The NFDI4Health Task Force COVID-19 Metadata Schema Mapping (Metadata Schema Mapping) contains a list of properties describing a resource being registered in the Study Hub of the NFDI4Health Task Force COVID-19 (Study Hub) and how those properties align with other standards (FHIRE, CDISK, DRKS, ITRCP)

The free database mapping COVID-19 treatment and vaccine development based on the global scientific research is available at https://covid19-help.org/.

Files provided here are curated partial data exports in the form of .csv files or full data export as .sql script generated with pg_dump from our PostgreSQL 12 database. You can also find .png file with our ER diagram of tables in .sql file in this repository.

Structure of CSV files

*On our site, compounds are named as substances

compounds.csv

Id - Unique identifier in our database (unsigned integer)

Name - Name of the Substance/Compound (string)

Marketed name - The marketed name of the Substance/Compound (string)

Synonyms - Known synonyms (string)

Description - Description (HTML code)

Dietary sources - Dietary sources where the Substance/Compound can be found (string)

Dietary sources URL - Dietary sources URL (string)

Formula - Compound formula (HTML code)

Structure image URL - Url to our website with the structure image (string)

Status - Status of approval (string)

Therapeutic approach - Approach in which Substance/Compound works (string)

Drug status - Availability of Substance/Compound (string)

Additional data - Additional data in stringified JSON format with data as prescribing information and note (string)

General information - General information about Substance/Compound (HTML code)

references.csv

Id - Unique identifier in our database (unsigned integer)

Impact factor - Impact factor of the scientific article (string)

Source title - Title of the scientific article (string)

Source URL - URL link of the scientific article (string)

Tested on species - What testing model was used for the study (string)

Published at - Date of publication of the scientific article (Date in ISO 8601 format)

clinical-trials.csv

Id - Unique identifier in our database (unsigned integer)

Title - Title of the clinical trial study (string)

Acronym title - Acronym of title of the clinical trial study (string)

Source id - Unique identifier in the source database

Source id optional - Optional identifier in other databases (string)

Interventions - Description of interventions (string)

Study type - Type of the conducted study (string)

Study results - Has results? (string)

Phase - Current phase of the clinical trial (string)

Url - URL to clinical trial study page on clinicaltrials.gov (string)

Status - Status in which study currently is (string)

Start date - Date at which study was started (Date in ISO 8601 format)

Completion date - Date at which study was completed (Date in ISO 8601 format)

Additional data - Additional data in the form of stringified JSON with data as locations of study, study design, enrollment, age, outcome measures (string)

compound-reference-relations.csv

Reference id - Id of a reference in our DB (unsigned integer)

Compound id - Id of a substance in our DB (unsigned integer)

Note - Id of a substance in our DB (unsigned integer)

Is supporting - Is evidence supporting or contradictory (Boolean, true if supporting)

compound-clinical-trial.csv

Clinical trial id - Id of a clinical trial in our DB (unsigned integer)

Compound id - Id of a Substance/Compound in our DB (unsigned integer)

tags.csv

Id - Unique identifier in our database (unsigned integer)

Name - Name of the tag (string)

tags-entities.csv

Tag id - Id of a tag in our DB (unsigned integer)

Reference id - Id of a reference in our DB (unsigned integer)

API Specification

Our project also has an Open API that gives you access to our data in a format suitable for processing, particularly in JSON format.

https://covid19-help.org/api-specification

Services are split into five endpoints:

Substances - /api/substances

References - /api/references

Substance-reference relations - /api/substance-reference-relations

Clinical trials - /api/clinical-trials

Clinical trials-substances relations - /api/clinical-trials-substances

Method of providing data

All dates are text strings formatted in compliance with ISO 8601 as YYYY-MM-DD

If the syntax request is incorrect (missing or incorrectly formatted parameters) an HTTP 400 Bad Request response will be returned. The body of the response may include an explanation.

Data updated_at (used for querying changed-from) refers only to a particular entity and not its logical relations. Example: If a new substance reference relation is added, but the substance detail has not changed, this is reflected in the substance reference relation endpoint where a new entity with id and current dates in created_at and updated_at fields will be added, but in substances or references endpoint nothing has changed.

The recommended way of sequential download

During the first download, it is possible to obtain all data by entering an old enough date in the parameter value changed-from, for example: changed-from=2020-01-01 It is important to write down the date on which the receiving the data was initiated let’s say 2020-10-20

For repeated data downloads, it is sufficient to receive only the records in which something has changed. It can therefore be requested with the parameter changed-from=2020-10-20 (example from the previous bullet). Again, it is important to write down the date when the updates were downloaded (eg. 2020-10-20). This date will be used in the next update (refresh) of the data.

