Research projects funded by the National Institutes of Health (NIH), other DHHS Operating Divisions (ACF, AHRQ, CDC, FDA, HRSA), and the Department of Veterans Affairs. The ExPORTER files provide weekly and/or yearly snapshots of the data publicly accessible through the NIH Research Portfolio Online Reporting Tools, Expenditures and Results (RePORTER) system at https://reporter.nih.gov. The RePORTER database can also be queried using the user interface or the API. The RePORTER database contains information such as project title, abstract, principal investigator, funded organization, total awarded costs, categorization by area of research (NIH only), and project keywords. Also available is information on research publications and patents that have cited support from each project.

LinkOut is a service that allows you to link directly from PubMed and other NCBI databases to a wide range of information and services beyond the NCBI systems. LinkOut aims to facilitate access to relevant online resources in order to extend, clarify, and supplement information found in NCBI databases. Third parties can link directly from PubMed and other Entrez database records to relevant Web-accessible resources beyond the Entrez system. Includes full-text publications, biological databases, consumer health information and research tools.

The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) is a national genetics data repository facilitating access to genotypic and phenotypic data for Alzheimer's disease (AD). Data include GWAS, whole genome (WGS) and whole exome (WES), expression, RNA Seq, and CHIP Seq analyses. Data for the Alzheimer’s Disease Sequencing Project (ADSP) are available through a partnership with dbGaP (ADSP at dbGaP). Results are integrated and annotated in the searchable genomics database that also provides access to a variety of software packages, analytic pipelines, online resources, and web-based tools to facilitate analysis and interpretation of large-scale genomic data. Data are available as defined by the NIA Genomics of Alzheimer’s Disease Sharing Policy and the NIH Genomics Data Sharing Policy. Investigators return secondary analysis data to the database in keeping with the NIAGADS Data Distribution Agreement.

The Learning Resources Database is a catalog of interactive tutorials, videos, online classes, finding aids, and other instructional resources on National Library of Medicine (NLM) products and services. Resources may be available for immediate use via a browser or downloadable for use in course management systems.

The table is an overview of database and online systems to manage/publish prosopographical and biographical data.

Project - Huntingtin structure-function open lab notebook.

Rationale - To identify different huntingtin interaction partners.

Overview - Different online databases which detail protein interaction partners were searched for huntingtin protein interaction partners. Data detailing huntingtin interaction partners from 9 different databases was extracted and simplified – worksheets 1-15. The information from each database was collated – worksheet 16. Huntingtin protein interaction partners were ranked according to the number of databases they were found in as well as the number of different experiments detailing the interaction with huntingtin – worksheet 17.

This is the main dataset which consist a list all relevant details of the CS Track database. This dataset contains the following information from the CS Track database:

Citizen Science (CS) projects title

the data extracted date

the language of the CS projects informations

the URL(s) of the website(s) from where the CS projects information was extracted. For other studies developed in CS Track consortium it might be useful to consult this data

full list of assignments for research areas and SDGs for each CS project.

Objective: Routinely collected health data, collected for administrative and clinical purposes, without specific a priori research questions, are increasingly used for observational, comparative effectiveness, health services research, and clinical trials. The rapid evolution and availability of routinely collected data for research has brought to light specific issues not addressed by existing reporting guidelines. The aim of the present project was to determine the priorities of stakeholders in order to guide the development of the REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. Methods: Two modified electronic Delphi surveys were sent to stakeholders. The first determined themes deemed important to include in the RECORD statement, and was analyzed using qualitative methods. The second determined quantitative prioritization of the themes based on categorization of manuscript headings. The surveys were followed by a meeting of RECORD working committee, and re-engagement with stakeholders via an online commentary period. Results: The qualitative survey (76 responses of 123 surveys sent) generated 10 overarching themes and 13 themes derived from existing STROBE categories. Highest-rated overall items for inclusion were: Disease/exposure identification algorithms; Characteristics of the population included in databases; and Characteristics of the data. In the quantitative survey (71 responses of 135 sent), the importance assigned to each of the compiled themes varied depending on the manuscript section to which they were assigned. Following the working committee meeting, online ranking by stakeholders provided feedback and resulted in revision of the final checklist. Conclusions: The RECORD statement incorporated the suggestions provided by a large, diverse group of stakeholders to create a reporting checklist specific to observational research using routinely collected health data. Our findings point to unique aspects of studies conducted with routinely collected health data and the perceived need for better reporting of methodological issues.

This is the master consensus database of the Wormbiome genome collection.This tab-delimited file contains the curated annotations from all the bacteria present in the database.

