A High-Resolution Spatial Room Impulse Response Database:

A Database of various SRIRs measured in three rooms, for two receiver and 3 source positions each. The positions are shown in the floor plans.

Naming Convention:

Receivers: SMA (DRIRs): spherical microphone array impulse responses on a 2702 sampling point Lebedev grid.

KU100 (BRIRs): Neumann KU100 dummy head impulse responses on a 360 sampling point horizontal grid

Omni_ir: Omnidirectional impulse responses measured with an Earthworks M30 microphone

Receiver positions: P1, P2 as indicated in the floor plans

Source positions: LSL: left speaker, LSR: right speaker, LSC: center speaker as indicated in the floor plans

Rooms: Audiolab, Classroom, Audimax

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Contact:
Tim Lübeck, Johannes M. Arend, and Christoph Pörschmann
TH Köln - University of Applied Sciences
Institute of Communications Engineering
Department of Acoustics and Audio Signal Processing
Betzdorfer Str. 2, D-50679 Cologne, Germany

https://www.th-koeln.de/personen/tim.luebeck/

https://www.th-koeln.de/personen/johannes.arend/

https://www.th-koeln.de/personen/christoph.poerschmann/

Learning to Drive consultation response database. Information relating to the Learning To Drive consultation including copies of responses linked to the individuals and organisations who provided them.

This table contains data from the community survey conducted as part of an Audit of the City's Cultural Centers. We surveyed members of the Austin community using a survey developed by the audit team. Survey questions generally asked respondents' opinions on cultural center programs, staff, fees, and facilities. The survey opened January 3 and closed January 27, 2020. Austin community members were invited to take the survey through social media outreach and direct email invitations. The survey and outreach materials were written in English and translated into Spanish, Vietnamese, and Simplified Chinese. A total of 1,330 community members responded to the survey. Respondents were asked only to respond for centers they had visited in the last two years and could respond for more than one center. The comments detailed in this table were in response to open-ended survey items that allowed respondents to give opinions or suggestions about each center's programming, fees, staff, and facilities. Any open-ended responses answered in Spanish, Vietnamese, or Chinese were translated prior to analysis. To gauge the general sentiment of the responses, each was categorized as "Positive," "Negative," "Suggestion," or "N/A." During analysis, some comments were deemed more relevant to other open-ended survey items than the items for which they were originally written. These responses were re-assigned to the survey items that more closely aligned with their subject.

Dataset

This dataset was created by Charlie Zimmerman

Contents

DESCRIPTION

The TAU Spatial Room Impulse Response Database (TAU-SRIR DB) database contains spatial room impulse responses (SRIRs) captured in various spaces of Tampere University (TAU), Finland, for a fixed receiver position and multiple source positions per room, along with separate recordings of spatial ambient noise captured at the same recording point. The dataset is intended for emulation of spatial multichannel recordings for evaluation and/or training of multichannel processing algorithms in realistic reverberant conditions and over multiple rooms. The major distinct properties of the database compared to other databases of room impulse responses are:

Capturing in a high resolution multichannel format (32 channels) from which multiple more limited application-specific formats can be derived (e.g. tetrahedral array, circular array, first-order Ambisonics, higher-order Ambisonics, binaural).

Extraction of densely spaced SRIRs along measurement trajectories, allowing emulation of moving source scenarios.

Multiple source distances, azimuths, and elevations from the receiver per room, allowing emulation of complex configurations for multi-source methods.

Multiple rooms, allowing evaluation of methods at various acoustic conditions, and training of methods with the aim of generalization on different rooms.

The RIRs were collected by staff of TAU between 12/2017 - 06/2018, and between 11/2019 - 1/2020. The data collection received funding from the European Research Council, grant agreement 637422 EVERYSOUND.

NOTE: This database is a work-in-progress. We intend to publish additional rooms, additional formats, and potentially higher-fidelity versions of the captured responses in the near future, as new versions of the database in this repository.

