cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57761/dz3m-tn92
title: USA Structures

description: DHS, FIMA, FEMA’s Response Geospatial Office, Oak Ridge National Laboratory, and the U.S. Geological Survey collaborated to build and maintain the nation’s first comprehensive inventory of all structures larger than 450 square feet for use in Flood Insurance Mitigation, Emergency Preparedness and Response.

FEMA and our federal partners identified a need to create a building outline (the polygon representation of the structure) and an updated address database for the nation that could help ensure that critical infrastructure and residential buildings are accounted for in the disaster response and recovery decision-making processes.

To respond effectively, we need to understand population and the built environment—where people live, work and the critical infrastructure they rely on.

Many residential structures have an address where the occupants receive mail, but the address is not associated with the structure’s physical location. This can happen when people use a Post Office box, or when the mailbox is located in a central location, which is often the case in mobile home parks, apartment buildings, and rural communities.

The lack of structure information can limit our ability to adequately characterize a disaster’s potential impacts since parts of the community are missed by the predictive models. Furthermore, the homes that lack a footprint with an associated address are occupied by the most vulnerable in the community, delaying aid to those who are most in need. How?  Since the location and address are not coupled, FEMA and our response partners can struggle to determine which damaged home is associated with the address presented by the survivor seeking assistance.
description: To create the building outline inventory, FEMA, in conjunction with DHS Science and Technology, partnered with the Oak Ridge National Laboratory (ORNL) to extract the outlines via commercially available satellite imagery. We then worked to determine the building’s usage or occupancy type (e.g., residential, commercial, industrial) which is noted as an attribute for each structure.



In the past, geographers have relied on satellite imagery as a high-coverage and low-cost data source to create building-location inventories; however, identifying individual buildings is labor-intensive and had been difficult to automate due to large variations of building appearances. Our processes included some new machine learning techniques and a collection method to obtain data from multiple sources, including from local governments who agreed to share it, and open data from the National Geospatial-Intelligence Agency (NGA).
DOI: 10.57761/g2mj-6z78
title: Weighting Techniques for Large Private Claims Data

description: If you have additional questions please reach out to Sarah Hirsch (swhirsch@stanford.edu) or Neil Kamdar (neilseal@stanford.edu).
description: The page contains materials from the PHS Seminar on Weighting Techniques for Large Private Claims Data that was held on On October 24, 2024.

Supplemental materials will be added to this page soon as well.
description: On October 24, 2024, PHS hosted a Seminar on Weighting Techniques for Large Private Claims Data. Using the MarketScan Commercial Database as an example case, Social Scientist Sarah Hirsch discussed three schemes for weighting private claims data using US census-based surveys, and the associated methods and techniques. She provided researchers with the tools to implement these methodologies, or to formulate their own for other datasets.



We invite you to view the [Recording of the Seminar](<https://drive.google.com/file/d/1aBEAhp1idvFYI2NdauOyQvSZ7lCjvgai/view?usp=sharing>) to learn more about this topic! The [slide deck](<https://drive.google.com/file/d/1dBIaHZC-XL9_0A0WZmp8ydSmTFbev5HR/view?usp=sharing>) and [transcript](<https://drive.google.com/file/d/1JcDlPpTRa6kNTa-4_PALy9O0acGC38yJ/view?usp=sharing>) are also available for reference.



In the near-future, we will also add some code scripts, a written description of the weighting process, and the final MarketScan weights. Some additional revisions are also being made related to the following (so more explanation about these elements will be added as well):

- The region imputation.
- Patients from Puerto Rico (who are under a different survey from those employed here), who are being removed.
- Imputation based on non-null values in other years that were available for some people.

<!-- -->
DOI: 10.57761/k5kz-gh68
title: Weighting Techniques for Large Private Claims Data

description: If you have additional questions please reach out to Sarah Hirsch (hirsch.sw@gmail.com) or Neil Kamdar (neilseal@stanford.edu).
description: The page contains materials from the PHS Seminar on Weighting Techniques for Large Private Claims Data that was held on On October 24, 2024, as well as some additional documentation and the weights themselves.
description: On October 24, 2024, PHS hosted a Seminar on Weighting Techniques for Large Private Claims Data. Using the MarketScan Commercial Database as an example case, Social Scientist Sarah Hirsch discussed three schemes for weighting private claims data using US census-based surveys, and the associated methods and techniques. She provided researchers with the tools to implement these methodologies, or to formulate their own for other datasets.



