GITBOOK-346: change request with no subject merged in GitBook

README.md

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+---
+layout:
+  title:
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+
 # Welcome!
 
-{% hint style="info" %}
-You can contact IDC support by sending email to [<mark style="color:blue;">[email protected]</mark>](mailto:[email protected]) or posting your question on [IDC User forum](https://discourse.canceridc.dev).
-{% endhint %}
+[**NCI Imaging Data Commons** **(IDC)**](https://imaging.datacommons.cancer.gov) is a cloud-based environment containing publicly available cancer imaging data co-located with analysis and exploration tools and resources. IDC is a node within the broader NCI [Cancer Research Data Commons (CRDC)](https://datacommons.cancer.gov/) infrastructure that provides secure access to a large, comprehensive, and expanding collection of cancer research data.&#x20;
 
-{% hint style="info" %}
-**“IDC Community Office Hours”** take place weekly on Google Meet at [https://meet.google.com/xyt-vody-tvb](https://meet.google.com/xyt-vody-tvb) **every Tuesday 16:30 – 17:30 (New York) and Wednesday 10:30-11:30 (New York)**. Join us to find answers to any questions you might have about IDC!
-{% endhint %}
+<figure><img src=".gitbook/assets/idc_v18_summary.jpg" alt=""><figcaption><p>Summary of the selected aspects of IDC content; see interactive dashboard <a href="https://lookerstudio.google.com/reporting/04cf5976-4ea0-4fee-a749-8bfd162f2e87/page/p_s7mk6eybqc">here</a></p></figcaption></figure>
+
+## Highlights
 
-**NCI Imaging Data Commons** **(IDC)** is a cloud-based environment containing publicly available cancer imaging data co-located with analysis and exploration tools and resources. IDC is a node within the broader NCI [Cancer Research Data Commons (CRDC)](https://datacommons.cancer.gov/) infrastructure that provides secure access to a large, comprehensive, and expanding collection of cancer research data.&#x20;
+* **>60 TB**: IDC contains radiology, brightfield (H\&E) and fluorescence slide microscopy images, along with image-derived data (annotations, segmentations, quantitative measurements) and accompanying clinical data
+* **free**: all of the data in IDC is publicly available: no registration, no access requests
+* **commercial-friendly**: >95% of the data in IDC is covered by the permissive CC-BY license, which allows commercial reuse (small subset of data is covered by the CC-NC license); each file in IDC is tagged with the license to make it easier for you to understand and follow the rules
+* **cloud-based**: all of the data in IDC is available from both Google and AWS public buckets: fast and free to download, no out-of-cloud egress fees
+* **harmonized**: all of the images and image-derived data in IDC is harmonized into standard DICOM representation
 
-IDC maintains data and makes it available for download (free egress) both in the Google GCP and Amazon AWS clouds.
+## Functionality
 
-IDC **connects** researchers with&#x20;
+IDC is as much about data as it is about what you can do with the data! We maintain and actively develop a variety of tools that are designed to help you efficiently navigate, access and analyze IDC data:
 
-1. Cancer image collections
-2. Robust infrastructure that contains imaging data, subject and sample metadata, and experimental metadata from disparate sources
-3. Resources for searching, identifying, and viewing images, and
-4. Additional data types contained in other Cancer Research Data Commons nodes (e.g., [Genomics Data Commons](https://datacommons.cancer.gov/repository/genomic-data-commons) and [Proteomic Data Commons](https://datacommons.cancer.gov/repository/proteomic-data-commons)).
+* **exploration**: start with the [IDC Portal](https://portal.imaging.datacommons.cancer.gov/explore/) to get an idea of the data available
+* **visualization**: examine images and image-derived annotations and analysis results from the convenience of your browser using integrated OHIF, VolView and Slim open source viewers
+* **cohort building**: use rich and extensive metadata to build subsets of data programmatically using SQL
+* **download**: use your favorite S3 API client, or `pip install`[`idc-index`](https://github.com/ImagingDataCommons/idc-index), to efficiently fetch any of the IDC files from our public buckets
+* **analysis**: conveniently access IDC files and metadata from the tools that are cloud-native, such as Google Colab or Looker; fetch IDC data directly into 3D Slicer using [SlicerIDCBrowser extension](https://github.com/ImagingDataCommons/SlicerIDCBrowser/)
 
 {% hint style="info" %}
 The overview of IDC is available in this open access publication. If you use IDC, please acknowledge us by citing it!