REPORT AND REFERENCE

A compact description of the dataset, recording setup, recording procedure, and extraction can be found in:

Politis., Archontis, Adavanne, Sharath, & Virtanen, Tuomas (2020). A Dataset of Reverberant Spatial Sound Scenes with Moving Sources for Sound Event Localization and Detection. In Proceedings of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020), Tokyo, Japan.

available here. A more detailed report specifically focusing on the dataset collection and properties will follow.

AIM

The dataset can be used for generating multichannel or monophonic mixtures for testing or training of methods under realistic reverberation conditions, related to e.g. multichannel speech enhancement, acoustic scene analysis, and machine listening, among others. It is especially suitable for the follow application scenarios:

monophonic and multichannal reverberant single- or multi-source speech in multi-room reverberant conditions

monophonic and multichannel polyphonic sound events in multi-room reverberant conditions

single-source and multi-source localization in multi-room reverberant conditions, in static or dynamic scenarios

single-source and multi-source tracking in multi-room reverberant conditions, in static or dynamic scenarios

sound event localization and detection in multi-room reverberant conditions, in static or dynamic scenarios

SPECIFICATIONS

The SRIRs were captured using an Eigenmike spherical microphone array. A Genelec G Three loudspeaker was used to playback a maximum length sequence (MLS) around the Eigenmike. The SRIRs were obtained in the STFT domain using a least-squares regression between the known measurement signal (MLS) and far-field recording independently at each frequency. In this version of the dataset the SRIRs and ambient noise are downsampled to 24kHz for compactness.

The currently published SRIR set was recorded at nine different indoor locations inside the Tampere University campus at Hervanta, Finland. Additionally, 30 minutes of ambient noise recordings were collected at the same locations with the IR recording setup unchanged. SRIR directions and distances differ with the room. Possible azimuths span the whole range of $\phi\in[-180,180)$, while the elevations span approximately a range between $\theta\in[-45,45]$ degrees. The currently shared measured spaces are as follows:

Large open space in underground bomb shelter, with plastic-coated floor and rock walls. Ventilation noise. Circular source trajectory.

Large open gym space. Ambience of people using weights and gym equipment in adjacent rooms. Circular source trajectory.

Small classroom (PB132) with group work tables and carpet flooring. Ventilation noise. Circular source trajectory.

Meeting room (PC226) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.

Lecture hall (SA203) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.

Small classroom (SC203) with group work tables and carpet flooring. Ventilation noise. Linear source trajectory.

Large classroom (SE203) with hard floor and rows of desks. Ventilation noise. Linear source trajectory.

Lecture hall (TB103) with inclined floor and rows of desks. Ventilation noise. Linear source trajectory.

Meeting room (TC352) with hard floor and partially glass walls. Ventilation noise. Circular source trajectory.

The measurement trajectories were organised in groups, with each group being specified by a circular or linear trace at the floor at a certain distance from the z-axis of the microphone. For circular trajectories two ranges were measured, a close and a far one, except room TC352, where the same range was measured twice, but with different furniture configuration and open or closed doors. For linear trajectories also two ranges were measured, close and far, but with linear paths at either side of the array, resulting in 4 unique trajectory groups, with the exception of room SA203 where 3 ranges were measured resulting on 6 trajectory groups. Linear trajectory groups are always parallel to each other, in the same room.

Each trajectory group had multiple measurement trajectories, following the same floor path, but with the source at different heights.

The SRIRs are extracted from the noise recordings of the slowly moving source across those trajectories, at an angular spacing of approximately every 1 degree from the microphone. Instead of extracting SRIRs at equally spaced points along the path (e.g. every 20cm), this extraction scheme was found more practical for synthesis purposes, making emulation of moving sources at an approximately constant angular speed easier.

More details on the trajectory geometries can be found in the README file and the measinfo.mat file.

RECORDING FORMATS

As with the DCASE2019-2021 datasets, currently the database is provided in two formats, first-order Ambisonics, and a tetrahedral microphone array - both derived from the Eigenmike 32-channel recordings. For more details on the format specifications, check the README.

We intend to add additional formats of the database, of both higher resolution (e.g. higher-order Ambisonics), or lower resolution (e.g. binaural).