We invite you to view the [Recording of the Seminar](<https://drive.google.com/file/d/1aBEAhp1idvFYI2NdauOyQvSZ7lCjvgai/view?usp=sharing>) to learn more about this topic! The [slide deck](<https://drive.google.com/file/d/1dBIaHZC-XL9_0A0WZmp8ydSmTFbev5HR/view?usp=sharing>) and [transcript](<https://drive.google.com/file/d/1JcDlPpTRa6kNTa-4_PALy9O0acGC38yJ/view?usp=sharing>) are also available for reference.



We have also added some code scripts, a written description of the weighting process, and the final MarketScan weights. Some additional have also been made related to the following:

- The region imputation.
- Patients from Puerto Rico (who are under a different survey from those employed here), who are being removed.
- Imputation based on non-null values in other years that were available for some people.

<!-- -->
DOI: 10.57761/9xs3-6w28
title: Weighting Techniques for Large Private Claims Data

description: If you have additional questions please reach out to Sarah Hirsch (hirsch.sw@gmail.com) or Neil Kamdar (neilseal@stanford.edu).
description: The page contains materials from the PHS Seminar on Weighting Techniques for Large Private Claims Data that was held on On October 24, 2024, as well as some additional documentation and the weights themselves.
description: On October 24, 2024, PHS hosted a Seminar on Weighting Techniques for Large Private Claims Data. Using the MarketScan Commercial Database as an example case, Social Scientist Sarah Hirsch discussed three schemes for weighting private claims data using US census-based surveys, and the associated methods and techniques. She provided researchers with the tools to implement these methodologies, or to formulate their own for other datasets.



We invite you to view the [Recording of the Seminar](<https://drive.google.com/file/d/1aBEAhp1idvFYI2NdauOyQvSZ7lCjvgai/view?usp=sharing>) to learn more about this topic! The [slide deck](<https://drive.google.com/file/d/1dBIaHZC-XL9_0A0WZmp8ydSmTFbev5HR/view?usp=sharing>) and [transcript](<https://drive.google.com/file/d/1JcDlPpTRa6kNTa-4_PALy9O0acGC38yJ/view?usp=sharing>) are also available for reference.



We have also added some code scripts, a written description of the weighting process, and the final MarketScan weights. Some additional have also been made related to the following:

- The region imputation.
- Patients from Puerto Rico (who are under a different survey from those employed here), who are being removed.
- Imputation based on non-null values in other years that were available for some people.

<!-- -->
DOI: 10.57761/x2vm-jk64
title: MedAlign

description: jason-fries@stanford.edu (Jason Fries)
description: MedAlign is a benchmark dataset of 983 clinician-curated natural language instructions for EHR data, grounded by 275 longitudinal EHRs. It includes reference responses for 303 instructions and supports evaluation of LLMs on healthcare-specific tasks.
description: **1\. Overview**

MedAlign is a **longitudinal EHR** **benchmark** for instruction-following with LLMs. The dataset includes:

- 275 patients
- 46,252 clinical notes
- 128 clinical note types
- 3\.6 million clinical events

<!-- -->



**2\. EHR Data**

EHR data is sourced from [Stanford’s STARR-OMOP database. ](<https://academic.oup.com/jamiaopen/article/6/3/ooad054/7236015>)Data are standardized in the [OMOP CDM](<https://www.ohdsi.org/data-standardization/>) schema and are scrubbed on identifying PHI information. Complete technical details are included in the paper, but key highlights:

- Dates are jittered within patient to conceal real dates (but preserve deltas between dates)
- Data for patients >= 90 years old are removed

<!-- -->

- Unstructured text fields not mappable to OMOP standard concepts are redacted

<!-- -->

- All clinical note text has been scrubbed of PHI variables using hiding-in-plain-sight (HIPS) [Carrell et al. 2013](<https://pubmed.ncbi.nlm.nih.gov/22771529/>).
- HIV test results are redacted.
- Provider names and NPIs are redacted

