GITBOOK-362: change request with no subject merged in GitBook

core-functions-of-idc.md

@@ -1,27 +1,52 @@
 # Core functions
 
-## Cloud-based analysis
+## Easy and efficient access to public cancer imaging data
 
-* Connect cancer researchers with the data and compute resources co-located on the cloud.
-* Enable building of imaging-focused cohorts for cross-omics analyses.
+We ingest and distribute datasets from variety of sources and contributors, primarily focusing on large data collection initiatives sponsored by US National Cancer Institute. At this time, we do not have resources to prioritize receipt of the imaging data from individual PIs (but we are encouraging submissions of annotations for existing IDC data!). Nevertheless, if you feel you might have a compelling dataset, please email us at [[email protected]](https://mail.google.com/mail/?view=cm\&fs=1\&tf=1\&[email protected]).
 
-## FAIR science
+On ingestion, we harmonize images and image-derived data into DICOM format for interoperability, whenever data is represented in a non-DICOM format.
 
-* Implement and refine FAIR (Findable Accessible Interoperable Reusable) data stewardship principles as applied to cancer imaging research.
-* Apply DICOM standard to harmonize imaging and image-derived cancer imaging data.
-* Define best practices and worked out examples for using and generating FAIR data.
+Upon conversion, the data undergoes Extract-Transform-Load (ETL), which extracts DICOM metadata to make the data searchable, ingests the DICOM files into public S3 storage buckets and a DICOMweb store. Once the data is released, we provide various interfaces to access data and metadata.
 
-## Use cases
+{% embed url="https://docs.google.com/presentation/d/1UVpNVyVy3xIYLDnm4rtgAUmSu-uKQo5krekI9DSMT8o/edit#slide=id.p" %}
+Schematic summary of the IDC data ingestion and release process.
+{% endembed %}
 
-* Apply the developed technology to solve meaningful research problems in radiology, digital pathology, and other domains.
-* Develop examples of harmonization of images, image-derived, and image-related data.
+## Tools to simplify the use of the data
 
-## Open source
+We are actively developing a variety of capabilities to make it easier for the users to work with the data in IDC. Some of the examples of those tools include
 
-* Simplify access to open source image computing tools.
-* Support adoption of IDC-developed tools.
+* [IDC Portal](https://portal.imaging.datacommons.cancer.gov/explore/) provides interactive browser-based interface for exploration of IDC data
+* we are the maintainers of [Slim](https://github.com/ImagingDataCommons/slim) - an open-source viewer of DICOM digital pathology images; Slim is integrated with IDC Portal for visualizing pathology images and image-derived data available in IDC
+* we are actively contributing to the [OHIF Viewer](https://github.com/OHIF/Viewers), and rely on it for visualizing radiology images and image-derived data
+* [`idc-index`](https://github.com/ImagingDataCommons/idc-index) is a python package that provides convenience functions for accessing IDC data, including efficient download from IDC public S3 buckets
+* we are contributing to a variety of tools that aim to simplify the use of DICOM in cancer imaging research; these include [OpenSlide](https://openslide.org/formats/dicom/) and [BioFormats bfconvert](https://bio-formats.readthedocs.io/en/v7.3.1/formats/dicom.html) library that can be used for conversion between DICOM Whole Slide Imaging (WSI) format and other slide microscopy formats, [dcmqi](https://github.com/QIICR/dcmqi) library for converting image analysis results to and from DICOM representation
 
-## Community building
+{% embed url="https://docs.google.com/presentation/d/13NQKWfauODArO4A6BrxJNZQODKoq5tJEQ0xg_Dvji7s/edit?usp=sharing" %}
+Although IDC data is stored in DICOM format, it can be converted into alternative research representations using open-source tools. 
+{% endembed %}
+
+## Support of continuous enrichment of data
+
+We welcome you to apply to contribute analysis results and annotations of the images available in IDC! These can be expert manual annotations, analysis results generated using AI tools, segmentations, contours, metadata attributes describing the data (e.g., annotation of the scan type), expert evaluation of the quality of existing AI-generated annotations in IDC.
+
+If you would like your annotations/analysis results to be considered, you must establish the value of your contribution (e.g., describe the qualifications of the experts performing manual annotations, demonstrate robustness of the AI tool you are applying to images with a peer-reviewed publication or other type of evidence), and be willing to share your contribution under a permissive Creative Commons Attribution [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/deed.en). 
+
+See more details on our curation policy [here](https://zenodo.org/communities/nci-idc/curation-policy), and reach out by sending email to [[email protected]](https://mail.google.com/mail/?view=cm\&fs=1\&tf=1\&[email protected]) with any questions or inquries. Every application will be reviewed by IDC stakeholders.
+
+If your contribution is accepted by the IDC stakeholders:
+
+* we will work with you to choose the appropriate DICOM object type for your data and convert it into DICOM representation
+* upon conversion, we will create a Zenodo entry under the[NCI Imaging Data Commons Zenodo community](https://zenodo.org/communities/nci-idc/) for your contribution so that you get the Digital Object Identifier (DOI), citation and recognition of your contribution
+* once published in IDC
+  * your data will become searchable and viewable in IDC Portal, so it is easier for the users of your data to discover and work with your data
+  * files can be downloaded very efficiently using S3 interface and `idc-index`
+
+## Integration of cancer imaging data with other components of CRDC
+
+IDC is a component of the broader NCI [Cancer Research Data Commons (CRDC)](https://datacommons.cancer.gov/), giving you access to the following:
+
+* [Cancer Data Aggregator (CDA)](https://cda.readthedocs.io/en/latest/) can be used to find data related to the images in IDC in [Genomics Data Commons](https://portal.gdc.cancer.gov/), [Proteomics Data Commons](https://pdc.cancer.gov/pdc/) and [Integrated Canine Data Commons](https://caninecommons.cancer.gov/)
+* Broad [FireCloud](https://firecloud.terra.bio/) and [Seven Bridges Cancer Genimics Cloud](https://www.cancergenomicscloud.org/) (SB-CGC) can be used to apply analysis tools to the data in IDC (you can read more about how this can be done in [this preprint](https://doi.org/10.21203/rs.3.rs-4351526/v1) from the IDC team)
+* [MHub.AI](https://mhub.ai/) platform curates a growing number of cancer imaging AI models that can be applied directly to the DICOM data available in IDC
 
-* Identify and address unmet needs of the cancer imaging community.
-* Promote openness, support, and education.

idc-team.md

@@ -9,9 +9,7 @@ Our team includes the following sites and project leads:
 * Brigham and Women's Hospital, Boston, MA, USA (BWH)
   * Andrey Fedorov, PhD, and Ron Kikinis, MD - Co-PIs of the project
   * Hugo Aerts, PhD
-  * Dennis Bontempi
   * Cosmin Ciausu, MS
-  * Vamsi Thiriveedhi, MS
   * Deepa Krishnaswamy, PhD
   * Katie Mastrogiacomo
   * Maria Loy
@@ -51,6 +49,7 @@ Oversight:
   * Todd Pihl - project manager
 * National Cancer Institute
   * Erika Kim - federal lead
+  * Granger Sutton - federal lead
 
 ### IDC Alumni
 
@@ -71,6 +70,8 @@ We are grateful to the following individuals who contributed to IDC in the past,
 * Rasmus Kiehl (Fraunhofer MEVIS)
 * Chad Osborne (GDIT)
 * Afshin Akbarzadeh (BWH)
+* Dennis Bontempi (BWH)
+* Vamsi Thiriveedhi (BWH)
 * Jessica Cienda (GDIT)
 * Bernard Larbi (GDIT)
 * Mi Tian (ISB)