The Wormbiome collection is an online database dedicated to centralizing all the information related to bacteria associated with C. elegans. More information on wormbiome.org.

This database assembles different published datasets of observed interaction networks between plants and pollinators, which were extracted from articles, theses and existing online databases.

Each row in the data table corresponds to an interaction between a plant and a pollinator species reported at a given site by a given publication.

Integrated Grants is a virtual database currently indexing funded research resources including NIH Research Portfolio Online Reporting Tool (RePORT) (current grants, updated monthly) and ResearchCrossroads (1970-2008, defunct as of 2009).

This database builds upon the collection assembled by Brose et al. (2005), and represents the largest standardised collection of trophic links for freshwater organisms, of which we are aware.

https://sites.google.com/site/foodwebsdatabase/

The data set contains the CRISPR screening data for AsPC1 exposed three chemotherapeutics, with a custom CRISPR Knockout library. The custom library targets 996 genes for which a currently identified drug was identified for the corresponding gene product. The complete description of this library is included in Methods and the data file, in .xls format, contains the corresponding sgRNAs for each gene.

The data file has separate tabs containing the therapeutic genome library sgRNAs, the primers used in the study, the raw counts for each sample, and the MaGeCK analysis for comparison of each treated sample with the T0 puro sample as described in methods.

The Dietary Supplement Ingredient Database (DSID) provides estimated levels of ingredients in dietary supplement products sold in the United States. These statistically predicted estimates may differ from labeled amounts and are based on chemical analysis of nationally representative products. The DSID was developed by the Nutrient Data Laboratory, US Department of Agriculture, in collaboration with the Office of Dietary Supplements at the National Institutes of Health (NIH) and other federal agencies. DSID-4 reports national estimates of ingredient content in adult, children’s and non-prescription prenatal multivitamin/mineral (MVMs) and omega-3 fatty acid supplements. New! Analytically-validated mean estimates for vitamin D, vitamin A and chromium in adult MVMs are reported for the first time, and estimates for 18 other ingredients have been calculated based on a new, second study of representative adult MVMs. DSID-4 also reports results for the first DSID study of botanical dietary supplements. The “Green Tea Research Summary and Results" are available on the 'Botanicals' page. The DSID is intended primarily for research applications. These data are appropriate for use in population studies of nutrient intake rather than for assessing content of individual products. Resources in this dataset:Resource Title: The Dietary Supplement Ingredient Database (DSID), Release 4. File Name: Web Page, url: https://dietarysupplementdatabase.usda.nih.gov/ provides estimated levels of ingredients in dietary supplement products sold in the United States. These statistically predicted estimates may differ from labeled amounts and are based on chemical analysis of nationally representative products. The DSID was developed by the Nutrient Data Laboratory, US Department of Agriculture, in collaboration with the Office of Dietary Supplements at the National Institutes of Health (NIH) and other federal agencies. DSID-4 reports national estimates of ingredient content in adult, children’s and non-prescription prenatal multivitamin/mineral (MVMs) and omega-3 fatty acid supplements.

TOXLINE was the National Library of Medicine (NLM) bibliographic database for toxicology, a varied science encompassing many disciplines. TOXLINE records provide bibliographic information covering the biochemical, pharmacological, physiological, and toxicological effects of drugs and other chemicals. TOXLINE references were drawn from various sources organized into component subfiles.

This version of TOXLINE is no longer updated. Updated TOXLINE content is available in PubMed or by searching PubMed using the search string: tox [sb] .

WP3 of CORE-MD investigated how to aggregate and extract maximal value for post-market surveillance from medical device registries, big data, clinical practices and experience, and the internet. This data collection was created by the Task 3.2 of the CORE-MD project, as the result of the proposed methodological framework to transform unstructured and dispersed publicly available safety information (Field Safety Notices, recalls, alerts) into a standardized and harmonized database. The databases includes 137,720 historical safety notices (updated to February 2024) safety notices published by different competent national authorities (16 EU Member States and 5 extra EU jurisdictions).

ARCOS Database provided by the Washington Post. We use the data for the empirical analysis in our article`` Retail Pharmacies and Drug Diversion during the Opioid Epidemic’’.

2006--2012 data from the Automation of Reports and Consolidated Orders System (ARCOS), maintained by the Diversion Control Division of the US Drug Enforcement Administration (DEA).

