This is an api that provides continuous real time as well as historic data from the network of air quality monitoring stations that are part of the national air quality monitoring network managed in cooperation between the Environmental Protection Agency and Dublin City Council, as well as other stations set up by Dublin City Council to monitor local air quality conditions. This api also provides access to Dublin City Council's network of environmental sound level monitors. For more information, visit https://dublincityairandnoise.ie/

API operated by Louisville Metro that returns AQI information from local sensors operated by APCD. Shows the latest hourly data in a JSON feed.The Air Quality Index (AQI) is an easy way to tell you about air quality without having to know a lot of technical details. The “Metropolitan Air Quality Index” shows the AQI from the monitor in Kentuckiana that is currently detecting the highest level of air pollution. See: https://louisvilleky.gov/government/air-pollution-control-district/servi...See the air quality map (Louisville Air Watch) for more details: airqualitymap.louisvilleky.gov/#Read the FAQ for more information about the AQI data: https://louisvilleky.gov/government/air-pollution-control-district/louis...If you'd prefer air quality forecast data (raw data, maps, API) instead, please see AIRNow: https://www.airnow.gov/index.cfm?action=airnow.local_city&zipcode=40204&...See the Data Dictionary section below for information about what the AQI numbers mean, their corresponding colors, recommendations, and more info and links.To download daily snapshots of AQI for the last 25 years, visit the EPA website, set your year range, and choose, Louisville KY. Then download with the CSV link at the bottom of the page.IFTTT integration trigger that fires and after retrieving air quality from Louisville Metro air sensors via the APIGives a forecast instead of the current conditions, so you can take action before the air quality gets bad.The U.S. EPA AirNow program (www.AirNow.gov) protects public health by providing forecast and real-time observed air quality information across the United States, Canada, and Mexico. AirNow receives real-time air quality observations from over 2,000 monitoring stations and collects forecasts for more than 300 cities.Sign up for a free account and get started using the RSS data feed for Louisville. https://docs.airnowapi.org/feedsAir Quality Forecast via AirNowAQI Level - Value and Related Health Concerns LegendGood 0-50 GreenAir quality is considered satisfactory, and air pollution poses little or no risk.Moderate 51-100 YellowAir quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups 101-150 OrangeMembers of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy 151-200 RedEveryone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy 201-300 PurpleHealth alert: everyone may experience more serious health effects.Hazardous > 300 Dark PurpleHealth warnings of emergency conditions. The entire population is more likely to be affected.Here are citizen actions APCD recommends on air quality alert days, that is, days when the forecast is for the air quality to reach or exceed the “unhealthy for sensitive groups” (orange) level:Don’t idle your car. (Recommended all the time; see the second link below.)Put off mowing grass with a gas mower until the alert ends.“Refuel when it’s cool” (pump gasoline only in the evening or night).Avoid driving if possible. Share rides or take TARC.Check on neighbors with breathing problems.Here are some links in relation to the recommendations:KAIRE, www.helptheair.org/Idle Free Louisville, www.helptheair.org/idle-freeTARCTicket to Ride, tickettoride.org/Lawn Care for Cleaner Air (rebates)Contact:Bryan FrazerBryan.Frazar@louisvilleky.gov

Success.ai’s LinkedIn Data Solutions offer unparalleled access to a vast dataset of 700 million public LinkedIn profiles and 70 million LinkedIn company records, making it one of the most comprehensive and reliable LinkedIn datasets available on the market today. Our employee data and LinkedIn data are ideal for businesses looking to streamline recruitment efforts, build highly targeted lead lists, or develop personalized B2B marketing campaigns.

Whether you’re looking for recruiting data, conducting investment research, or seeking to enrich your CRM systems with accurate and up-to-date LinkedIn profile data, Success.ai provides everything you need with pinpoint precision. By tapping into LinkedIn company data, you’ll have access to over 40 critical data points per profile, including education, professional history, and skills.

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• Comprehensive Data Points for LinkedIn Profiles: Our LinkedIn profile data includes over 40 key data points for every individual and company, ensuring a complete understandin...

OpenAQ has collected 231,965,688 air quality measurements from 8,469 locations in 65 countries. Data are aggregated from 105 government level and research-grade sources. https://medium.com/@openaq/where-does-openaq-data-come-from-a5cf9f3a5c85 Note: this dataset is temporary not updated. We're currently working to update it as soon as possible.Disclaimers:- Some records contain encoding issues on specific characters; those issues are present in the raw API data and were not corrected.- Some dates are set in the future: those issues also come from the original data and were not corrected.