REFERENCE DOAs

For each extracted RIR across a measurement trajectory there is a direction-of-arrival (DOA) associated with it, which can be used as the reference direction for sound source spatialized using this RIR, for training or evaluation purposes. The DOAs were determined acoustically from the extracted RIRs, by windowing the direct sound part and applying a broadband version of the MUSIC localization algorithm on the windowed multichannel signal.

The DOAs are provided as Cartesian components [x, y, z] of unit length vectors.

SCENE GENERATOR

A set of routines is shared, here termed scene generator, that can spatialize a bank of sound samples using the SRIRs and noise recordings of this library, to emulate scenes for the two target formats. The code is similar to the one used to generate the TAU-NIGENS Spatial Sound Events 2021 dataset, and has been ported to Python from the original version written in Matlab.

The generator can be found here, along with more details on its use.

The generator at the moment is set to work with the NIGENS sound event sample database, and the FSD50K sound event database, but additional sample banks can be added with small modifications.

The dataset together with the generator has been used by the authors in the following public challenges:

• DCASE 2019 Challenge Task 3, to generate the TAU Spatial Sound Events 2019 dataset (development/evaluation)

• DCASE 2020 Challenge Task 3, to generate the TAU-NIGENS Spatial Sound Events 2020 dataset

• DCASE2021 Challenge Task 3, to generate the TAU-NIGENS Spatial Sound Events 2021 dataset

• DCASE2022 Challenge Task 3, to generate additional SELD synthetic mixtures for training the task baseline

NOTE: The current version of the generator is work-in-progress, with some code being quite "rough". If something does not work as intended or it is not clear what certain parts do, please contact us.

DATASET STRUCTURE

The dataset contains a folder of the SRIRs (TAU-SRIR_DB), with all the SRIRs per room in a single MAT file. The file rirdata.mat contains some general information such as sample rate, format specifications, and most importantly the DOAs of every extracted SRIR. The file measinfo.mat contains measurement and recording information in each room. Finally, the dataset contains a folder of spatial ambient noise recordings (TAU-SNoise_DB), with one subfolder per room having two audio recordings fo the spatial ambience, one for each format, FOA or MIC. For more information on how to SRIRs and DOAs are organized, check the README.

DOWNLOAD

The files TAU-SRIR_DB.z01, ..., TAU-SRIR_DB.zip contain the SRIRs and measurement info files.

The files TAU-SNoise_DB.z01, ..., TAU-SNoise_DB.zip

This room impulse response database was created by the Institute of Communication Systems (IKS) and the Institute for Hearing Technology and Acoustics (IHTA) at RWTH Aachen University (Germany).

Measurement Setup

The measurements were conducted in the IKS|Lab of the Institute of Communication Systems (IKS) at RWTH Aachen University. This room complies with the ITU-R BS.1116-3 recommendation by featuring a reverberation time of approximately T60 ≈ 0.25 s. The room impulse responses were measured using the mh acoustics Eigenmike em64 spherical microphone array.

Loudspeaker Arrangement

The IKS|Lab consists of 36 spherically arranged Neumann KH120D studio monitors, 32 of which are in a spherical arrangement. These loudspeakers are positioned on four different height levels. Specifically, there are 14 speakers at a height of 1.15 m from the floor on the middle level, 12 speakers at a height of 2.15 m from the floor on the upper level, 5 speakers at a height of 0.25 m from the floor on the ground level, and 5 speakers at a height of 2.7 m from the floor on the ceiling. The distance from the center of the room to each speaker is 1.6 m. Additionally, 4 loudspeakers are arranged on the ceiling in a circle with a radius of 1.2 m around the ceiling loudspeaker that is located on the sphere. The distance between the center and these 4 loudspeakers is 1.9 m.

