Persistent Identifiers to Subsets of TCIA Data
To facilitate data sharing, many publications encourage authors to include data citations to the data that the authors used in creating the results described in their scholarly papers. In addition, new journals are now available for describing data collections outright. As a service to the community, TCIA now has the ability to create persistent identifiers to subsets of data held within TCIA that authors may use as data citations in their scholarly papers.
TCIA uses the DataCite system to manage these references. DataCite leverages the Digital Object Identifier (DOI) infrastructure, which is widely used in citing scholarly articles. TCIA users may request that a DOI be created for subsets of data stored within TCIA.
To request a DOI for a subset of data, the TCIA user must first identify the subset of data that will be referenced by the DOI. The best way for a user to identify this subset of data is to create a shared list using TCIA's NBIA web application. By definition, only publicly available data may be included in the shared list. Creating a DOI to private data is not allowed. Currently DOIs created by TCIA may only reference static (unchanging) subsets of data. In other words, if someone changes the content of the shared list, this will not be picked up by existing DOIs created from that shared list.
Once a user has create a shared list, they may request that a DOI be created by sending an e-mail message to TCIA's help desk with the following information:
- Requestor - The name and e-mail address of the person with whom TCIA will coordinate in creating the DOI, which defaults to the user submitting the request to the help desk.
- Shared List Name - The name of the shared list that identifies the data that will be the subject of the DOI.
- DOI Name - The name to be given to this DOI (part of the citation), which may be the same as the shared list name (the default).
- Authors - The names of the authors who wish to be associated with the DOI, which defaults to the Requestor.
- Description - A description (500 characters or less) of the data subset. We suggest the description include why the authors created the subset of data.
- Publication Date - The requested publication date, which is a point or period of time important to this subset of data, for example the time period when the subset of data was actively being used by the authors. Please choose today's date for "As Soon as Possible". The date may be a range of dates separated by a dash ("-") character, or a list of dates separated by a comma (,) character. A data range may be open ended (i.e. no date following the dash). Please use the tilde ("~") character for approximate dates.
Once TCIA validates the request, they will create a DOI landing page for the citation and associate a DOI with that landing page. The landing page will include a link that allows readers to directly download the subset of data cited. The help desk will then inform the requestor when the new DOI is ready.
Recent space activity
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To enhance the value of TCIA collections we encourage the research community to publish their analyses of existing TCIA image collections. Examples of this kind of data includes radiologist or pathologist annotations, image classifications, segmentations, radiomics features, or derived/reprocessed images. Similar to submitting new image collections, these data are reviewed by the TCIA Advisory Group for relevance and curated using our normal processes to assure data are de-identified. However, TCIA does not certify the quality of the analyses themselves (e.g. accuracy of segmentation on a given scan). Researchers should always carefully review the data and any related publications before deciding whether these analyses could be useful in their work.
Note: Column headers can be clicked to sort the table.
Analysis Artifacts on TCIA
|DICOM SR of clinical data and measurement for breast cancer collections to TCIA||Breast||Breast||474||TCGA-BRCA, BREAST-DIAGNOSIS, ISPY1, Breast-MRI-NACT-Pilot||DICOM SR descriptions of patient characteristics, histopathology, receptor status and clinical findings including measurements.||2020-05-28|
|Glioblatoma, Low Grade Glioma||Brain||167||TCGA-GBM, TCGA-LGG||Tumor segmentations||2020-04-30|
|Integration of CT-based Qualitative and Radiomic Features with Proteomic Variables in Patients with High-Grade Serous Ovarian Cancer: An Exploratory Analysis||Ovarian||Ovary||20||TCGA-OV||Radiologist assessments of image features, proteogenomic features||2020-04-15|
|Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines||Lung||Lung||402||NSCLC-Radiomics||Thoracic segmentations, pleural effusion segmentations, image features||2020-04-08|
|Lung||Chest||1,010||LIDC-IDRI||Tumor segmentations, image features||2020-03-26|
|Lung, Head-Neck||Lung, Head-Neck||701||NSCLC-Radiomics, NSCLC-Radiomics-Genomics, Head-Neck-Radiomics-HN1, NSCLC-Radiomics-Interobserver1, RIDER Lung CT||Tumor segmentations and radiomic features||2020-03-23|
|Lung||Chest||31||RIDER Lung CT||Tumor segmentations||2020-02-13|
|Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images||Various (14 collections)||Various (14 collections)||Various (14 TCGA collections)||Nuclei segmentations||2020-02-08|
|Head and Neck Squamous Cell Carcinoma||Head-Neck||215||HNSCC||Radiation Therapy Structures||2019-07-11|
|Breast, Glioblastoma||Breast, Brain||516||ISPY1, BREAST-DIAGNOSIS, Breast-MRI-NACT-Pilot, TCGA-BRCA, Ivy GAP||Standardized (SDTM format) conversions of clinical and image analysis data||2019-06-21|
|Various (13 collections)||Various (13 collections)||324||Various (13 collections)||Lesion measurements||2019-05-30|
|Lung||Chest||31||Lung Phantom, LIDC-IDRI, QIN LUNG CT, RIDER Lung CT||Tumor segmentations||2018-12-18|
|Various (13 collections)||Various (13 collections)||4,759||Various (13 TCGA collections)||Deep learning based computational stain for staining tumor-infiltrating lymphocytes (TILs)||2018-12-17|
|Breast||Breast||84||TCGA-BRCA||Radiologist assessments of image features, lesion segmentations, radiomic features, and multi-gene assays||2018-09-04|
|Lung Adenocarcinoma, Renal Clear Cell, Liver, Ovarian||Chest, Kidney, Liver, Ovary||352||TCGA-LUAD, TCGA-KIRC, TCGA-LIHC, TCGA-OV||Lesion measurements||2018-05-17|
|Lung Adenocarcinoma||Chest||40||LungCT-Diagnosis, QIN LUNG CT||Tumor segmentations and radiomic image features||2017-08-11|
|Glioblastoma||Brain||135||TCGA-GBM||Tumor segmentations and radiomic image features||2017-07-17|
|Low Grade Glioma||Brain||108||TCGA-LGG||Tumor segmentations and radiomic image features||2017-07-17|
|Low Grade Glioma||Brain||188||TCGA-LGG||Radiologist assessments of image features, tumor segmentations||2017-03-17|
|Colon, Lung, Breast, Glioblastoma||Colon, Chest, Breast, Brain||40||CT COLONOGRAPHY, LIDC-IDRI, TCGA-BRCA, TCGA-GBM||N/A||2016-12-08|
|High-Grade Serous Ovarian Cancer||Ovary||93||TCGA-OV||Radiologist assessments of image features, genomic subtypes||2016-08-02|
|Prostate||Prostate||80||Prostate structure segmentations||2015-08-20|
|Renal Clear Cell Carcinoma||Kidney||103||TCGA-KIRC||Radiologist assessments of image features||2015-05-28|
Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset
|Glioblastoma||Brain||75||TCGA-GBM||Radiologist assessments of image features||2014-11-12|
|Glioblastoma||Brain||45||TCGA-GBM||Radiologist assessments of image features and hemodynamic parameters||2014-07-24|