getting-started-with-idc.md

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-# Getting started
+---
+layout:
+  title:
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+  description:
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+  pagination:
+    visible: true
+---
 
-{% hint style="info" %}
-Whether you are new to the cloud, or you consider yourself an expert, we encourage you to apply for free Google cloud credits that we provide to our users to support cancer imaging research projects that work with Imaging Data Commons. All reasonable requests will receive a $300 allocation of credits that do not expire, and we will not require you to provide a credit card information to verify your identity. All you have to do is fill out and submit [this application form](https://docs.google.com/forms/d/e/1FAIpQLSfXvXqficGaVEalJI3ym6rKqarmW\_YUUWG6A4U8pclvR8MmRQ/viewform).
-{% endhint %}
+# Getting started
 
 IDC is not the place that gives you a push-button solution to your analysis needs, but aims to help you do the analyses you would usually do on your local resources, but faster, and with better reproducibility, and at scale. Our goal in the various examples is to give you a taste of what can be done with IDC data.
 

publications.md

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 See the full list, as curated by Google Scholar, [here](https://scholar.google.com/scholar?oi=bibs\&hl=en\&cites=8052604365477078213).
 {% endhint %}
 
-1. Murugesan, G. K., McCrumb, D., Aboian, M., Verma, T., Soni, R., Memon, F. & Van Oss, J. The AIMI initiative: AI-generated annotations for imaging data commons collections. _arXiv \[eess.IV]_ (2023). at <[http://arxiv.org/abs/2310.14897](http://arxiv.org/abs/2310.14897)>
-2. Kulkarni, P., Kanhere, A., Yi, P. H. & Parekh, V. S. Text2Cohort: Democratizing the NCI Imaging Data Commons with natural language cohort discovery. _arXiv \[cs.LG]_ (2023). at <[http://arxiv.org/abs/2305.07637](http://arxiv.org/abs/2305.07637)> &#x20;
-3. Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C. & Ruppin, E. Big data in basic and translational cancer research. _Nat. Rev. Cancer_ 22, 625–639 (2022). [http://dx.doi.org/10.1038/s41568-022-00502-0](http://dx.doi.org/10.1038/s41568-022-00502-0)
-4. Schapiro, D., Yapp, C., Sokolov, A., Reynolds, S. M., Chen, Y.-A., Sudar, D., Xie, Y., Muhlich, J., Arias-Camison, R., Arena, S., Taylor, A. J., Nikolov, M., Tyler, M., Lin, J.-R., Burlingame, E. A., Human Tumor Atlas Network, Chang, Y. H., Farhi, S. L., Thorsson, V., Venkatamohan, N., Drewes, J. L., Pe’er, D., Gutman, D. A., Herrmann, M. D., Gehlenborg, N., Bankhead, P., Roland, J. T., Herndon, J. M., Snyder, M. P., Angelo, M., Nolan, G., Swedlow, J. R., Schultz, N., Merrick, D. T., Mazzili, S. A., Cerami, E., Rodig, S. J., Santagata, S. & Sorger, P. K. MITI minimum information guidelines for highly multiplexed tissue images. _Nat. Methods_ 19, 262–267 (2022). [http://dx.doi.org/10.1038/s41592-022-01415-4](http://dx.doi.org/10.1038/s41592-022-01415-4)
-5. Wahid, K. A., Glerean, E., Sahlsten, J., Jaskari, J., Kaski, K., Naser, M. A., He, R., Mohamed, A. S. R. & Fuller, C. D. Artificial intelligence for radiation oncology applications using public datasets. _Semin. Radiat. Oncol._ 32, 400–414 (2022). [http://dx.doi.org/10.1016/j.semradonc.2022.06.009](http://dx.doi.org/10.1016/j.semradonc.2022.06.009)
-6. Hartley, M., Kleywegt, G. J., Patwardhan, A., Sarkans, U., Swedlow, J. R. & Brazma, A. The BioImage Archive - Building a Home for Life-Sciences Microscopy Data. _J. Mol. Biol._ 167505 (2022). doi:10.1016/j.jmb.2022.167505 [http://dx.doi.org/10.1016/j.jmb.2022.167505](http://dx.doi.org/10.1016/j.jmb.2022.167505)
