Marine Trackline Geophysical data represented within the side-scan sonar data are from towed instruments closer to the seafloor that use sound to image features on the ocean floor. This technique can create shadows like shining a flashlight, which help determine size and features. This system is often used to map cultural heritage sites like shipwrecks, to characterize the makeup of the seafloor, and can even be used to help biologists identify habitats of marine animals.

Abstract

The MarketScan Commercial Database (previously called the 'MarketScan Database') contains real-world data for healthcare research and analytics to examine health economics and treatment outcomes.

This page also contains the MarketScan Commercial Lab Database starting in 2018.

Starting in 2026, there will be a data access fee for using the full dataset. Please refer to the 'Usage Notes' section of this page for more information.

Methodology

MarketScan Research Databases are a family of data sets that fully integrate many types of data for healthcare research, including:

• De-identified records of more than 188 million patients (medical, drug and dental)

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• Laboratory results

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• Hospital discharges

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The MarketScan Databases track millions of patients throughout the healthcare system. The data are contributed by large employers, managed care organizations, hospitals, EMR providers, and Medicare.

Usage

This page contains the MarketScan Commercial Database.

We also have the following on other pages:

• The MarketScan Medicare Supplemental Database
• The MarketScan Dental Database
• The MarketScan Benefit Plan Design data
• MarketScan Redbook (The MarketScan Code Reference Book)

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**Starting in 2026, there will be a data access fee for using the full dataset **(though the 1% sample will remain free to use). The pricing structure and other **relevant information can be found in this **FAQ Sheet.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to support@stanfordphs.freshdesk.com for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Data Documentation

Data access is required to view this section.

Section 2

Metadata access is required to view this section.

Section 3

Metadata access is required to view this section.

Usage FAQs (Answers provided in User Guide starting on page 56)

Metadata access is required to view this section.

Collection of neuroanatomically labeled MRI brain scans, created by neuroanatomical experts. Regions of interest include the sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter and sub-divided cortex into parcellation units that are defined by gyral and sulcal landmarks.

This is the MRI scan database used in the research work of classifying Meningioma Tumor in humans by using hybrid Ensemble Deep Learning Network AlGoRes. It consist of two sets; one for training and another one for testing the Deep Learning Network AlGoRes.Training data set consist of 822 imagers with meningioma_tumor and 395 images without tumor.Testing data set consist of 115 imagers with meningioma_tumor and 104 images without tumor.

Abstract

The MarketScan Dental Database is a standalone product that corresponds with and is linkable to a given year and version of the IBM MarketScan Commercial Claims and Encounters Database and the MarketScan Medicare Supplemental and Coordination of Benefits Database. Currently, data is available for the years: 2005 - 2023. In order to view the MarketScan Dental user guide or data dictionary, you must have data access to this dataset.

Usage

In addition to what's on this page, we also have:

• The MarketScan Commercial Database
• The MarketScan Medicare Supplemental Database
• The MarketScan Benefit Plan Design data

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• MarketScan Redbook (The MarketScan Code Reference Book)

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**Starting in 2026, there will be a data access fee for using the full dataset **(though the 1% sample will remain free to use). The pricing structure and other **relevant information can be found in this **FAQ Sheet.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

support@stanfordphs.freshdesk.com for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Data Documentation

Data access is required to view this section.

Section 3

Metadata access is required to view this section.

Section 4

Metadata access is required to view this section.

Section 5

Metadata access is required to view this section.

Section 6

Metadata access is required to view this section.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 17, 2022. A large-scale database of genetics and genomics data associated to a web-interface and a set of methods and algorithms that can be used for mining the data in it. The database contains two categories of single nucleotide polymorphism (SNP) annotations: # Physical-based annotation where SNPs are categorized according to their position relative to genes (intronic, inter-genic, etc.) and according to linkage disequilibrium (LD) patterns (an inter-genic SNP can be annotated to a gene if it is in LD with variation in the gene). # Functional annotation where SNPs are classified according to their effects on expression levels, i.e. whether they are expression quantitative trait loci (eQTLs) for that gene. SCAN can be utilized in several ways including: (i) queries of the SNP and gene databases; (ii) analysis using the attached tools and algorithms; (iii) downloading files with SNP annotation for various GWA platforms. . eQTL files and reported GWAS from NHGRI may be downloaded.

The Lung Image Database Consortium image collection (LIDC-IDRI) consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.

Seven academic centers and eight medical imaging companies collaborated to create this data set which contains 1018 cases. Each subject includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.

Note : The TCIA team strongly encourages users to review pylidc and the Standardized representation of the TCIA LIDC-IDRI annotations using DICOM (DICOM-LIDC-IDRI-Nodules) of the annotations/segmentations included in this dataset before developing custom tools to analyze the XML version.

Abstract

The MarketScan Medicare Supplemental Database provides detailed cost, use and outcomes data for healthcare services performed in both inpatient and outpatient settings.

It Include Medicare Supplemental records for all years, and Medicare Advantage records starting in 2020. This page also contains the MarketScan Medicare Lab Database starting in 2018.

Starting in 2026, there will be a data access fee for using the full dataset. Please refer to the 'Usage Notes' section of this page for more information.

Methodology

MarketScan Research Databases are a family of data sets that fully integrate many types of data for healthcare research, including:

• De-identified records of more than 250 million patients (medical, drug and dental)

%3C!-- --%3E

• Laboratory results

%3C!-- --%3E

• Hospital discharges

%3C!-- --%3E

The MarketScan Databases track millions of patients throughout the healthcare system. The data are contributed by large employers, managed care organizations, hospitals, EMR providers and Medicare.