<!-- -->



Please see our Github repo to obtain code for loading the dataset.




description: Access to the MedAlign dataset requires the following:

- Verified Affiliation (Academic, Government, Industry Research Lab). Please use your **verified email address** when applying, **do not use gmail or personal emails. **Applications using personal, unverified email addresses will be rejected.
- Encryption Verification / Attestation for Data Storage
- Signing the terms of the MedAlign Data Set License 1.0
- Providing a short description of your intended research use of MedAlign
- CITI Training

<!-- -->

**These data must remain on your encrypted machine. Redistribution of data is FORBIDDEN and will result in immediate termination of access privileges. **



Please allow 7-10 business days to process applications.
DOI: 10.57761/y2dx-wj03
title: MedAlign

description: jason-fries@stanford.edu (Jason Fries)
description: MedAlign is a benchmark dataset of 983 clinician-curated natural language instructions for EHR data, grounded by 275 longitudinal EHRs. It includes reference responses for 303 instructions and supports evaluation of LLMs on healthcare-specific tasks.
description: **1\. Overview**

MedAlign is a **longitudinal EHR** **benchmark** for instruction-following with LLMs. The dataset includes:

- 275 patients
- 46,252 clinical notes
- 128 clinical note types
- 3\.6 million clinical events

<!-- -->



**2\. EHR Data**

EHR data is sourced from [Stanford’s STARR-OMOP database. ](<https://academic.oup.com/jamiaopen/article/6/3/ooad054/7236015>)Data are standardized in the [OMOP CDM](<https://www.ohdsi.org/data-standardization/>) schema and are scrubbed on identifying PHI information. Complete technical details are included in the paper, but key highlights:

- Dates are jittered within patient to conceal real dates (but preserve deltas between dates)
- Data for patients >= 90 years old are removed

<!-- -->

- Unstructured text fields not mappable to OMOP standard concepts are redacted

<!-- -->

- All clinical note text has been scrubbed of PHI variables using hiding-in-plain-sight (HIPS) [Carrell et al. 2013](<https://pubmed.ncbi.nlm.nih.gov/22771529/>).
- HIV test results are redacted.
- Provider names and NPIs are redacted

<!-- -->



Please see our Github repo to obtain code for loading the dataset.




description: Access to the MedAlign dataset requires the following:

- Verified Affiliation (Academic, Government, Industry Research Lab). Please use your **verified email address** when applying, **do not use gmail or personal emails. **Applications using personal, unverified email addresses will be rejected.
- Encryption Verification / Attestation for Data Storage
- Signing the terms of the MedAlign Data Set License 1.0
- Providing a short description of your intended research use of MedAlign
- CITI Training

<!-- -->

**These data must remain on your encrypted machine. Redistribution of data is FORBIDDEN and will result in immediate termination of access privileges. **



**IMPORTANT NOTES**:

- Our policy on derived works aligns with PhysioNet's guidelines, requiring that these artifacts be hosted on Redivis. If you create derived research artifacts based on MedAlign (such as additional annotations or synthetic data), please contact us to discuss hosting arrangements.
- Sending MedAlign data over a non-HIPAA-compliant API is a violation of the DUA.

<!-- -->



Please allow 7-10 business days to process applications.
DOI: 10.57761/1gdm-qh63
title: MedAlign

description: jason-fries@stanford.edu (Jason Fries)
description: MedAlign is a benchmark dataset of 983 clinician-curated natural language instructions for EHR data, grounded by 275 longitudinal EHRs. It includes reference responses for 303 instructions and supports evaluation of LLMs on healthcare-specific tasks.
description: **IMPORTANT USAGE NOTE: **MedAlign **only includes test set examples**. No training examples are provided for fine-tuning models.