Services for entities

List of endpoint URLs:

/api/substances

/api/references

/api/substance-reference-relations

/api/clinical-trials

/api/clinical-trials-substances

Format of the request

All endpoints have these parameters in common:

changed-from - a parameter to return only the entities that have been modified on a given date or later.

continue-after-id - a parameter to return only the entities that have a larger ID than specified in the parameter.

limit - a parameter to return only the number of records specified (up to 1000). The preset number is 100.

Request example:

/api/references?changed-from=2020-01-01&continue-after-id=1&limit=100

Format of the response

The response format is the same for all endpoints.

number_of_remaining_ids - the number of remaining entities that meet the specified criteria but are not displayed on the page. An integer of virtually unlimited size.

entities - an array of entity details in JSON format.

Response example:

{

"number_of_remaining_ids" : 100,


"entities" : [


  {


    "id": 3,


    "url": "https://www.ncbi.nlm.nih.gov/pubmed/32147628",


    "title": "Discovering drugs to treat coronavirus disease 2019 (COVID-19).",


    "impact_factor": "Discovering drugs to treat coronavirus disease 2019 (COVID-19).",


    "tested_on_species": "in silico",


    "publication_date": "2020-22-02",


    "created_at": "2020-30-03",


    "updated_at": "2020-31-03",


    "deleted_at": null


  },


  {


    "id": 4,


    "url": "https://www.ncbi.nlm.nih.gov/pubmed/32157862",


    "title": "CT Manifestations of Novel Coronavirus Pneumonia: A Case Report",


    "impact_factor": "CT Manifestations of Novel Coronavirus Pneumonia: A Case Report",


    "tested_on_species": "Patient",


    "publication_date": "2020-06-03",


      "created_at": "2020-30-03",


    "updated_at": "2020-30-03",


    "deleted_at": null


  },


]

}

Endpoint details

Substances

URL: /api/substances

Substances endpoint returns data in the format specified in Response example as an array of entities in JSON format specified in the entity format section.

Entity format:

id - Unique identifier in our database (unsigned integer)

name - Name of the Substance (string)

description - Description (HTML code)

phase_of_research - Phase of research (string)

how_it_helps - How it helps (string)

drug_status - Drug status (string)

general_information - General information (HTML code)

synonyms - Synonyms (string)

marketed_as - "Marketed as" (string)

dietary_sources - Dietary sources name (string)

dietary_sources_url - Dietary sources URL (string)

prescribing_information - Prescribing information as an array of JSON objects with description and URL attributes as strings

formula - Formula (HTML code)

created_at - Date when the entity was added to our database (Date in ISO 8601 format)

updated_at - Date when the entity was last updated in our database (Date in ISO 8601 format)

deleted_at - Date when the entity was deleted in our database (Date in ISO 8601 format)

References

URL: /api/references

References endpoint returns data in the format specified in Response example as an array of entities in JSON format specified in the entity format section.

Entity format:

id - Unique identifier in our database (unsigned integer)

url - URL link of the scientific article (string)

title - Title of the scientific article (string)

impact_factor - Impact factor of the scientific article (string)

tested_on_species - What testing model was used for the study (string)

publication_date - Date of publication of the scientific article (Date in ISO 8601 format)

created_at - Date when the entity was added to our database (Date in ISO 8601 format)

updated_at - Date when the entity was last updated in our database (Date in ISO 8601

Weekly archive of some State of Pennsylvania datasets found in this list: https://data.pa.gov/browse?q=vaccinations For most of these datasets, the "date_saved" field is the date that the WPRDC pulled the data from the state data portal and the archive combines all the saved records into one table. The exception to this is the "COVID-19 Vaccinations by Day by County of Residence Current Health (archive)" which is already published by the state as an entire history. The "date_updated" field is based on the date that the "updatedAt" field from the corresponding data.pa.gov dataset. Changes to this field have turned out to not be a good indicator of whether records have updated, which is why we are archiving this data on a weekly basis without regard to the "updatedAt" value. The "date_saved" field is the one you should sort on to see the variation in vaccinations over time. Most of the source tables have gone through schema changes or expansions. In some cases, we've kept the old archives under a separate resource with something like "[Orphaned Schema]" added to the resource name. In other cases, we've adjusted our schema to accommodate new column names, but there will be a date range during which the new columns have null values because we did not start pulling them until we became aware of them.