The data can be downloaded from https://www.washingtonpost.com/national/2019/07/18/how- download-use-dea-pain-pills-database/ in raw format and until 2021 through an R package (API) on https://github.com/wpinvestigative/arcos. Please follow the requirement of the Washington Post: ‘If you publish an online story, graphic, map or other piece of journalism based on this data set,
please credit The Washington Post, link to the original source, and send us an email when you’ve hit publish. We want to learn what you discover and will attempt to link to your work as part of cataloguing the impact of this project.” (The Washington Post, 2019)

The Washington Post. How to download and use the DEA pain pills database, 2019. https://www.washingtonpost.com/national/2019/07/18/how-download-use-dea-pain-pills-database/

The Stanford University HIV Drug Resistance Database is a curated public database designed to represent, store, and analyze the different forms of data underlying HIVs drug resistance. HIVDB has three main types of content: (1) Database queries and references, (2) Interactive programs, and (3) Educational resources. Database queries are designed primarily for researchers studying HIV drug resistance. The interactive programs and educational resources are designed for both researchers and those wishing to learn more about HIV drug resistance. 1.DATABASE QUERY AND REFERENCE PAGES Genotype-Treatment Correlations This Genotype-Treatment section of the database links to 15 interactive query pages that explore the relationship between treatment with HIV-1 antiretroviral drugs (ARVs) and mutations in HIV reverse transcriptase (RT), protease, and integrase. There are five types of interactive query pages: Treatment Profiles (Protease and RT inhibitors) Mutation Profiles (Protease and RT mutations) Detailed Treatment Queries (Protease, RT, and integrase inhibitors) Detailed Mutation Queries (Protease, RT, and integrase mutations) Mutation Prevalence According to Subtype and Treatment Genotype-Phenotype Correlations The main page of the Genotype-Phenotype Correlations section links to four interactive query pages: three dynamically updated data summaries and one regularly updated downloadable dataset. Drug Resistance Positions Query for levels of resistance associated with known drug resistance mutations Detailed Phenotype Queries Queries for levels of resistance associated with individual mutations or mutation combinations at all positions of protease, RT, and integrase Patterns of Drug Resistance Mutations Downloadable Reference Dataset Genotype-Clinical Correlations This part of the database has two main sections: Clinical Trials Datasets Summaries of Clinical Studies References This part of the database has two main sections: one with summaries of the data from each of the references in HIVDB and one in which every primate immunodeficiency virus sequence in GenBank is annotated according to its presence or absence in HIVDB. Studies in HIVDB GenBank HIVDB New Submissions Approximately every three months, the New Submissions section lists the studies that have been entered into HIVDB. The study title links to the introductory page of the study in the References section. Database Statistics (http://hivdb.stanford.edu/pages/HIVdbStatistics.html) 2. INTERACTIVE PROGRAMS HIVDB has seven main interactive programs. 1. HIVdb Program Mutation List Analysis Sequence Analysis HIVdb Output Sierra Web Service Release Notes Algorithm Specification Interface (ASI) 2. HIValg Program 3. HIVseq Program 4. Calibrated Population Resistance (CPR) tool 5. Mutation ARV Evidence Listing (MARVEL) 6. ART-AiDE 7. Rega HIV-1 Subtyping tool Three programs in the HIV Drug Resistance Database share a common code base: HIVseq, HIVdb, and HIValg. HIVseq accepts user-submitted protease, RT, and integrase sequences, compares them to the consensus subtype B reference sequence, and uses the differences as query parameters for interrogating the HIV Drug Resistance database (Shafer, D Jung, & B Betts, Nat Med 2000; Rhee SY et al. AIDS 2006). The query result provides users with the prevalence of protease, RT and integrase mutations according to subtype and PI, nucleoside RT inhibitor (NRTI), non-nucleoside RT inhibitor (NNRTI), and integrase inhibitor (INI) exposure. This allows users to detect unusual sequence results immediately so that the person doing the sequencing can check the primary sequence output while it is still on the desktop. In addition, unexpected associations between sequences or isolates can be discovered by immediately retrieving data on isolates sharing one or more mutations with the sequence. There are three ways in which the HIVdb program can be used: (i) entering a list of protease and RT mutations, (ii) entering a complete sequence containing protease, RT, and/or integrase, and (iii) using a Web Service. HIVdb is an expert system that accepts user-submitted HIV-1 pol sequences and returns inferred levels of resistance to 20 FDA-approved ARV drugs including 8 PIs, 7 NRTIs, 4 NNRTIs, and - with this update - one INI. In the HIVdb system, each HIV-1 drug resistance mutation is assigned a drug penalty score and a comment; the total score for a drug is derived by adding the scores of each mutation associated with resistance to that drug. Using the total drug score, the program reports one of the following levels of inferred drug resistance: susceptible, potential low-level resistance, low-level resistance, intermediate resistance, and high-level resistance. HIValg is designed for users interested in comparing the results of different algorithms or who are interested in comparing and evaluating existing and newly developed algorithms. The ability to develop new algorithms that can be run on the HIV Drug Resistance Database depends on the Algorithm Specific Interface (ASI) compiler (Shafer & Betts JCM 2003). Submission of Sequences and Mutations For each of the three programs, sequences can be entered using either the Sequence Analysis Form or the Mutation List form. 3. EDUCATIONAL RESOURCES HIVDB contains several regularly updated sections summarizing data linking RT, protease, and integrase mutations and antiretroviral drugs (ARVs). These sections include (i) tabular summaries of the major mutations associated with each ARV class, (ii) detailed summaries of the major, minor, and accessory mutations associated with each ARV, (iii) the comments used by the HIVdb program, (iv) the scores used by the HIVdb program, (v) clinical studies in which baseline drug resistance mutations have been correlated with the virological response (clinical outcome) to a specific ARV, (vi) mutations that can be used for drug resistance surveillance, and (vii) a two-page PDF handout. 1. Drug Resistance Summaries Tabular Drug Resistance Summaries by ARV Class Detailed Drug Resistance Summaries by ARV Drug Resistance Mutation Comments Used by the HIVdb Program Drug Resistance Mutation Scores Used by the HIVdb Program Genotype-Clinical Outcome Correlation Studies 2. Surveillance Drug-Resistance Mutation List Section 3. PDF Handout Grant Support 1. National Institute for Allergy and Infectious Diseases (NIAID, NIH): Online HIV Drug Resistance Database (PI: Robert W. Shafer, MD, 1R01AI68581-01A1), 04/01/06 - 3/31/11 2. National Institute for Allergy and Infectious Diseases (NIAID, NIH) supplement to the grant Identification of Multidrug-Resistant HIV-1 Isolates (PI: Robert W. Shafer, MD, AI46148-01): Supplement provided 1999-2005. 3. NIH/NIGMS Program Project on AIDS Structural Biology Program Project: Targeting Ensembles of Drug Resistant Protease Variants (PI: Celia Schiffer, PhD, University of Massachusetts): 2002-2007 4. University-wide AIDS Research Program (CR03-ST-524). Community collaborative award: Optimizing Clinical HIV Genotypic Resistance Interpretation: Principal Investigators: Robert W. Shafer, MD and W. Jeffrey Fessel MD (Kaiser Permanente Medical Care Program): 2004-2005 5. Stanford University Bio-X Interdisciplinary Initiative: HIV Gene Sequence Analysis for Drug Resistance Studies: A Pharmacogenetic Challenge Principal Investigators: Robert W. Shafer, MD and Daphne Koller, Ph.D. (Computer Science): 2000-2002