**1\. Overview**

MedAlign is a **longitudinal EHR** **benchmark** for instruction-following with LLMs. The dataset includes:

- 275 patients
- 46,252 clinical notes
- 128 clinical note types
- 3\.6 million clinical events

<!-- -->



**2\. EHR Data**

EHR data is sourced from [Stanford’s STARR-OMOP database. ](<https://academic.oup.com/jamiaopen/article/6/3/ooad054/7236015>)Data are standardized in the [OMOP CDM](<https://www.ohdsi.org/data-standardization/>) schema and are scrubbed on identifying PHI information. Complete technical details are included in the paper, but key highlights:

- Dates are jittered within patient to conceal real dates (but preserve deltas between dates)
- Data for patients >= 90 years old are removed

<!-- -->

- Unstructured text fields not mappable to OMOP standard concepts are redacted

<!-- -->

- All clinical note text has been scrubbed of PHI variables using hiding-in-plain-sight (HIPS) [Carrell et al. 2013](<https://pubmed.ncbi.nlm.nih.gov/22771529/>).
- HIV test results are redacted.
- Provider names and NPIs are redacted

<!-- -->





**3\. Instruction Following Benchmark**



See "medalign\_instructions\_responses\_v1\_2.zip" for instructions, responses, and EHR text timelines.

Please see our Github repo to obtain code for loading the dataset.




description: Access to the MedAlign dataset requires the following:

- Verified Affiliation (Academic, Government, Industry Research Lab). Please use your **verified email address** when applying, **do not use gmail or personal emails. **Applications using personal, unverified email addresses will be rejected.
- Encryption Verification / Attestation for Data Storage
- Signing the terms of the MedAlign Data Set License 1.0
- Providing a short description of your intended research use of MedAlign
- CITI Training

<!-- -->

**These data must remain on your encrypted machine. Redistribution of data is FORBIDDEN and will result in immediate termination of access privileges. **



**IMPORTANT NOTES**:

- Our policy on derived works aligns with PhysioNet's guidelines, requiring that these artifacts be hosted on Redivis. If you create derived research artifacts based on MedAlign (such as additional annotations or synthetic data), please contact us to discuss hosting arrangements.
- Sending MedAlign data over a non-HIPAA-compliant API is a violation of the DUA.

<!-- -->



Please allow 7-10 business days to process applications.
DOI: 10.57761/7hh9-gd47
title: FactEHR Stanford

description: jason-fries@stanford.edu
description: FactEHR is a dataset for verifying facts in clinical text, containing fact decompositions of 2,168 clinical notes from three hospital systems generated by four language models: GPT4o, o1-mini, Gemini-1.5-Pro, and Llama3-8B. It includes 3,504 textual entailment pairs labeled by 7 clinicians, supporting advanced research in clinical fact verification.
description: **1\. Overview**

FactEHR is a fact decomposition dataset for clinical notes. FactEHR is sampled from three source datasets: MIMIC-III, UCSF's CORAL, and Stanford's MedAlign. For governance reasons, MIMIC and UCSF data must be downloaded from PhysioNet (**FactEHR PhysioNet**) and MedAlign from Redivis (**FactEHR Stanford**).



**2\. FactEHR Stanford**

1. all\_human\_model\_entailment\_labels.csv – This file contains entailment pairs sampled from entailment\_pairs.csv, labeled by clinical experts to serve as ground truth for entailment tasks. 
2. combined\_notes.csv – This file contains all clinical notes from which FactEHR is derived, including their full text, estimated token count, note type, and dataset source. 
3. entailment\_pairs.csv – This file consists of entailment pairs derived from fact decompositions and their source clinical notes, with entailment predictions generated by models. 
4. fact\_decompositions.csv – This file stores fact decompositions generated by LLMs, linking decomposed facts to clinical notes using unique identifiers.
5. factehr\_dev\_set.csv – This file contains entailment pairs sampled from the same data sources as FactEHR (but different notes), intended for entailment model development.
6. precision\_hypotheses.csv – This file contains hypotheses for precision entailment pairs, where the premise is a source clinical note and each hypothesis is a fact from its corresponding decomposition.
7. recall\_hypotheses.csv – This file contains hypotheses for recall entailment pairs, where the premise is a fact decomposition and each hypothesis is a sentence from its original clinical note. 