Background

The COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.

Purpose

To create the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. The Radiological Society of North America (RSNA) assembled the RSNA International COVID-19 Open Radiology Database (RICORD) collection of COVID-related imaging datasets and expert annotations to support research and education. RICORD data will be incorporated in the Medical Imaging and Data Resource Center (MIDRC), a multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Materials and Methods

This dataset was a collaboration between the RSNA and Society of Thoracic Radiology (STR).

Results

The RSNA International COVID-19 Open Annotated Radiology Database (RICORD) release 1b consists of 120 thoracic computed tomography (CT) scans of COVID negative patients from four international sites.

Patient Selection: Patients at least 18 years in age receiving negative diagnosis for COVID-19.

Data Abstract

1. 120 de-identified Thoracic CT scans from COVID negative patients.

2. Supporting clinical variables: MRN*, Age, Exam Date/Time*, Exam Description, Sex, Study UID*, Image Count, Modality, Symptomatic, Testing Result, Specimen Source (* pseudonymous values).

Research Benefits

As this is a public dataset, RICORD is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for COVID-19, use of RICORD to create AI systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond COVID-19 pneumonia. Deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with RICORD, particularly as more public COVID-19 imaging datasets are made available via complementary and parallel efforts. It is important to emphasize that there are limitations to the clinical “ground truth” as the SARS-CoV-2 RT-PCR tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. These limitations notwithstanding, RICORD has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive COVID-19 thoracic imaging data resource.

A full description of this dataset along with updated information can be found here.

In response to the COVID-19 pandemic, the Allen Institute for AI has partnered with leading research groups to prepare and distribute the COVID-19 Open Research Dataset (CORD-19), a free resource of scholarly articles, including full text content, about COVID-19 and the coronavirus family of viruses for use by the global research community.

This dataset is intended to mobilize researchers to apply recent advances in natural language processing to generate new insights in support of the fight against this infectious disease. The corpus will be updated weekly as new research is published in peer-reviewed publications and archival services like bioRxiv, medRxiv, and others.

By downloading this dataset you are agreeing to the Dataset license. Specific licensing information for individual articles in the dataset is available in the metadata file.

Additional licensing information is available on the PMC website, medRxiv website and bioRxiv website.

Dataset content:

Commercial use subset

Non-commercial use subset

PMC custom license subset

bioRxiv/medRxiv subset (pre-prints that are not peer reviewed)

Metadata file

Readme

Each paper is represented as a single JSON object (see schema file for details).

Description:

The dataset contains all COVID-19 and coronavirus-related research (e.g. SARS, MERS, etc.) from the following sources:

PubMed's PMC open access corpus using this query (COVID-19 and coronavirus research)

Additional COVID-19 research articles from a corpus maintained by the WHO

bioRxiv and medRxiv pre-prints using the same query as PMC (COVID-19 and coronavirus research)

We also provide a comprehensive metadata file of coronavirus and COVID-19 research articles with links to PubMed, Microsoft Academic and the WHO COVID-19 database of publications (includes articles without open access full text).

We recommend using metadata from the comprehensive file when available, instead of parsed metadata in the dataset. Please note the dataset may contain multiple entries for individual PMC IDs in cases when supplementary materials are available.

This repository is linked to the WHO database of publications on coronavirus disease and other resources, such as Microsoft Academic Graph, PubMed, and Semantic Scholar. A coalition including the Chan Zuckerberg Initiative, Georgetown University’s Center for Security and Emerging Technology, Microsoft Research, and the National Library of Medicine of the National Institutes of Health came together to provide this service.

Citation:

When including CORD-19 data in a publication or redistribution, please cite the dataset as follows:

In bibliography:

COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-MM-DD. Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed YYYY-MM-DD. 10.5281/zenodo.3715505

In text:

(CORD-19, 2020)

The Allen Institute for AI and particularly the Semantic Scholar team will continue to provide updates to this dataset as the situation evolves and new research is released.

This dataset is now archived and purely historical. The state of Pennsylvania stopped updating the source data at the end of June, 2023.

Weekly archive of some State of Pennsylvania datasets found in this list: https://data.pa.gov/browse?q=vaccinations

For most of these datasets, the "date_saved" field is the date that the WPRDC pulled the data from the state data portal and the archive combines all the saved records into one table. The exception to this is the "COVID-19 Vaccinations by Day by County of Residence Current Health (archive)" which is already published by the state as an entire history.

