The datadaemon extension for CKAN enables the loading of RDF (Resource Description Framework) data and its storage as a CKAN resource. This functionality allows users to integrate semantic web data directly into their CKAN catalogs, making it accessible and manageable alongside other datasets. By bridging the gap between RDF data and CKAN's data management capabilities, the extension streamlines the process of incorporating structured data into open data portals. Key Features: RDF Data Loading: Facilitates the upload and ingestion of RDF data into CKAN. Resource Creation: Automatically creates CKAN resource(s) based on the uploaded RDF data. Data Storage: Stores the loaded RDF data as a standard CKAN resource, making it discoverable and accessible through CKAN's standard APIs and user interface. Integration of Semantic Web Data: Allows for integration of semantic web data facilitating the addition of schema.org, DCAT or other standard vocabulary. Technical Integration: Due to the limited information provided in the README, the precise integration details with CKAN are unclear. However, it can be inferred that the extension likely leverages CKAN's plugin architecture to add new functionalities related to RDF data handling. This likely involves utilizing CKAN's API for resource creation and management. Benefits & Impact: The datadaemon extension allows users to consolidate RDF data within their CKAN instances. This means enriched data catalogs and more useful data management.

Cityscapes is a dataset consisting of diverse urban street scenes across 50 different cities at varying times of the year as well as ground truths for several vision tasks including semantic segmentation, instance level segmentation (TODO), and stereo pair disparity inference.

For segmentation tasks (default split, accessible via 'cityscapes/semantic_segmentation'), Cityscapes provides dense pixel level annotations for 5000 images at 1024 * 2048 resolution pre-split into training (2975), validation (500) and test (1525) sets. Label annotations for segmentation tasks span across 30+ classes commonly encountered during driving scene perception. Detailed label information may be found here: https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/helpers/labels.py#L52-L99

Cityscapes also provides coarse grain segmentation annotations (accessible via 'cityscapes/semantic_segmentation_extra') for 19998 images in a 'train_extra' split which may prove useful for pretraining / data-heavy models.

Besides segmentation, cityscapes also provides stereo image pairs and ground truths for disparity inference tasks on both the normal and extra splits (accessible via 'cityscapes/stereo_disparity' and 'cityscapes/stereo_disparity_extra' respectively).

Ingored examples:

• For 'cityscapes/stereo_disparity_extra':
  • troisdorf_000000_000073_{*} images (no disparity map present)

WARNING: this dataset requires users to setup a login and password in order to get the files.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('cityscapes', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

PKT Human Disease KG Benchmark Builds

The PheKnowLator (PKT) Human Disease KG (PKT-KG) was built to model mechanisms of human disease, which includes the Central Dogma and represents multiple biological scales of organization including molecular, cellular, tissue, and organ. The knowledge representation was designed in collaboration with a PhD-level molecular biologist (Figure).

The PKT Human Disease KG was constructed using 12 OBO Foundry ontologies, 31 Linked Open Data sets, and results from two large-scale experiments (Supplementary Material). The 12 OBO Foundry ontologies were selected to represent chemicals and vaccines (i.e., ChEBI and Vaccine Ontology), cells and cell lines (i.e., Cell Ontology, Cell Line Ontology), gene/gene product attributes (i.e., Gene Ontology), phenotypes and diseases (i.e., Human Phenotype Ontology, Mondo Disease Ontology), proteins, including complexes and isoforms (i.e., Protein Ontology), pathways (i.e., Pathway Ontology), types and attributes of biological sequences (i.e., Sequence Ontology), and anatomical entities (Uberon ontology). The RO is used to provide relationships between the core OBO Foundry ontologies and database entities.

The PKT Human Disease KG contained 18 node types and 33 edge types. Note that the number of nodes and edge types reflects those that are explicitly added to the core set of OBO Foundry ontologies and does not take into account the node and edge types provided by the ontologies. These nodes and edge types were used to construct 12 different PKT Human Disease benchmark KGs by altering the Knowledge Model (i.e., class- vs. instance-based), Relation Strategy (i.e., standard vs. inverse relations), and Semantic Abstraction (i.e., OWL-NETS (yes/no) with and without Knowledge Model harmonization [OWL-NETS Only vs. OWL-NETS + Harmonization]) parameters. Benchmarks within the PheKnowLator ecosystem are different versions of a KG that can be built under alternative knowledge models, relation strategies, and with or without semantic abstraction. They provide users with the ability to evaluate different modeling decisions (based on the prior mentioned parameters) and to examine the impact of these decisions on different downstream tasks.

The Figures and Tables explaining attributes in the builds can be found here.

Build Data Access

Important Build Information

The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (here). Note that we have developed this Zenodo-based archive for the builds. While the original GCP resources contained all of the resources needed to generate the builds, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the logs associated with each build.

🗂 For additional information on the KG file types please see the following Wiki page, which is also available as a download from this repository (PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx).

v1.0.0

• KGs: https://zenodo.org/doi/10.5281/zenodo.7030200
• Embeddings: https://zenodo.org/doi/10.5281/zenodo.7030188

All Other Build Versions

Class-based Builds

Standard Relations

• OWL Build
  • v2.0.0: MAY2020; JAN2021; FEB2021
  • v2.1.0: MAY2021; JUN2021; JUL2021; AUG2021; SEP2021
  • v3.0.2: OCT2021; NOV2021
• OWL-NETS Build
  • v2.0.0: MAY2020; JAN2021; FEB2021
  • v2.1.0: MAY2021; JUN2021; JUL2021; AUG2021; SEP2021
  • v3.0.2: OCT2021; NOV2021

Inverse Relations

• OWL Build
  • v2.0.0: MAY2020; JAN2021; FEB2021
  • v2.1.0: MAY2021; JUN2021; JUL2021; AUG2021; SEP2021
  • v3.0.2: OCT2021; NOV2021
• OWL-NETS Build
  • v2.0.0: MAY2020; JAN2021; FEB2021
  • v2.1.0: MAY2021; JUN2021; JUL2021; AUG2021; SEP2021
  • v3.0.2: OCT2021; NOV2021

Instance-based Builds

Standard Relations

• OWL Build
  • v2.0.0: MAY2020; JAN2021; FEB2021
  • v2.1.0: MAY2021; JUN2021; JUL2021; AUG2021; SEP2021
  • v3.0.2: OCT2021; NOV2021
• OWL-NETS Build
  • v2.0.0: MAY2020; JAN2021; FEB2021
  • v2.1.0: MAY2021; JUN2021; JUL2021; AUG2021; SEP2021
  • v3.0.2: OCT2021; NOV2021

Inverse Relations

• OWL Build
  • v2.0.0: MAY2020; JAN2021; FEB2021
  • v2.1.0: MAY2021; JUN2021; JUL2021; AUG2021; SEP2021
  • v3.0.2: OCT2021; NOV2021
• OWL-NETS Build
  • v2.0.0: MAY2020; JAN2021; FEB2021
  • v2.1.0: MAY2021; JUN2021; JUL2021; AUG2021; SEP2021
  • v3.0.2: OCT2021; NOV2021