The SCAN Policies Database includes state definitions and policies from the 50 states, the District of Columbia, and the Commonwealth of Puerto Rico. The SCAN Policies Database 2019 represents data, collected, reviewed, and verified between May 2019 and July 2020, and the data reflect the state definitions and policies for the calendar year 2019. The SCAN Policies Database 2021 represents data collected, reviewed, and verified between July 2021 and January 2022, and the data reflect the state definitions and policies for the calendar year 2021. The SCAN Policies Database 2023 represents data, collected, reviewed, and verified between May 2023 and July 2024, and the data reflect the state definitions and policies for the calendar year 2023.

Investigators: Elizabeth C. Weigensberg, PhD - Mathematica Nuzhat Islam, MS - Mathematica Jean Knab, PhD - Mathematica Mary A. Grider, MBA - Mathematica Jeremy Page, MA - Mathematica Sarah Bardin, BA - MathematicaAddison Larson, MS - MathematicaMilena Raketic, , M.Ed -Mathematica

The State Child Abuse and Neglect (SCAN) Policies Database, supported by the Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human services, compiles data on state definitions and policies related to the surveillance of child maltreatment incidence and associated risk and protective factors. The SCAN Policies Database is a resource for researchers, analysts, and others who are interested in examining differences in definitions and policies on child maltreatment across states. A primary use of these data is to allow researchers to link the analytic files to other data sources to address important questions about how variations in states’ definitions and policies are associated with the incidence of child maltreatment, the child welfare system response, and ultimately child safety and well-being. Other data sources that can be linked with the SCAN Policies Database include data from the National Child Abuse and Neglect Data System (NCANDS), the Adoption and Foster Care Analysis and Reporting System (AFCARS), state administrative data, and survey data. When data from the SCAN Policies Database are linked with other data sources, these data can be used to answer key research questions about how variations in definitions and policies are associated with key aspects of understanding the incidence of child abuse and neglect. The SCAN Policies Database includes state definitions and policies from the 50 states, the District of Columbia, and the Commonwealth of Puerto Rico. The data were collected from a review of statutes and state documentation between May 2019 - June 2020.

Investigators: Elizabeth C. Weigensberg, PhD - Mathematica Nuzhat Islam, MS - Mathematica Jean Knab, PhD - Mathematica Mary A. Grider, MBA - Mathematica Jeremy Page, MA - Mathematica Sarah Bardin, BA - Mathematica

Other data sources that can be linked with the SCAN Policies Database include data from the National Child Abuse and Neglect Data System (NCANDS), the Adoption and Foster Care Analysis and Reporting System (AFCARS), state administrative data, and survey data. When data from the SCAN Policies Database are linked with other data sources, these data can be used to answer key research questions about how variations in definitions and policies are associated with key aspects of understanding the incidence of child abuse and neglect. Investigators: Elizabeth C. Weigensberg, PhD - Mathematica Nuzhat Islam, MS - Mathematica Milena Raketic, M.Ed - Mathematica Mary A. Grider, MBA - Mathematica Jeremy Page, MA - Mathematica

The SCAN Policies Database includes state definitions and policies from the 50 states, the District of Columbia, and the Commonwealth of Puerto Rico. The SCAN Policies Database 2021 represents data, collected, reviewed, and verified between May 2021 and July 2022, and the data reflect the state definitions and policies for the calendar year 2019. The SCAN Policies Database 2021 represents data collected, reviewed, and verified between July 2021 and January 2022, and the data reflect the state definitions and policies for the calendar year 2021.

Investigators: Elizabeth C. Weigensberg, PhD - Mathematica Nuzhat Islam, MS - Mathematica Jean Knab, PhD - Mathematica Mary A. Grider, MBA - Mathematica Jeremy Page, MA - Mathematica Addison Larson, MS - Mathematica

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

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Section 6

Metadata access is required to view this section.

Abstract

THE DATA ON THIS PAGE SHOULD NOW ONLY BE USED TO FINISH UP EXISTING PROJECTS. NO NEW PROJECTS SHOULD BE STARTED WITH THIS COPY OF THE DATA.

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)

%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.

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

Metadata access is required to view this section.

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 DATA ON THIS PAGE SHOULD NOW ONLY BE USED TO FINISH UP EXISTING PROJECTS. NO NEW PROJECTS SHOULD BE STARTED WITH THIS COPY OF THE DATA.

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)

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• 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.

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 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., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

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.

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

This is a continuously growing and improving database of high-quality neuroanatomically labeled MRI brain scans, created not by an algorithm, but by neuroanatomical experts. All results are checked and corrected. Regions of interest include the usual sub-cortical structures (thalamus, caudate, putamen, hippocampus, etc), along with ventricles, brain stem, cerebellum, and gray and white matter. We also sub-divide the cortex into "parcellation units" that are defined by gyral and sulcal landmarks. There are 157 ROIs now and more to come.

Abstract

Lookup table (Code Reference Book) for MarketScan

Usage

Redbook is cumulative, so the most recent Redbook can be used for all years.

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

• The MarketScan Commercial Database

%3C!-- --%3E

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

%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 Submissions

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.

The IBM MarketScan Research Databases contain individual-level, de-identified healthcare claims data including clinical utilization, expenditures, insurance enrollment/plan benefit for inpatient, outpatient, prescription drug, and carve-out services for a large population of individuals and their dependents with employer-provided commercial insurance in the United States of America. De-identified records of more than 250 million patients are included in the database.

Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for this data set may be limited.This data set contains measurements taken during the Soil Moisture Experiment 2004 (SMEX04) in southern Arizona, USA. The SCAN station houses numerous sensors which were used to automatically record the data.

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.

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.

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 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.