The "date_updated" field is based on the date that the "updatedAt" field from the corresponding data.pa.gov dataset. Changes to this field have turned out to not be a good indicator of whether records have updated, which is why we are archiving this data on a weekly basis without regard to the "updatedAt" value. The "date_saved" field is the one you should sort on to see the variation in vaccinations over time.

Most of the source tables have gone through schema changes or expansions. In some cases, we've kept the old archives under a separate resource with something like "[Orphaned Schema]" added to the resource name. In other cases, we've adjusted our schema to accommodate new column names, but there will be a date range during which the new columns have null values because we did not start pulling them until we became aware of them.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

COVID-19 Open Research Dataset (CORD-19) 🙄 😃🙄 ❤️😃🙄 😃

The COVID-19 Open Research Dataset is “a free resource of over 29,000 scholarly articles 🤝😎😎🤝

DESCRIPTION❤️❤️

About This Data ❤️❤️

Description: 😃😃

The COVID-19 Open Research Dataset is “a free resource of over 29,000 scholarly articles, including over 13,000 with full text, about COVID-19 and the coronavirus family of viruses for use by the global research community.”

in-the-news: On March 16, 2020, the White House issued a “call to action to the tech community” regarding the dataset, asking experts “to develop new text and data mining techniques that can help the science community answer high-priority scientific questions related to COVID-19.”

Included in this dataset:

Commercial use subset (includes PMC content) -- 9000 papers, 186Mb Non-commercial use subset (includes PMC content) -- 1973 papers, 36Mb PMC custom license subset -- 1426 papers, 19Mb bioRxiv/medRxiv subset (pre-prints that are not peer reviewed) -- 803 papers, 13Mb Each paper is represented as a single JSON object. The schema is available here.

We also provide a comprehensive metadata file of 29,000 coronavirus and COVID-19 research articles with links to PubMed, Microsoft Academic and the WHO COVID-19 database of publications (includes articles without open access full text):

Metadata file (readme) -- 47Mb Source: https://pages.semanticscholar.org/coronavirus-research Updated: Weekly License: https://data.world/kgarrett/covid-19-open-research-dataset/workspace/file?filename=COVID.DATA.LIC.AGMT.pdf

Note😃😃😃😃

• This data is for training how using data analysis 🤝🎉

• Please appreciate the effort with an upvote 👍 😃😃

Thank You ❤️❤️❤️

The Metadata Schema of the NFDI4Health and the NFDI4Health Task Force COVID-19 (Metadata Schema) contains a list of properties that describe a resource to be registered in the German Central Health Study Hub. Currently, two main types of resources are distinguished: a) study descriptions (i.e., metadata set describing a study) and b) study documents. However, due to the generic character of the Metadata Schema, other types of resources may also be described and registered. The metadata properties are divided into mandatory and recommended ones. Along with bibliographic information such as title and description of the resource, the related persons and organizations contributing to the development of the resource can also be specified. The results of studies published in journal articles or other text publications can be linked too. For studies, information about study design and accessibility of the collected data should be additionally provided. The Metadata Schema consists mainly of properties adapted from established standards and models such as DataCite Metadata Schema 4.4, data models of the ClinicalTrials.gov, German Clinical Trials Register, International Clinical Trials Registry, HL7® FHIR, MIABIS, Maelstrom Research cataloguing toolkit and DDI Controlled Vocabularies. This is an updated version V3_2 of the Metadata Schema, which improves the modules of the previous version via refined description texts and added, deleted, moved, or renamed items. Additional use case-specific requirements, particularly for the chronic diseases and record linkage modules, have also been considered in this new version along with updating the list of sources. The undertaken changes are described within the document.

🛑 Note: please do not claim diagnostic performance of a model without a clinical study! This is not a kaggle competition dataset.

Disclaimer

This dataset is credited to ieee8023. Use this dataset for research purpose.

COVID-19 image data collection

We are building a database of COVID-19 cases with chest X-ray or CT images. We are looking for COVID-19 cases as well as MERS, SARS, and ARDS.

All images and data will be released publicly in this GitHub repo. Currently we are building the database with images from publications as they are images that are already available.

