https://www.nlm.nih.gov/databases/download/terms_and_conditions.htmlhttps://www.nlm.nih.gov/databases/download/terms_and_conditions.html
This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute Imaging Data Commons (IDC) [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: NLM-Visible-Human-Project. You can use the manifests included in this Zenodo record to download the content of the collection following the Download instructions below.
The NLM Visible Human Project [2] has created publicly-available complete, anatomically detailed, three-dimensional representations of a human male body and a human female body. Specifically, the VHP provides a public-domain library of cross-sectional cryosection, CT, and MRI images obtained from one male cadaver and one female cadaver. The Visible Man data set was publicly released in 1994 and the Visible Woman in 1995.
The data sets were designed to serve as (1) a reference for the study of human anatomy, (2) public-domain data for testing medical imaging algorithms, and (3) a test bed and model for the construction of network-accessible image libraries. The VHP data sets have been applied to a wide range of educational, diagnostic, treatment planning, virtual reality, artistic, mathematical, and industrial uses. About 4,000 licensees from 66 countries were authorized to access the datasets. As of 2019, a license is no longer required to access the VHP datasets.
Courtesy of the U.S. National Library of Medicine. Release of this collection by IDC does not indicate or imply that NLM has endorsed its products/services/applications. Please see the Visible Human Project information page to learn more about the images and to obtain any supporting metadata for this collection. Note that this collection may not reflect the most current/accurate data available from NLM.
Citation guidelines can be found on the National Library of Medicine Terms and Conditions information page.
A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, collection_id-idc_v8-aws.s5cmd
corresponds to the contents of the collection_id
collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.
nlm_visible_human_project-idc_v15-aws.s5cmd
: manifest of files available for download from public IDC Amazon Web Services bucketsnlm_visible_human_project-idc_v15-gcs.s5cmd
: manifest of files available for download from public IDC Google Cloud Storage bucketsnlm_visible_human_project-idc_v15-dcf.dcf
: Gen3 manifest (for details see https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids)Note that manifest files that end in -aws.s5cmd
reference files stored in Amazon Web Services (AWS) buckets, while -gcs.s5cmd
reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.
Each of the manifests include instructions in the header on how to download the included files.
To download the files using .s5cmd
manifests:
pip install --upgrade idc-index
.s5cmd
manifest file: idc download manifest.s5cmd
.To download the files using .dcf
manifest, see manifest header.
Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.
[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. RadioGraphics (2023). https://doi.org/10.1148/rg.230180
[2] Spitzer, V., Ackerman, M. J., Scherzinger, A. L. & Whitlock, D. The visible human male: a technical report. J. Am. Med. Inform. Assoc. 3, 118–130 (1996). https://doi.org/10.1136/jamia.1996.96236280
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The iratebirds database contains comprehensive visual aesthetic attractiveness, as seen by humans, data for bird taxonomic units (following the eBird/Clements integrated checklist v. 2019). The data were collected with the iratebirds.app -website citizen science project, where users rated the appearance of birds on a linear scale from 1-10. The rating were based on photographs of the birds available from the Macaulay Library database. Each rating score of a bird species or subspecies is based on several photographs of the same bird species. The application code is openly available on GitHub: https://github.com/luomus/iratebirds The application was spread during August 2020 – April 2021, globally, to as wide audiences as possible using social media, traditional media, collaborators and email-lists.
The iratebirds database is based on 408 207 ratings from 6 212 users. It consists of raw visual aesthetic attractiveness rating data as well as complementary data from an online survey that sourced demographic information from a subset of 2 785 users who scored the birds. The online survey gives information on these users’ birding skills, nature connectedness, profession, home country, age and gender. On top of these, the data scores for birds’ visual aesthetic attractiveness to humans have been modelled with hierarchical models to obtain overall average scores for the bird species and subspecies. More details on the data are found in this file’s section “Methodological information” as well as in the publication Haukka, A. et al. (2023), The iratebirds Citizen Science Project: a Dataset on Birds’ Visual Aesthetic Attractiveness to Humans, Scientific Data. The full database "iratebirds_raw_data_taxonomy_photoinfo_ratings_survey_251022.csv" includes all the data related to the photographs scored (e.g. place and location of the photograph, and its quality), the species and subspecies names (following the eBird/Clements integrated checklist v. 2019), the raw scores made by the users, details of the users (e.g. language used), and internal user ID, and for the users who took the online survey, also detailed information about their demography, e.g. home country and other information related to their knowledge of and connection to nature and birds. The modeled rating scores database "iratebirds_final_predictions_average_fullmodel_subsetmodel_151122.csv" includes visual aesthetic attractiveness of birds, as perceived by humans, calculated in three different ways. The most appropiate score can be chosen by the user according to the specific research needs, but in general we recommend using the scores from the full model (ii). The three different measures are i) raw visual aesthetic attractiveness for each bird species (or subspecies), ii) full model: visual aesthetic attractiveness corrected for language group of the scorer and the quality of the photo scored, iii) subset model: visual aesthetic attractiveness corrected as in ii) plus other user specific factors (related to bird and nature knowlegde and connections, home country, age. and gender). The file also gives information on how many photos were used for scoring each bird and how many users have scored the species. The latter subset model iii) represents only a subset of all the species. The data on visual aesthetic attractiveness are also available at the species and the sex within-species level, for the sexually dichromatic species, in the file "iratebirds_pred_ratings_species_and_sex_level_120123.csv".
