhack_projects_with_github.csv`, `data/github_repos_data.json`\n\n## Copyright\n\nCopyright © 2025-2028 Swiss Data Science Center (SDSC),\n[www.datascience.ch](http://www.datascience.ch/). All rights reserved. The SDSC\nis jointly established and legally represented by the École Polytechnique\nFédérale de Lausanne (EPFL) and the Eidgenössische Technische Hochschule Zürich\n(ETH Zürich). This copyright encompasses all materials, software, documentation,\nand other content created and developed by the SDSC.",
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+ "readme_text": "# Open Pulse Ontology\n\nAn RDF ontology for modeling contributions to open source scientific software and research articles from EPFL organizations and researchers.\n\n## Overview\n\nThe Open Pulse Ontology provides a structured vocabulary and validation rules (SHACL shapes) for capturing metrics about:\n\n- **People**: Researchers and contributors with GitHub usernames, ORCID identifiers, and institutional affiliations\n- **Organizations**: Universities, research institutions, and companies with ROR identifiers and GitHub handles\n- **Software Repositories**: GitHub repositories with metadata, ownership, and contribution tracking\n- **Research Articles**: Scholarly publications with DOI identifiers and author information\n- **Contributions**: Individual commit-level contributions linking people to repositories\n- **Memberships**: Affiliations between people and organizations with time periods and roles\n\n## Setup\n\n### Prerequisites\n\n- Python 3.9 or higher\n- [uv](https://docs.astral.sh/uv/) (fast Python package manager)\n\n### Installation\n\n```bash\n# Install dependencies\nuv sync\n\n# Or for development (includes testing tools)\nuv sync --extra dev\n```\n\n## Validation\n\n### Running Validation\n\n```bash\n# Validate test data\nuv run python tools/python/checks/shacl.py example/test_dataset_large_example.ttl ontology-combined.ttl\n\n# Run full test suite\nuv run python tools/python/checks/test_validation.py\n```\n\n### Example Test Cases\n\nThe `example/` directory contains test files demonstrating both valid and invalid data patterns:\n\n- `test_valid_*.ttl` - Valid data conforming to the ontology\n- `test_invalid_*.ttl` - Invalid data triggering specific validation errors\n\n## SPARQL Endpoint\n\nStart the Flask application to query data via SPARQL:\n\n```bash\npython main.py\n```\n\nThe endpoint accepts SPARQL queries at `/sparql`.\n\n## License\n\nCC-BY-4.0\n\n## Version\n\nCurrent version: v2.0.1",
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Furthermore, Open Pulse segments repositories by type and discipline, allowing exploration of metrics related to specific research communities.\n\nThe platform brings all the data together into **three complementary components** to help make such community contributions visible and actionable:\n\n| **Component** | **What it’s for?** | **Think of it as…** | **Powered by…** |\n|----------------|--------------------|----------------------|------------------|\n| **Development metrics** | To keep an eye on how active and healthy a software or research community is. | A live dashboard showing who's contributing, how often, and what they're doing — kind of like tracking the *pulse* of a community. | **GrimoireLab**, a set of open-source tools from the Linux Foundation. |\n| **Community network** | To explore relationships between things like code repositories, contributors, and organizations. | A tool to answer questions like “Which people are working across multiple projects?” or “How are different organizations connected?” | **Neo4j**, a graph database, and **Cypher**, a language for querying graphs (like SQL but for networks). |\n| **Repositories metadata** | To store and query semantic metadata about repositories — like what field of science they belong to, what license they use, or how FAIR (Findable, Accessible, Interoperable, Reusable) they are. | A smart filing cabinet that lets you ask complex questions like “Show me all repositories in biology that use an MIT License and have a Docker image.” | **Tentris**, a database for linked data, and **SPARQL**, a query language for this type of data. |\n\nBy combining these layers, **Open Pulse** provides a foundation for **evidence-based evaluation of open-source contributions**, with an emphasis on **community health, engagement, and relevance** across EPFL’s research landscape.