View current images and metadata

Current stats of PA, AP, and AP Supine views. Labels 0=No or 1=Yes. Data loader is here ``` COVID19_Dataset num_samples=136 views=['PA'] {'ARDS': {0.0: 131, 1.0: 5}, 'Bacterial Pneumonia': {0.0: 119, 1.0: 17}, 'COVID-19': {0.0: 46, 1.0: 90}, 'Chlamydophila': {0.0: 135, 1.0: 1}, 'Fungal Pneumonia': {0.0: 123, 1.0: 13}, 'Klebsiella': {0.0: 135, 1.0: 1}, 'Legionella': {0.0: 134, 1.0: 2}, 'MERS': {0.0: 136}, 'No Finding': {0.0: 135, 1.0: 1}, 'Pneumocystis': {0.0: 123, 1.0: 13}, 'Pneumonia': {0.0: 1, 1.0: 135}, 'SARS': {0.0: 125, 1.0: 11}, 'Streptococcus': {0.0: 123, 1.0: 13}, 'Viral Pneumonia': {0.0: 35, 1.0: 101}}

COVID19_Dataset num_samples=28 views=['AP', 'AP Supine'] {'ARDS': {0.0: 28}, 'Bacterial Pneumonia': {0.0: 28}, 'COVID-19': {0.0: 4, 1.0: 24}, 'Chlamydophila': {0.0: 28}, 'Fungal Pneumonia': {0.0: 28}, 'Klebsiella': {0.0: 28}, 'Legionella': {0.0: 28}, 'MERS': {0.0: 28}, 'No Finding': {0.0: 28}, 'Pneumocystis': {0.0: 28}, 'Pneumonia': {0.0: 4, 1.0: 24}, 'SARS': {0.0: 28}, 'Streptococcus': {0.0: 28}, 'Viral Pneumonia': {0.0: 4, 1.0: 24}} ```

Contribute

• We can extract images from publications. Help identify publications which are not already included using a GitHub issue (DOIs we have are listed in the metadata file). There is a searchable database of COVID-19 papers here, and a non-searchable one (requires download) here.

• Submit data to https://radiopedia.org/ or https://www.sirm.org/category/senza-categoria/covid-19/ (we can scrape the data from them)

• Provide bounding box/masks for the detection of problematic regions in images already collected.

• See CONTRIBUTING.md for more information on the metadata schema.

Formats: For chest X-ray dcm, jpg, or png are preferred. For CT nifti (in gzip format) is preferred but also dcms. Please contact with any questions.

Background

The 2019 novel coronavirus (COVID-19) presents several unique features. While the diagnosis is confirmed using polymerase chain reaction (PCR), infected patients with pneumonia may present on chest X-ray and computed tomography (CT) images with a pattern that is only moderately characteristic for the human eye Ng, 2020. COVID-19’s rate of transmission depends on our capacity to reliably identify infected patients with a low rate of false negatives. In addition, a low rate of false positives is required to avoid further increasing the burden on the healthcare system by unnecessarily exposing patients to quarantine if that is not required. Along with proper infection control, it is evident that timely detection of the disease would enable the implementation of all the supportive care required by patients affected by COVID-19.

In late January, a Chinese team published a paper detailing the clinical and paraclinical features of COVID-19. They reported that patients present abnormalities in chest CT images with most having bilateral involvement Huang 2020. Bilateral multiple lobular and subsegmental areas of consolidation constitute the typical findings in chest CT images of intensive care unit (ICU) patients on admission Huang 2020. In comparison, non-ICU patients show bilateral ground-glass opacity and subsegmental areas of consolidation in their chest CT images Huang 2020. In these patients, later chest CT images display bilateral ground-glass opacity with resolved consolidation [Huang 2020](https://www.thelancet.com/journals/la...

Context

There's a story behind every dataset and here's your opportunity to share yours. There are four knowledge graphs related to SARS-COV-2 virus. 1. virusnetwork.taxonomy, taxonomy of most viruses, data from NCBI and some other databases. 2. virusnetwork.sars-cov-2, fundamental information about SARS-COV-2, data from NCBI and some other databases. 3. virusnetwork.drug, anti-virus drug related KG, data from drugbank and some other databases. 4. phylogeny of COVID-19, data from nextstrain database.

Content

Please read schema definitions in files extracted from schema.zip . Readme document is written in chinese now. We plan to provide an English version in the future.

Acknowledgements

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4820882%2F45a57ce93a1f5cc261b31a559659849a%2F2016-06-01-060520.78739420120321225402110b1332312087121.jpg?generation=1586158900288797&alt=media%20=60x20" alt="Zhejiang University"> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4820882%2F070bb42977f3b7f2dfcb376731274695%2F2020-03-10-125137.958002HWPOSRBGVertical-300ppi.jpg?generation=1586158939864852&alt=media%20=60x20" alt="Huawei Cloud"> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4820882%2Fd6bf353000ff7391ad3bf3ccd5449e54%2F2016-06-01-062020.396032c1s.png?generation=1586158864797139&alt=media%20=220x50" alt="OpenKG">

The dataset was published originally in OpenKG.cn ( http://openkg.cn/dataset/covid-19-research ). If you want to contact with maintainers, please follow this link and obtain their emails.