All database files are given both as .csv- and .xlsx -files. The data and code to reproduce the analyses, figures and tables presented in Haukka et al. 2023 The iratebirds citizen science project: a dataset of birds’ visual aesthetic attractiveness to humans (Scientific Data doi: https://doi.org/10.1038/s41597-023-02169-0) are included in the 'iratebirds_raw_data_taxonomy_photoinfo_ratings_survey_251022.csv' and 'Haukka_et_al_Scientific_Data_modelling.R','Haukka_et_al_Scientific_Data_Figure.R' and 'Haukka_et_al_Scientific_Data_Tables.R' -files. Detailed information on dataprosessing and models can be found in the publication Haukka et al. 2023 The iratebirds Citizen Science Project: a Dataset on Birds’ Visual Aesthetic Attractiveness to Humans, Scientific Data doi: https://doi.org/10.1038/s41597-023-02169-0)
Not seeing a result you expected?
Learn how you can add new datasets to our index.
https://www.nlm.nih.gov/databases/download/terms_and_conditions.htmlhttps://www.nlm.nih.gov/databases/download/terms_and_conditions.html
This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute Imaging Data Commons (IDC) [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: NLM-Visible-Human-Project. You can use the manifests included in this Zenodo record to download the content of the collection following the Download instructions below.
The NLM Visible Human Project [2] has created publicly-available complete, anatomically detailed, three-dimensional representations of a human male body and a human female body. Specifically, the VHP provides a public-domain library of cross-sectional cryosection, CT, and MRI images obtained from one male cadaver and one female cadaver. The Visible Man data set was publicly released in 1994 and the Visible Woman in 1995.
The data sets were designed to serve as (1) a reference for the study of human anatomy, (2) public-domain data for testing medical imaging algorithms, and (3) a test bed and model for the construction of network-accessible image libraries. The VHP data sets have been applied to a wide range of educational, diagnostic, treatment planning, virtual reality, artistic, mathematical, and industrial uses. About 4,000 licensees from 66 countries were authorized to access the datasets. As of 2019, a license is no longer required to access the VHP datasets.
Courtesy of the U.S. National Library of Medicine. Release of this collection by IDC does not indicate or imply that NLM has endorsed its products/services/applications. Please see the Visible Human Project information page to learn more about the images and to obtain any supporting metadata for this collection. Note that this collection may not reflect the most current/accurate data available from NLM.
Citation guidelines can be found on the National Library of Medicine Terms and Conditions information page.
A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, collection_id-idc_v8-aws.s5cmd
corresponds to the contents of the collection_id
collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.
nlm_visible_human_project-idc_v15-aws.s5cmd
: manifest of files available for download from public IDC Amazon Web Services bucketsnlm_visible_human_project-idc_v15-gcs.s5cmd
: manifest of files available for download from public IDC Google Cloud Storage bucketsnlm_visible_human_project-idc_v15-dcf.dcf
: Gen3 manifest (for details see https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids)Note that manifest files that end in -aws.s5cmd
reference files stored in Amazon Web Services (AWS) buckets, while -gcs.s5cmd
reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.
Each of the manifests include instructions in the header on how to download the included files.
To download the files using .s5cmd
manifests:
pip install --upgrade idc-index
.s5cmd
manifest file: idc download manifest.s5cmd
.To download the files using .dcf
manifest, see manifest header.
Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.
[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. RadioGraphics (2023). https://doi.org/10.1148/rg.230180
[2] Spitzer, V., Ackerman, M. J., Scherzinger, A. L. & Whitlock, D. The visible human male: a technical report. J. Am. Med. Inform. Assoc. 3, 118–130 (1996). https://doi.org/10.1136/jamia.1996.96236280