\n\nIn this Quickstart we will show you how to start accessing this data\n\n## How to use this Quickstart?\n\nTBD: You can run this quickstart in Renku or in local by using the dockerimage we provide\n\n### Development metrics \n\nThe standard for community metrics are the CHAOSS metrics.\n\n> **Disclaimer:** We originally intended to deploy a **GrimoireLab** instance for this session. However, due to technical issues, it will not be available for the hackathon. Instead, we offer **OSS Insight** as a powerful alternative to analyze repository metrics and community health.\n\nYou can get started with the **OSS Insight Quickstart** located in `src/ossinsight-quickstart/`.\n- **Notebook:** `src/ossinsight-quickstart/quickstart.ipynb`\n- **Guide:** `src/ossinsight-quickstart/README.md`\n\nThis quickstart maps OSS Insight API data to CHAOSS metrics, allowing you to visualize:\n- Project Growth (Stars)\n- Development Activity (Commits, PRs)\n- Community Responsiveness (Issues)\n- Contributor Diversity (Geography, Organizations)\n\n\n\n**Resources:**\n- **CHAOSS:** [Website](https://chaoss.community/) | [Metrics Knowledge Base](https://chaoss.community/kbtopic/all-metrics/)\n- **OSS Insight:** [Website](https://ossinsight.io/) | [API Docs](https://ossinsight.io/docs/api)\n- **Example:** [DeepLabCut Analysis](https://ossinsight.io/analyze/DeepLabCut/DeepLabCut#overview) \n\n### Community Network\n\nRelationships are structure in a property-graph and accessed via Cypher Queries.\n\nYou can explore the graph via the Neo4J User Interface of via the `neo4j-quickstart` notebook or script.\n\n- Code there allows you to run CYPHER queries, remember you can explore [graph patterns (such as shortest path between 2 nodes)](https://neo4j.com/docs/cypher-manual/current/patterns/).\n- You can download into a pandas dataframe part of the graph. The Graph being huge, we do not recommend reading the entire thing into Pandas.\n- Visualization code is also made available for the graphs. \n\n\n\nLink to Neo4j Open Pulse Instance: \n- Neo4J Browser: http://128.178.219.51:7503\n- Neo4J Bolt: http://128.178.219.51:7504\nDocumentation: https://neo4j.com/docs/\nCypher Tutorial: https://neo4j.com/docs/cypher-manual/current/appendix/tutorials/\n\n### Repositories Metadata\n\nMetadata and semantic properties are stored in a RDF-compatible database based on the Imaging Plaza Ontology (ORD Project). We can query its results with SPARQL queries.\n\nYou can query the repositories' metadata via the quickstart notebook of the `tentris-quickstart`\nYou can also combine the queries to tentris about the extracted data with queries to WikiData for more complex information extraction. (see examples in notebook)\n\n\n\nLink to Tentris Open Pulse: http://128.178.219.51:7502/ui\nDocumentation: https://docs.tentris.io/\nSPARQL Tutorial: https://sparql.dev/\n\n## Credits\n\nSDSC",
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+ "readme_text": "# PNEUMA RDI Hub\n\n[](https://example.com/badge-link)\n\n> [!CAUTION]\n> This project is not an official RDI Hub by the pNeuma team. It is a prototype created for the hackathon and intended to serve as inspiration. We would like to express our gratitude to the pNeuma team for their support and guidance in this endeavor.\n\nThis is an example of a hackathon project proposing a Research Data Infrastrucre (RDI) Hub prototype around the pNeuma project. The RDI is designed with simplicity in mind, a flexible structure that allows iterative development next to the community, and a low operational cost. For this reasons, the RDI takes advantage of Github techonology in order to host a catalog of research assets, provide a discussion forum, and distribute the content via github pages.\n\n- [Go here if you wanna know more about the hackathon](https://sdsc-hackathons.ch/)\n- [EPFL pNeuma project](https://open-traffic.epfl.ch)\n\n## Features of this RDI Hub\n\n- **Catalog of related assets**: These are resources cards contained in the catalog.\n- **Contributions & revision guidelines**: Guidelines TBD\n- **Automations**: Github actions\n- **Static landing page**: Page built in gh-pages.