The COVID Symptom Tracker (https://covid.joinzoe.com/) mobile application was designed by doctors and scientists at King's College London, Guys and St Thomas’ Hospitals working in partnership with ZOE Global Ltd – a health science company.

This research is led by Dr Tim Spector, professor of genetic epidemiology at King’s College London and director of TwinsUK a scientific study of 15,000 identical and non-identical twins, which has been running for nearly three decades.

The dataset schema includes:

Demographic Information (Year of Birth, Gender, Height, Weight, Postcode) Health Screening Questions (Activity, Heart Disease, Diabetes, Lung Disease, Smoking Status, Kidney Disease, Chemotherapy, Immunosuppressants, Corticosteroids, Blood Pressure Medications, Previous COVID, COVID Symptoms, Needs Help, Housebound Problems, Help Availability, Mobility Aid) COVID Testing Conducted How You Feel? Symptom Description Location Information (Home, Hospital, Back From Hospital) Treatment Received The data is hosted within the SAIL Databank, a trusted research environment facilitating remote access to health, social care, and administrative data for various national organisations.

The process for requesting access to the data is dependent on your use case. SAIL is currently expediting all requests that feed directly into the response to the COVID-19 national emergency, and therefore requests from NHS or Government institutions, or organisations working alongside such care providers and policymakers to feed intelligence directly back into the national response, are being expedited with a ~48-hour governance turnaround for such applications once made. Please make enquiries using the link at the bottom of the page which will go the SAIL Databank team, or to Chris Orton at c.orton@swansea.ac.uk

SAIL is welcoming requests from other organisations and for longer-term academic study on the dataset, but please note if this is not directly relevant to the emergency research being carried out which directly interfaces with national responding agencies, there may be an access delay whilst priority use cases are serviced.

Please note: the CVST dataset in SAIL has not been updated since 01/11/2023.

This dataset requires additional governance approvals from the data provider before data can be provisioned to a SAIL project.

Description: Covid-19 aggregated data sets shall be issued on the basis of data sent to the Health Information System by health service providers. The date of statistics shall be based on the time of first receipt of vaccination data by the health information system and not on the date of vaccination. Depending on the documentation of health service providers and the sending of data, there may be nearly 1 day's reference to the receipt of data. Health service providers have the right and obligation to introduce corrections in the detection of errors which may affect retrospective statistics. The datasets are available in machine-readable JSON format and in CSV format. The metadata shall be published in the JSON Schema format. The datasets are updated 1 time a week, on Tuesdays from 12:00-12:30.

• Category: MEDICINE. BIOCHEMISTRY. ANATOMY. PHYSIOLOGY. COSMETICS SOCIAL POLICY DEMOGRAPHY. STATISTICS GOVERNMENT. ADMINISTRATION. CIVIL DEFENCE. SECURITY SERVICE
• Keywords: health data, health indicators, viral diseases, open access data, vaccines, health care, health statistics, national statistics, morbidity statistics
• Phone: +3727943943
• E-mail: itabi@tehik.ee
• Spatial/geographical coverage: All of Estonia
• Dataset language: Estonian
• Temporal coverage: 26.12.2020
• License: Creative Commons Attribution-ShareAlike 3.0 Unported
• Update frequency: Every week
• Landing page: https://opendata.digilugu.ee/docs/#/et/readme
• Co-author: Terviseamet, Sotsiaalministeerium
• Purpose of creation: COVID-19, SARS-CoV-2 statistics
• Temporal resolution: Day
• Added: 15:04 09.04.2021
• Last modified: 07:23 12.01.2023
• Regulation date: 30.12.2024
• Dataset's ID: b8c8924e-af58-42e5-aca3-62976b78313a
• Version: 9

CSCoV (Computational Studies about COVID-19) is a dataset containing COVID-19 related studies extracted from PubMed, bioRxiv, medRxiv, and arXiv, together with article and author related metrics obtained from Semantic Scholar (plus page views from bioRxiv and medRxiv). Using machine learning, the articles are categorized in six topics (Pharmacology, Genomics, Epidemiology, Healthcare, Clinical Medicine, Clinical Imaging) and prioritized. The database is periodically updated.