Measurement Procedure

The room impulse responses were measured at 63 different positions. At each position, a logarithmic sweep signal with length of 10 second was played back by each of the 36 loudspeakers and recorded at the 64 channels of the microphone array, which yields 36 x 63 = 2268 source-receiver combinations and 2268 x 64 = 145152 room impulse responses in total. The measured room impulse responses were truncated to 1 second at a sampling rate of 48 kHz. The 63 microphone positions are distributed across three different height levels. Each height level contains 21 measurement positions arranged in a 5 × 5 rectangular grid, except for the corner positions. These were excluded because they are very close to some of the loudspeakers, which makes reasonable measurements difficult.

Folder Structure

In the folder em64, there are 63 subfolders, where each subfolder IR_xyz uses the naming convention xyz, with the indices denoting the microphone array position. In each subfolder IR_xyz, there are 36 .mat files from speaker_1.mat to speaker_36.mat, where each file name indicates the loudspeaker index and contains the 64-channel room impulse responses between the microphone array position xyz and the corresponding loudspeaker.

For a more detailed explanation, we refer the reader to the documentation that is part of the published data.

This database was presented in the 51st Annual Meeting on Acoustics (DAS|DAGA). If you wish to use the database in your research, please cite the corresponding paper as:

Chatzimoustafa, E., Heck, J., Patek, D., Jax, P., "A Comprehensive Room Impulse Response Database for Spatial Audio", in Proceedings of the 51st Annual Meeting on Acoustics (DAS|DAGA), Copenhagen, Denmark, March 2025.

SMS responses from automated database.

The Multichannel Impulse Response Database from Bar-Ilan university consists of measured RIRs with sources placed on a grid of [0◦, 180◦] in steps of 15◦, at distances of 1 m and 2 m from the array.

USEPA/National-Based Assets includes current radiological emergency response monitoring and sampling Resource-Type I, II, and III Equipment.

This data set contains dose-response data for perchlorate exposure in multiple species. These data were assembled from peer-reviewed studies. Species included in this dataset are: rats (Rattus sp.), meadow voles (Microtus sp.), rabbits (Oryctolagus cuniculus), the African clawed frog (Xenopus laevis), zebrafish (Danio rerio), mosquito fish (Gambusia holbrooki), the bobwhite quail (Colinus virginianus), earthworms (Eisenia foetida), mosquito larvae (Culex quinquefasciatus), the water flea (Daphnia magna), and the sand dollar (Peronella japonica). This dataset is associated with the following publication: Hines, D., S. Edwards, R. Conolly, and A. Jarabek. The Aggregate Exposure Pathway (AEP) and Adverse Outcome Pathway (AOP) frameworks facilitate the integration of human health and ecological endpoints for Cumulative Risk Assessment (CRA). ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 52(2): 839-849, (2018).

The emergency response database includes an emergency response technology database and a chemical characteristics database

We present a large set of simulated room impulse responses for a multi-room apartment. The simulated apartment models a real vacation apartment for which a recorded set of audio data has already been made available in the context of the DCASE challenges. The impulse responses were rendered using a dense grid of sources and receivers by means of a hybrid auralization algorithm based on a low-order image-source method and deterministic cone tracing. The proposed data set can be used to generate a wide variety of acoustic scenes which, in turn, can benefit numerous data-demanding machine-learning algorithms.

To obtain more information on the database, please visit the website. Please read the license file (available in the GitHub repository) before using the database.

AbstractPlants form belowground associations with mycorrhizal fungi in one of the most common symbioses on Earth. However, few large-scale generalizations exist for the structure and function of mycorrhizal symbioses, as the nature of this relationship varies from mutualistic to parasitic and is largely context-dependent. We announce the public release of MycoDB, a database of 4,010 studies (from 438 unique publications) to aid in multi-factor meta-analyses elucidating the ecological and evolutionary context in which mycorrhizal fungi alter plant productivity. Over 10 years with nearly 80 collaborators, we compiled data on the response of plant biomass to mycorrhizal fungal inoculation, including meta-analysis metrics and 24 additional explanatory variables that describe the biotic and abiotic context of each study. We also include phylogenetic trees for all plants and fungi in the database. To our knowledge, MycoDB is the largest ecological meta-analysis database. We aim to share these data to highlight significant gaps in mycorrhizal research and encourage synthesis to explore the ecological and evolutionary generalities that govern mycorrhizal functioning in ecosystems. Usage notesREADMEMycoDB_version4FungalTree_version2PlantTree_version2phylometa_2018

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

This dataset details service schedules for Demand Response (DR) modes for each applicable agency and type of service (TOS) reported to the National Transit Database for the 2024 report year.