Usage

This page contains the MarketScan Medicare Database.

We also have the following on other pages:

• The MarketScan Commercial Database
• The MarketScan Dental Database
• The MarketScan Benefit Plan Design data
• MarketScan Redbook (The MarketScan Code Reference Book)

%3C!-- --%3E

**Starting in 2026, there will be a data access fee for using the full dataset **

(though the 1% sample will remain free to use). The pricing structure and other

**relevant information can be found in this **FAQ Sheet.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

support@stanfordphs.freshdesk.com for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Data Documentation

Data access is required to view this section.

Section 2

Metadata access is required to view this section.

Section 3

Metadata access is required to view this section.

Paper logs are the primary data collection tool used by observers of the Northeast Fisheries Observer Program deployed on commercial fishing vessels. After the data collected on the paper are entered into a database, the paper logs are scanned for each trip. After all trips for a calendar year are scanned, they are archived at the National Archives and Records Administration.

The CATH-Gene3D database describes protein families and domain architectures in complete genomes. Protein families are formed using a Markov clustering algorithm, followed by multi-linkage clustering according to sequence identity. Mapping of predicted structure and sequence domains is undertaken using hidden Markov models libraries representing CATH and Pfam domains. CATH-Gene3D is based at University College, London, UK.

SFLD (Structure-Function Linkage Database) is a hierarchical classification of enzymes that relates specific sequence-structure features to specific chemical capabilities.

NCBIfam is a collection of protein families, featuring curated multiple sequence alignments, hidden Markov models (HMMs) and annotation, which provides a tool for identifying functionally related proteins based on sequence homology. NCBIfam is maintained at the National Center for Biotechnology Information (Bethesda, MD). NCBIfam includes models from TIGRFAMs, another database of protein families developed at The Institute for Genomic Research, then at the J. Craig Venter Institute (Rockville, MD, US).

The SCAN data retrieval tools provides an interactive process to identify and retrieve data from individual SCAN sites. The user does not need to know the ID for the site but must know either it's general location or the name of the site

PROSITE is a database of protein families and domains. It consists of biologically significant sites, patterns and profiles that help to reliably identify to which known protein family a new sequence belongs. PROSITE is based at the Swiss Institute of Bioinformatics (SIB), Geneva, Switzerland.

This dataset presents the raw and processed sidescan mosaic for surveys completed along the 58‐km long Assateague barrier island stretching from the Ocean City inlet in Maryland, down past Chincoteague Island in northern Virginia. The data was collected June 20th-25th, 2014 and May 12th - 21th, 2015. Full coverage side-scan sonar and partial coverage bathymetry data were collected using an EdgeTech 6205 Multiphase Echosounder. In total, 73 square kilometers were mapped at primarily at 100m line spacing and 80 m swath range per channel (to allow overlap between lines).

PROSITE consists of documentation entries describing protein domains, families and functional sites as well as associated patterns and profiles to identify them [More... / References / Commercial users ]. PROSITE is complemented by ProRule , a collection of rules based on profiles and patterns, which increases the discriminatory power of profiles and patterns by providing additional information about functionally and/or structurally critical amino acids [More...].

SMART (a Simple Modular Architecture Research Tool) allows the identification and annotation of genetically mobile domains and the analysis of domain architectures. SMART is based at EMBL, Heidelberg, Germany.

PIRSF protein classification system is a network with multiple levels of sequence diversity from superfamilies to subfamilies that reflects the evolutionary relationship of full-length proteins and domains. PIRSF is based at the Protein Information Resource, Georgetown University Medical Centre, Washington DC, US.

The 2MASS Scan Information Table provides basic data for each scan in the 2MASS All Sky Release. The table is organized according to the broad function and utility of the parameters: positional information, photometric information, source detection statistics, etc.

Reupload of anonymized postmortem CT scans of the whole body originally published by Michael Kistler through the SICAS Medical Image Repository (smir.ch) as open access Virtual Skeleton Database (VSD). The CT datasets were provided by the forensic institutes of the universities of Bern and Zürich and shared under the Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA) license after ethical approval of the Cantonal Ethics Committee Bern. Further information can be found in:

• Kistler, M., Bonaretti, S., Pfahrer, M., Niklaus, R. & Büchler, P. The virtual skeleton database: an open access repository for biomedical research and collaboration. Journal of Medical Internet Research 15(11), e245; 10.2196/jmir.2930 (2013).
• Kistler, M. A database framework to incorporate statistical variability in biomechanical simulations. PhD thesis. Faculty of Medicine of the University of Bern; boristheses.unibe.ch/916 (2014).

Due to ongoing difficulties in accessing the SMIR website a mirror of the original VSDFullBody datasets without any alterations is provided.

CAUTION: The VSD contains a few inconsistencies, such as duplicate CT datasets. The uploader is not connected to the SMIR or VSD and, therefore, not responsible for errors in the VSD. However, errors that the uploader recognized during the work with the VSD were logged in an Excel file: VSD_Comments.xlsx

Datasets of the VSD were used for the creation of surface models of the lower body's osseous anatomy. Further information can be found in:

• Fischer, M. C. M. Database of segmentations and surface models of bones of the entire lower body created from cadaver CT scans. Scientific Data 10, 763; 10.1038/s41597-023-02669-z (2023).