• Publication: TBA
• Files included in this release:
  • cscov_05_2021.png: dataset statistics for the current CSCoV release.
  • cscov_05_2021.tsv: CSCoV database.
  • schema.json: metadata.
  • cscov_05_2021.tar.gz: Doc2Vec and DeepWalk features used for the DL model

• Source code: https://github.com/SFB-KAUST/covid-review

Covid19Kerala.info-Data is a consolidated multi-source open dataset of metadata from the COVID-19 outbreak in the Indian state of Kerala. It is created and maintained by volunteers of ‘Collective for Open Data Distribution-Keralam’ (CODD-K), a nonprofit consortium of individuals formed for the distribution and longevity of open-datasets. Covid19Kerala.info-Data covers a set of correlated temporal and spatial metadata of SARS-CoV-2 infections and prevention measures in Kerala. Static releases of this dataset snapshots are manually produced from a live database maintained as a set of publicly accessible Google sheets. This dataset is made available under the Open Data Commons Attribution License v1.0 (ODC-BY 1.0).

Schema and data package Datapackage with schema definition is accessible at https://codd-k.github.io/covid19kerala.info-data/datapackage.json. Provided datapackage and schema are based on Frictionless data Data Package specification.

Temporal and Spatial Coverage

This dataset covers COVID-19 outbreak and related data from the state of Kerala, India, from January 31, 2020 till the date of the publication of this snapshot. The dataset shall be maintained throughout the entirety of the COVID-19 outbreak.

The spatial coverage of the data lies within the geographical boundaries of the Kerala state which includes its 14 administrative subdivisions. The state is further divided into Local Self Governing (LSG) Bodies. Reference to this spatial information is included on appropriate data facets. Available spatial information on regions outside Kerala was mentioned, but it is limited as a reference to the possible origins of the infection clusters or movement of the individuals.

Longevity and Provenance

The dataset snapshot releases are published and maintained in a designated GitHub repository maintained by CODD-K team. Periodic snapshots from the live database will be released at regular intervals. The GitHub commit logs for the repository will be maintained as a record of provenance, and archived repository will be maintained at the end of the project lifecycle for the longevity of the dataset.

Data Stewardship

CODD-K expects all administrators, managers, and users of its datasets to manage, access, and utilize them in a manner that is consistent with the consortium’s need for security and confidentiality and relevant legal frameworks within all geographies, especially Kerala and India. As a responsible steward to maintain and make this dataset accessible— CODD-K absolves from all liabilities of the damages, if any caused by inaccuracies in the dataset.

License

This dataset is made available by the CODD-K consortium under ODC-BY 1.0 license. The Open Data Commons Attribution License (ODC-By) v1.0 ensures that users of this dataset are free to copy, distribute and use the dataset to produce works and even to modify, transform and build upon the database, as long as they attribute the public use of the database or works produced from the same, as mentioned in the citation below.

Disclaimer

Covid19Kerala.info-Data is provided under the ODC-BY 1.0 license as-is. Though every attempt is taken to ensure that the data is error-free and up to date, the CODD-K consortium do not bear any responsibilities for inaccuracies in the dataset or any losses—monetary or otherwise—that users of this dataset may incur.

EU eHealthNetwork value sets as referenced by the EU Digital COVID Certificate (DCC) JSON Schema Published by European eHealth network - digital covid certificate coordination on github under Apache v2 license

Context

This dataset was created to assist with completion of the following Kaggle competition, https://www.kaggle.com/c/siim-covid19-detection/overview

Content

This dataset consists of 60 xray images of the chest region, and a corresponding numpy file of labels. Each numpy label file, consists of a 4x(width)x(height), where the first dimension specifies lungs 0 heart 1 diaphragm 2 spinal column 3 , and the remaining dimensions correspond to the image pixel locations. A pixel is 0 if the corresponding body part was not present, and 1 if present.

This data set was manually created using https://github.com/wkentaro/labelme to draw associated shapes encapsulating each body part.

Disclaimer (Caveat Emptor)

This labeled portion of the dataset was created by non-medical affiliated students. This dataset is a start, and meant to inspire others to create a more extensive database of similar images.