\n- **Blog**: TBD\n- **Interative serverless webapps**: TBD\n- **Tutorials**: Basic operations \n\n\nThis RDI Hub follow the FAIR principles: \n\n- Findability: It helps to find related assets via the catalog and the static landing page. \n- Accesibility: The datasets are provided in a cloud friendly format reducing the friction of access. Interactive serverless tutorials are planned to be provided.\n- Interoperability: The catalog follows the schema located in `./catalog/schemas/` and is available in machine-readable format allowing for easy interaction between tools, programming languages, and frameworks.\n- Reproducibility: The mandatory request of D.O.I. of each asset used increase the reproducibility of the results in related \n\nBesides this, we would like to add more presence to the importance of the governance. With this model the \n\n## RDI Hub Structure\n\nThis RDI Hub Catalog is organized in: \n\n- `catalog`:\n- `site`:\n\nThe structure can evolve with the time as a function of the community needs. New RDI Hub structures proposals can be made by the community via fork of this repository and requesting a pull request. \n\n\n### Catalog\n\nThis is where the catalog of assets is located. The assets available in this first prototype are: \n\n- Datasets\n- Repositories\n- Publications\n- Models\n- Webapps\n- Projects\n\nEach of these assets type has one basic metadata file requested. \n\n## How to...?\n\n### How the community can add a new research asset? \n\n1. Fork the RDI-Hub. \n2. Use the schema template located in `/catalog/schemas`\n3. Fill the template with all requested data and start a Pull Request (PR) to this repository. \n4. This will open a discussion thread with the community and moderators of the hub. \n5. The PR approval will trigger a github action, validating the item added.\n6. The action will render the new information in the page\n\n#### How to add a new project to this Hub?\n\nTBD\n\n#### How to add a new datasets/model/publication to the Hub?\n\nTBD\n\n### How the community can add a new research asset type?\n\nAdding or editing a research schema is as easy a modifying one of the files located in `/catalog/schemas`\n\n### How the community can add a new webapp? \n\nMaking use of the last development in webassembly technologies. \n\nIdeas for serverless apps: \n\n- Include stlite form\n- Include jupyter lite application\n\n\n### How the page is built?\n\nTBD\n\n### How is the roadmap organized? \n\nOpen issues are registered in the repository and addressed by the community.\n\n## Development\n\n### How to test the github action build index?\n\nThe github action can be tested by pulling both branches:\n\n## Develop by\n\nDevelop by SDSC in the frame of the Open Research Data for the Sciences Hackathon in collaboration with the EPFL Open Science office. We thanks the pNeuma Team for the support during the development of this prototype.",
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+ "readme_text": "# ORD for the Sciences Hackathon - Vehicles Detection\n\n[](https://renkulab.io/v2/projects/hackathon-team-1/pneuma-vehicles-detection) [](https://colab.research.google.com/github/sdsc-ordes/ordfts-hackathon-vehicles-detection/blob/main/002_vehicles_detection.ipynb)\n [](https://github.com/sdsc-ordes/ordfts-hackathon-vehicles-detection) [](https://doi.org/10.5281/zenodo.12751861) [](https://huggingface.co/datasets/katospiegel/ordfts-hackathon-pneuma-vehicles-segmentation)\n\n\n> [!CAUTION]\n> This project is an example of a hackathon project. The quality of the data produced has not been evaluated. Its goal is to provide an example on how a dataset can be update to Hugginface. \n\nThis is an example of a hackathon project presented to `ORD for the sciences hackathon` using the openly available [pNeuma vision dataset](https://zenodo.org/records/7426506). \n\n- [Go here if you wanna know more about the hackathon](https://sdsc-hackathons.ch/)\n- [EPFL pNeuma project](https://open-traffic.epfl.ch)\n\n\n## Description\n\nThe goal of this project is to create a training dataset derived from the publicly available pNeuma Vision dataset, which contains drone footage and coordinates of vehicles. By leveraging machine learning techniques, specifically the \"Segment Anything\" model by Meta, we will accurately segment and mask the pixels corresponding to each vehicle within the footage. The resulting dataset, stored in the efficient Parquet format, will be shared on Hugging Face as a new, open-access resource for the research community. Additionally, we will document our methodology in a detailed Jupyter notebook, which will be hosted in a public GitHub repository. Our work will be registered as a derived contribution in the pNeuma RDI Hub prototype, ensuring proper attribution and fostering further research and development.