<!-- -->




description: Access to the** FactEHR Stanford** requires following:

- Verified Affiliation (Academic, Government, Industry Research Lab). Please use your **verified email address** when applying, **do not use gmail or personal emails. **Applications using personal, unverified email addresses will be rejected.
- Encryption Verification / Attestation for Data Storage
- Signing the terms of the MedAlign Data Set License 1.0
- Providing a short description of your intended research use of MedAlign
- CITI Training

<!-- -->

**These data must remain on your encrypted machine. Redistribution of data is FORBIDDEN and will result in immediate termination of access privileges. **



**IMPORTANT NOTE**: Our policy on derived works aligns with PhysioNet's guidelines, requiring that these artifacts be hosted on Redivis. If you create derived research artifacts based on the dataset (such as additional annotations or synthetic data), please contact us to discuss hosting arrangements.



Please allow 7-10 business days to process applications.
DOI: 10.57761/8phf-km40
title: Geophysics 115 - Taking the Pulse of the Planet

description: These datasets are used for the GP 115 course: Taking the Pulse of the Planet. This purpose of these datasets is to give students exposure to working with satellite data and coding. 

One note: in the class if you update the version of the data it will update how you call it so make sure you change it in your code to the latest version if you want it to reflect that.
DOI: 10.57761/c3tj-5646
title: Geophysics 115 - Taking the Pulse of the Planet

description: These datasets are used for the GP 115 course: Taking the Pulse of the Planet. This purpose of these datasets is to give students exposure to working with satellite data and coding. 

One note: in the class if you update the version of the data it will update how you call it so make sure you change it in your code to the latest version if you want it to reflect that.
DOI: 10.57761/hh44-qn04
title: Geophysics 115 - Taking the Pulse of the Planet

description: These datasets are used for the GP 115 course: Taking the Pulse of the Planet. This purpose of these datasets is to give students exposure to working with satellite data and coding. 

One note: in the class if you update the version of the data it will update how you call it so make sure you change it in your code to the latest version if you want it to reflect that.
DOI: 10.57761/ppmj-gb18
title: Geophysics 115 - Taking the Pulse of the Planet

description: These datasets are used for the GP 115 course: Taking the Pulse of the Planet. This purpose of these datasets is to give students exposure to working with satellite data and coding. 

One note: in the class if you update the version of the data it will update how you call it so make sure you change it in your code to the latest version if you want it to reflect that.
DOI: 10.57761/qdec-kk37
title: Geophysics 115 - Taking the Pulse of the Planet

description: These datasets are used for the GP 115 course: Taking the Pulse of the Planet. This purpose of these datasets is to give students exposure to working with satellite data and coding. 

One note: in the class if you update the version of the data it will update how you call it so make sure you change it in your code to the latest version if you want it to reflect that.
DOI: 10.57761/r88m-5x21
title: Geophysics 115 - Taking the Pulse of the Planet

description: These datasets are used for the GP 115 course: Taking the Pulse of the Planet. This purpose of these datasets is to give students exposure to working with satellite data and coding. 

One note: in the class if you update the version of the data it will update how you call it so make sure you change it in your code to the latest version if you want it to reflect that.
DOI: 10.57761/tr24-te42
title: Geophysics 115 - Taking the Pulse of the Planet

description: These datasets are used for the GP 115 course: Taking the Pulse of the Planet. This purpose of these datasets is to give students exposure to working with satellite data and coding. 

One note: in the class if you update the version of the data it will update how you call it so make sure you change it in your code to the latest version if you want it to reflect that.
DOI: 10.57761/xd3z-1y36
title: Geophysics 115 - Taking the Pulse of the Planet

description: These datasets are used for the GP 115 course: Taking the Pulse of the Planet. This purpose of these datasets is to give students exposure to working with satellite data and coding. 

One note: in the class if you update the version of the data it will update how you call it so make sure you change it in your code to the latest version if you want it to reflect that.
DOI: 10.15468/dl.ra7n6h
title: Occurrence Download

description: A dataset containing 6370 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Eumida sanguinea (Örsted, 1843))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 6370 records from 169 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046113-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mxuvy6
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Atelopus erythropus Boulenger, 1903"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0201404-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zsu45k
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cryptotis phillipsii (Schaldach, 1966)"
 ]
}

 The dataset includes 19 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032077-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.