Citation

The BIMCV-COVID19 Data used by this challenge were originally published by the Medical Imaging Databank of the Valencia Region (BIMCV) in cooperation with The Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), and the Regional Ministry of Innovation, Universities, Science and Digital Society (Generalitat Valenciana), however the images were completely re-annotated using different annotation types. Users of this data must abide by the BIMCV-COVID19 Dataset research Use Agreement. Paper Reference: BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients

The MIDRC-RICORD Data used by this challenge were originally published by The Cancer Imaging Archive. The images were re-annotated for this challenge using a different annotation schema. Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution-NonCommercial 4.0 International License under which it has been published. Attribution should include references to citations listed on the TCIA citation information page (page bottom). Paper Reference: The RSNA International COVID-19 Open Radiology Database (RICORD) Citations & Data Usage Policy Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution-NonCommercial 4.0 International License under which it has been published. Attribution should include references to the following citations:

Data Citation

Tsai, E., Simpson, S., Lungren, M.P., Hershman, M., Roshkovan, L., Colak, E., Erickson, B.J., Shih, G., Stein, A.,Kalpathy-Cramer, J., Shen, J.,Hafez, M.A.F., John, S., Rajiah, P., Pogatchnik, B.P., Mongan, J.T., Altinmakas, E., Ranschaert, E., Kitamura, F.C., Topff, L., Moy, L., Kanne, J.P., & Wu, C. (2021). Data from Medical Imaging Data Resource Center (MIDRC) - RSNA International COVID Radiology Database (RICORD) Release 1c - Chest x-ray, Covid+ (MIDRC-RICORD-1c). The Cancer Imaging Archive. DOI: https://doi.org/10.7937/91ah-v663.

Publication Citation

Tsai, E. B., Simpson, S., Lungren, M., Hershman, M., Roshkovan, L., Colak, E., Erickson, B. J., Shih, G., Stein, A., Kalpathy-Cramer, J., Shen, J., Hafez, M., John, S., Rajiah, P., Pogatchnik, B. P., Mongan, J., Altinmakas, E., Ranschaert, E. R., Kitamura, F. C., … Wu, C. C. (2021). The RSNA International COVID-19 Open Annotated Radiology Database (RICORD). Radiology, 203957. DOI: https://doi.org/10.1148/radiol.2021203957

TCIA Citation

Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7

The global epidemic has been controlled to some extent, while sporadic outbreaks still occur in some places. It is essential to summarize the successful experience and promote the development of new drugs. This study aimed to explore the common mechanism of action of the four Chinese patent medicine (CPMs) recommended in the Medical Observation Period COVID-19 Diagnostic and Treatment Protocol and to accelerate the new drug development process. Firstly, the active ingredients and targets of the four CPMs were obtained by the Chinese medicine composition database (TCMSP, TCMID) and related literature, and the common action targets of the four TCMs were sorted out. Secondly, the targets of COVID-19 were obtained through the gene-disease database (GeneCards, NCBI). Then the Venn diagram was used to intersect the common drug targets with the disease targets. And GO and KEGG pathway functional enrichment analysis was performed on the intersected targets with the help of the R package. Finally, the results were further validated by molecular docking and molecular dynamics analysis. As a result, a total of 101 common active ingredients and 21 key active ingredients of four CPMs were obtained, including quercetin, luteolin, acacetin, kaempferol, baicalein, naringenin, artemisinin, aloe-emodin, which might be medicinal substances for the treatment of COVID-19. TNF, IL6, IL1B, CXCL8, CCL2, IL2, IL4, ICAM1, IFNG, and IL10 has been predicted as key targets. 397 GO biological functions and 166 KEGG signaling pathways were obtained. The former was mainly enriched in regulating apoptosis, inflammatory response, and T cell activation. The latter, with 92 entries related to COVID-19, was mainly enriched to signaling pathways such as Coronavirus disease—COVID-19, Cytokine-cytokine receptor interaction, IL-17 signaling pathway, and Toll-like receptor signaling pathway. Molecular docking results showed that 19/21 of key active ingredients exhibited strong binding activity to recognized COVID-19-related targets (3CL of SARS-CoV-2, ACE2, and S protein), even better than one of these four antiviral drugs. Among them, shinflavanone had better affinity to 3CL, ACE2, and S protein of SARS-CoV-2 than these four antiviral drugs. In summary, the four CPMs may play a role in the treatment of COVID-19 by binding flavonoids such as quercetin, luteolin, and acacetin to target proteins such as ACE2, 3CLpro, and S protein and acting on TNF, IL6, IL1B, CXCL8, and other targets to participate in broad-spectrum antiviral, immunomodulatory and inflammatory responses.

Schematic cohesion of sentimental clusters at different Im.