-7. Diaz-Pinto, A., Alle, S., Nath, V., Tang, Y., Ihsani, A., Asad, M., Pérez-García, F., Mehta, P., Li, W., Flores, M., Roth, H. R., Vercauteren, T., Xu, D., Dogra, P., Ourselin, S., Feng, A. & Cardoso, M. J. MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images. _arXiv \[cs.HC]_ (2022). at <[http://arxiv.org/abs/2203.12362](http://arxiv.org/abs/2203.12362)>&#x20;
-8.
+1. Pai, S., Bontempi, D., Hadzic, I., Prudente, V., Sokač, M., Chaunzwa, T. L., Bernatz, S., Hosny, A., Mak, R. H., Birkbak, N. J. & Aerts, H. J. W. L. Foundation model for cancer imaging biomarkers. _Nature Machine Intelligence_ 6, 354–367 (2024). [https://www.nature.com/articles/s42256-024-00807-9](https://www.nature.com/articles/s42256-024-00807-9)
+2. Murugesan, G. K., McCrumb, D., Aboian, M., Verma, T., Soni, R., Memon, F. & Van Oss, J. The AIMI initiative: AI-generated annotations for imaging data commons collections. _arXiv \[eess.IV]_ (2023). at <[http://arxiv.org/abs/2310.14897](http://arxiv.org/abs/2310.14897)>
+3. Kulkarni, P., Kanhere, A., Yi, P. H. & Parekh, V. S. Text2Cohort: Democratizing the NCI Imaging Data Commons with natural language cohort discovery. _arXiv \[cs.LG]_ (2023). at <[http://arxiv.org/abs/2305.07637](http://arxiv.org/abs/2305.07637)> &#x20;
+4. Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C. & Ruppin, E. Big data in basic and translational cancer research. _Nat. Rev. Cancer_ 22, 625–639 (2022). [http://dx.doi.org/10.1038/s41568-022-00502-0](http://dx.doi.org/10.1038/s41568-022-00502-0)
+5. Schapiro, D., Yapp, C., Sokolov, A., Reynolds, S. M., Chen, Y.-A., Sudar, D., Xie, Y., Muhlich, J., Arias-Camison, R., Arena, S., Taylor, A. J., Nikolov, M., Tyler, M., Lin, J.-R., Burlingame, E. A., Human Tumor Atlas Network, Chang, Y. H., Farhi, S. L., Thorsson, V., Venkatamohan, N., Drewes, J. L., Pe’er, D., Gutman, D. A., Herrmann, M. D., Gehlenborg, N., Bankhead, P., Roland, J. T., Herndon, J. M., Snyder, M. P., Angelo, M., Nolan, G., Swedlow, J. R., Schultz, N., Merrick, D. T., Mazzili, S. A., Cerami, E., Rodig, S. J., Santagata, S. & Sorger, P. K. MITI minimum information guidelines for highly multiplexed tissue images. _Nat. Methods_ 19, 262–267 (2022). [http://dx.doi.org/10.1038/s41592-022-01415-4](http://dx.doi.org/10.1038/s41592-022-01415-4)
+6. Wahid, K. A., Glerean, E., Sahlsten, J., Jaskari, J., Kaski, K., Naser, M. A., He, R., Mohamed, A. S. R. & Fuller, C. D. Artificial intelligence for radiation oncology applications using public datasets. _Semin. Radiat. Oncol._ 32, 400–414 (2022). [http://dx.doi.org/10.1016/j.semradonc.2022.06.009](http://dx.doi.org/10.1016/j.semradonc.2022.06.009)
+7. Hartley, M., Kleywegt, G. J., Patwardhan, A., Sarkans, U., Swedlow, J. R. & Brazma, A. The BioImage Archive - Building a Home for Life-Sciences Microscopy Data. _J. Mol. Biol._ 167505 (2022). doi:10.1016/j.jmb.2022.167505 [http://dx.doi.org/10.1016/j.jmb.2022.167505](http://dx.doi.org/10.1016/j.jmb.2022.167505)
+8. Diaz-Pinto, A., Alle, S., Nath, V., Tang, Y., Ihsani, A., Asad, M., Pérez-García, F., Mehta, P., Li, W., Flores, M., Roth, H. R., Vercauteren, T., Xu, D., Dogra, P., Ourselin, S., Feng, A. & Cardoso, M. J. MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images. _arXiv \[cs.HC]_ (2022). at <[http://arxiv.org/abs/2203.12362](http://arxiv.org/abs/2203.12362)>&#x20;
+9.
 
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