\n\n\n\nDatasets created:\n\n- [pneuma-vision-parquet](https://huggingface.co/datasets/katospiegel/pneuma-vision-parquet)\n- [ordfts-hackathon-pneuma-vehicles-segmentation](https://huggingface.co/datasets/katospiegel/ordfts-hackathon-pneuma-vehicles-segmentation) (doi:10.57967/hf/3028)\n\n\n## How is structured this repository?\n\n- 001_parquet_converter.ipynb: \n - In this notebook we downloaded part of the original dataset pNeuma Vision, converted into parquet and then uploaded in Huggingface\n- 002_vehicles_detection.ipynb\n - Here we take the coordinates of each vehicles tagged, we cropped an region of interest around it, and use Segment Anything by Meta in order to segment the vehicle.\n\n\n## How to run this project in docker? \n\n```\ndocker build -t vehicles-detection . \n```\n\nAnd then run with:\n\n```\ndocker run -it --rm --gpus all --env-file .env odtp-whisperx\n```\n\n## Developed by: \n\nDeveloped by Carlos Vivar Rios (SDSC), as an example for the ORD for the sciences Hackathon.",
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\n\nTechnical Presentation
\n\n
\n\nMake technical presentations in Markdown/HTML etc.\n\nThis uses [`revealjs`](https://github.com/hakimel/reveal.js) with some\nmodifications to package to PDF and standalone HTML format. Also it includes a\ncompany `scss` file for design modifications.\n\n**This is a fork of\n[`technical-presentation`](https://github.com/gabyx/technical-presentation.git)\nwith some small modifications for SDSC.**\n\n- **See the\n [demo presentation here](https://sdsc-ordes.github.io/technical-presentation/gh-pages/demo)**.\n- **See some other\n [Rust Workshop](https://sdsc-ordes.github.io/technical-presentation/gh-pages/rust-workshop/part-1/#/title-slide)**\n [src](https://github.com/sdsc-ordes/technical-presentation/tree/feat/rust-workshop).\n- **See some other\n [C++ presentation here](https://gabyx.github.io/tech-pr-cpp-value-catergories)**\n [src](https://github.com/gabyx/tech-pr-cpp-value-catergories).\n\nAuthors:\n\n- [Gabriel Nützi](https://github.com/gabyx)\n\n> [!WARNING]\n>\n> This is the `pandoc` tooling branch, if you want a simpler version with\n> `reveal.js` only, use `main-simple`.\n\nCurrent [`revealjs`](https://github.com/hakimel/reveal.js) version: `4.6.1`\n\n## Requirements\n\n### Using [`nix`](https://nixos.org)\n\nYou can enter a development shell with\n\n```shell\nnix develop './tools/nix#default'\n```\n\nwhere all requirements are installed to start working on your first\npresentation.\n\n### DevContainer\n\nThere is a [`.devcontainer`](.devcontainer) to be run over `code` or the\n[devcontainer](https://github.com/devcontainers/cli) CLI. First build the\ncontainer:\n\n```shell\njust build-dev-container\n```\n\nor with `docker` instead of `podman`:\n\n```shell\njust container_mgr=docker build-dev-container\n```\n\n### Manual\n\nYou need the following tools:\n\n- `bash`\n- `just`\n- `rsync`\n- `watchman`\n- `npm`\n- `yarn`\n\n## Usage\n\n1. **`just init`** -> Init the `build` folder with the pinned\n [`reveal.js`](.gitmodules) source and inject some [changed files](src/mixin)\n (styles, fonts, etc.) and install dependencies inside the build folder.\n\n2. **`just watch`** -> Watch the files in [`src`](src/) and synchronize changes\n to the `build` folder. This is needed to continuously update your changes\n source files and make the browser reload the page.\n\n3. **`just present`** -> Serve the presentation in the browser and continuously\n reload the page.\n\n4. **`just package`** -> Export the presentation as HTML and PDF inside a `.zip`\n file to the [`publish`](publish) folder.\n\n5. **`just publish`** -> Build and upload the (selected) presentation to the\n `publish` **branch** in the folder `docs/gh-pages` which can then be served\n by Github pages. The\n [`.publish.yaml`](./src/presentations/presentation-1/.publish.yaml) helps in\n defining the name and output folder in `docs/gh-pages` to host multiple\n presentations alongside.