These files can be categorized into three groups: (1) raw datasets obtained from public databases (i.e., GBIF, BOLD, and GenBank), (2) manually edited files needed for parsing and analysis, and (3) supplementary files for spatial analysis. All are used in the examination of gaps and biases present in Philippine biodiversity data, which can direct research on the taxa and spatial regions that need more sampling.

Asian Palmante Group database (AsPGdb) is a cleaned database for the 23 genera of the Asian Palmate Group (AsPG) of the ginseng family Araliaceae. Data has been collected from March 2018 to April 2020, from multiple data sorces, mostly open access online databases, especially GBIF.

Each record in the database contains descriptive information about the taxa and different variables related to the geographic location of each record. In addition, each record has climatic information according to the following classifications: latitudinal zonation, Köppen’s classification (Köppen and Geiger, 1936), Holdridge’s classification (Holdridge, 1996), Metzger’s classification (Metzger et al., 2012) and Ecoregions system (Olson et al., 2001). We developed climate layers of these bioclimatic classifications for use in Geographic Information Systems, either by modifying existing layers (Dinerstein et al., 2017; Beck et al., 2018) or by creating new ones (all layers are available in GitHub repository: https://github.com/vvalnun/Bioclimatic-classifications-AsPG.git. The complete description of the AsPG database is found in the file named "Supplementary material 1_Database description_V2". The file describes in detail the information found in each of the columns.

To compile the AsPGdb, we clean invalid records, correct spatial uncertainty and extract climatic data for all the records of the AsPG database, using R (R Core Team, 2018) and QGIS (QGIS Development Team, 2021).

DOI references from GBIF downloads are provided in the "References_GBIF_AsPG" file.