NTD Data Tables organize and summarize data from the 2024 National Transit Database in a manner that is more useful for quick reference and summary analysis.

This data is a part of new reporting requirements as of 2023. Other datasets describing aspects of Demand Response Geographical Area Coverage can be found at the following links: Counties and Places: https://data.transportation.gov/Public-Transit/2024-NTD-Annual-Data-Demand-Response-Geographic-Ar/qifj-zz6e/about_data Passenger Eligibility and Requirements: https://data.transportation.gov/Public-Transit/2024-NTD-Annual-Data-Demand-Response-Geographic-Ar/i3pw-uwab/about_data

If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

MPORTANT NOTE: This provisional data is being provided as VDH OEMS continues to improve its data systems. The data on this page will continue to change throughout the data system improvement process and will stabilize over time. Thank you for your patience.

This dataset contains Emergency Medical Services (EMS) information for reported emergency response incidents that involve a substance or have suspected substance involvement. Data in this dataset has been provided by ESO on behalf of the Office of EMS.

Please be advised that the accuracy of the data within the EMS patient care reporting system is limited by system performance and the accuracy of data submissions received from EMS agencies. While each record in this dataset is for a single patient involved in an incident reported by an EMS agency, unique patients may be counted more than once in the dataset (e.g., if a patient was treated by two EMS agencies, that patient may be counted in the dataset twice). This data should not be interpreted as the number of unique substance use incidents reported by Virginia EMS agencies.

For instances where medication was administered to the patient, the response to the medication is provided, if reported by the EMS agency (e.g., if a patient received "naloxone" and the response of the patient for this administration of naloxone was reported as "Improved", then the record will show "naloxone with Improved response"). In instances where multiple medications were administered to the patient, the administrations and their associated responses are provided as a pipe-delimited list in the order that the patient received the medications.

This dataset has been classified as a Tier 0 asset by the Commonwealth Data Trust. Tier 0 classifies a data resource as information that is neither sensitive nor proprietary, and intended for public access.

The Transgenic Rodent Assay Dose-response Database contains all available dose-response data for the Transgenic Rodent Somatic and Germ Cell Mutation Assay. The assay is used to detect agents that can induce genetic mutations and is conducted as described in the Organization for Economic Cooperation and Development (OECD) test guideline 488. The database contains data from experiments conducted using transgenic rodents (i.e., mice or rats). The experiments were aimed at assessing the mutagenic activity of selected agents (e.g., chemicals) by exposing the rodents to the compounds by various exposure routes (e.g., drinking water, food, injection, etc.). The database contains the results of animal studies that tested chemicals at two or more doses; each dataset contains data for a concurrent control. The database is useful for dose-response modeling (e.g., benchmark dose analyses) to determine a point of departure that may subsequently be used for human health hazard and/or risk assessment.

The Response Measures Database (RMD) of the European Centre for Disease Prevention and Control (ECDC) and the Joint Research Centre (JRC) of the European Commission is a regularly updated archive of non-pharmaceutical interventions (NPIs) introduced by 30 countries in the European Union (EU) and European Economic Area (EEA) since 1 January 2020 in response to the COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

The Iowa Department of Natural Resources (DNR) has six field offices (FO) who are local representatives to help people understand DNR’s environmental programs. Field staff conduct inspections for facilities with DNR environmental permits, and respond to spills and complaints.

This Field Office Compliance Database tracks the major actions of FO staff, and shows compliance issues found by the FOs. Compliance issues before 2009 or started by the Central Office may not be included.

Emergency Response Spills received by the TCEQ are assigned an Incident Tracking Number. The information submitted by the reporting party is documented and associated to that unique number and then further investigated. An Incident Tracking Number may be listed more than once if there are multiple Customer Names, Released Materials, Media, and/or Effects.