\n\n## Make a Presentation\n\n- Write your presentation in Markdown in\n [`src/presentations`](src/presentations). All images and other assets needed\n by the presentation should go into the respective `assets` directory (e.g.\n [`presentation-1/assets`](/src/presentations/presentation-1/assets))\n\n## Resources\n\nCheck the following resources with additional slide materials:\n\n- [Documentation](https://revealjs.com/)\n- [Markdown from a C++ presentation](https://github.com/gabyx/tech-pr-cpp-value-catergories/blob/main/files/cpp-meeting/Vortrag.md)\n [View](https://gabyx.github.io/tech-pr-cpp-value-catergories)\n\n## Modifications\n\n- Edit the design in [`company.scss`](css/theme/source/company.scss).\n\n- Company Logo: Edit the file\n [`company-logo.svg`](css/theme/source/files/company-logo.svg).\n\n - Replace embedded image in [`company.scss`](css/theme/source/company.scss)\n with\n\n ```shell\n just bake-logo\n ```",
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+ "readme_text": "# text-contextifyer\n\nTurn plain Markdown into enriched Markdown with ontology-based hyperlinks. This tool supports small-medium sized ontologies only right now, as they need to be loaded into memory. \n\n## Features\n- Load an RDF/OWL ontology from any SPARQL 1.1 compliant endpoint (Apache Jena, GraphDB, Virtuoso, etc.)\n- Extract labels (`rdfs:label`, `skos:prefLabel`)\n- Match words in Markdown text against ontology terms (fuzzy or exact)\n- Replace matches with hyperlinks in the markdown file supplied\n- Support for both named graphs and default graph queries\n\n## Example\n\nInput:\n```markdown\nComputer science and Geology are fascinating fields.\n```\nOutput:\n```markdown\n[Computer science](someurl-about-computerscience.org) and [geology](some-otherurl-related-to-geology.org) are fascinating fields.\n```\n\n## Usage\n\nFirst, make sure your SPARQL endpoint is running and create a `.env` file based on `.env.dist` with your configuration.\n\n### Configuration\n\nThe application supports any SPARQL 1.1 compliant endpoint. Configure your `.env` file:\n\n```bash\n# Required: Your SPARQL endpoint URL\nONTOLOGY_SPARQL_ENDPOINT=http://localhost:3030/myDataset/sparql # Apache Jena example\n# or\nONTOLOGY_SPARQL_ENDPOINT=http://localhost:7200/repositories/myRepo # GraphDB example\n\n# Optional: Authentication (if required)\nSPARQL_USERNAME=admin\nSPARQL_PASSWORD=password\n\n# Graph selection (choose one):\n# Option 1: Query specific named graphs\nNAMED_GRAPHS=http://example.org/graph1,http://example.org/graph2\n\n# Option 2: Query the default graph (leave NAMED_GRAPHS empty)\nUSE_DEFAULT_GRAPH=true\n```\n\n### Running Locally\n\nTo run tests:\n```bash\npoetry install\npytest\n```\n\nTo start the microservice locally:\n```bash\nPYTHONPATH=src poetry run uvicorn text_contextifyer.api.main:app --reload\n```\n\n### Running with Docker\n\n1. Build the Docker image:\n```bash\ndocker build -t text-contextifyer:latest .\n```\n\n2. Run the container (choose one of the following methods):\n\n **Option A: Using host network mode (Linux - Recommended)**\n \n If your SPARQL endpoint is running on your host machine (e.g., `localhost:3030`), use host network mode:\n ```bash\n docker run --rm --network host --env-file .env -e PORT=8001 text-contextifyer:latest\n ```\n \n The service will be available at `http://localhost:8001`\n \n Note: With `--network host`, you need to specify a custom `PORT` if 8000 is already in use.\n\n **Option B: Using port mapping (Mac/Windows)**\n \n For Mac/Windows, use `host.docker.internal` to access host services:\n ```bash\n docker run --rm -p 8001:8000 --env-file .env \\\n -e ONTOLOGY_SPARQL_ENDPOINT=http://host.docker.internal:3030/arema/sparql \\\n text-contextifyer:latest\n ```\n \n The service will be available at `http://localhost:8001`\n\nThe API documentation will be available at the URL shown above with `/docs` appended (e.g., `http://localhost:8001/docs`)\n\n### Testing the API\n\nOnce the service is running, you can test it with curl (make sure the ontology you point to contains labels that appear in the text you are contextifying):\n```bash\n# Adjust port number if you used a different port\ncurl -X POST http://localhost:8001/contextify \\\n -H \"Content-Type: application/json\" \\\n -d '{\"markdown\":\"Computer science and Geology are fascinating fields.\"}'\n```",
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\n\n tripsu\n
\n
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