This dataset contains the metadata of the datasets published in 77 Dataverse installations, information about each installation's metadata blocks, and the list of standard licenses that dataset depositors can apply to the datasets they publish in the 36 installations running more recent versions of the Dataverse software. The data is useful for reporting on the quality of dataset and file-level metadata within and across Dataverse installations. Curators and other researchers can use this dataset to explore how well Dataverse software and the repositories using the software help depositors describe data. How the metadata was downloaded The dataset metadata and metadata block JSON files were downloaded from each installation on October 2 and October 3, 2022 using a Python script kept in a GitHub repo at https://github.com/jggautier/dataverse-scripts/blob/main/other_scripts/get_dataset_metadata_of_all_installations.py. In order to get the metadata from installations that require an installation account API token to use certain Dataverse software APIs, I created a CSV file with two columns: one column named "hostname" listing each installation URL in which I was able to create an account and another named "apikey" listing my accounts' API tokens. The Python script expects and uses the API tokens in this CSV file to get metadata and other information from installations that require API tokens. How the files are organized ├── csv_files_with_metadata_from_most_known_dataverse_installations │ ├── author(citation).csv │ ├── basic.csv │ ├── contributor(citation).csv │ ├── ... │ └── topic_classification(citation).csv ├── dataverse_json_metadata_from_each_known_dataverse_installation │ ├── Abacus_2022.10.02_17.11.19.zip │ ├── dataset_pids_Abacus_2022.10.02_17.11.19.csv │ ├── Dataverse_JSON_metadata_2022.10.02_17.11.19 │ ├── hdl_11272.1_AB2_0AQZNT_v1.0.json │ ├── ... │ ├── metadatablocks_v5.6 │ ├── astrophysics_v5.6.json │ ├── biomedical_v5.6.json │ ├── citation_v5.6.json │ ├── ... │ ├── socialscience_v5.6.json │ ├── ACSS_Dataverse_2022.10.02_17.26.19.zip │ ├── ADA_Dataverse_2022.10.02_17.26.57.zip │ ├── Arca_Dados_2022.10.02_17.44.35.zip │ ├── ... │ └── World_Agroforestry_-_Research_Data_Repository_2022.10.02_22.59.36.zip └── dataset_pids_from_most_known_dataverse_installations.csv └── licenses_used_by_dataverse_installations.csv └── metadatablocks_from_most_known_dataverse_installations.csv This dataset contains two directories and three CSV files not in a directory. One directory, "csv_files_with_metadata_from_most_known_dataverse_installations", contains 18 CSV files that contain the values from common metadata fields of all 77 Dataverse installations. For example, author(citation)_2022.10.02-2022.10.03.csv contains the "Author" metadata for all published, non-deaccessioned, versions of all datasets in the 77 installations, where there's a row for each author name, affiliation, identifier type and identifier. The other directory, "dataverse_json_metadata_from_each_known_dataverse_installation", contains 77 zipped files, one for each of the 77 Dataverse installations whose dataset metadata I was able to download using Dataverse APIs. Each zip file contains a CSV file and two sub-directories: The CSV file contains the persistent IDs and URLs of each published dataset in the Dataverse installation as well as a column to indicate whether or not the Python script was able to download the Dataverse JSON metadata for each dataset. For Dataverse installations using Dataverse software versions whose Search APIs include each dataset's owning Dataverse collection name and alias, the CSV files also include which Dataverse collection (within the installation) that dataset was published in. One sub-directory contains a JSON file for each of the installation's published, non-deaccessioned dataset versions. The JSON files contain the metadata in the "Dataverse JSON" metadata schema. The other sub-directory contains information about the metadata models (the "metadata blocks" in JSON files) that the installation was using when the dataset metadata was downloaded. I saved them so that they can be used when extracting metadata from the Dataverse JSON files. The dataset_pids_from_most_known_dataverse_installations.csv file contains the dataset PIDs of all published datasets in the 77 Dataverse installations, with a column to indicate if the Python script was able to download the dataset's metadata. It's a union of all of the "dataset_pids_..." files in each of the 77 zip files. The licenses_used_by_dataverse_installations.csv file contains information about the licenses that a number of the installations let depositors choose when creating datasets. When I collected ... Visit https://dataone.org/datasets/sha256%3Ad27d528dae8cf01e3ea915f450426c38fd6320e8c11d3e901c43580f997a3146 for complete metadata about this dataset.

API for querying Queensland's live air quality data published on https://apps.des.qld.gov.au/air-quality/

This portal is intended to serve as a means to access all available NMED data via secure Application Programming Interface (API).

To access our data, we require that you register with the portal.

You can browse the titles and a short description of our available APIs before registering.

Anyone who registers will be provided access to all of NMED’s public APIs.

Members of the regulated community served by NMED, as well as partner agencies and other official entities, can, during registration, request an elevated account that provides access to certain protected data APIs. Such requests will need to register using an official email account and potentially provide other means of verification before these account requests are approved.

Once registered, you will be able to fully explore and consume our collection of APIs, and access Open API Specification documentation that will help you understand, learn, and leverage those APIs for your purposes.

We hope you enjoy your visit to the NMED Open Data Portal and return many times.

Noise and Air Quality Monitoring API DCC. Published by Dublin City Council. Available under the license cc-by (CC-BY-4.0).This is an api that provides continuous real time as well as historic data from the network of air quality monitoring stations that are part of the national air quality monitoring network managed in cooperation between the Environmental Protection Agency and Dublin City Council, as well as other stations set up by Dublin City Council to monitor local air quality conditions. This api also provides access to Dublin City Council's network of environmental sound level monitors. For more information, visit https://dublincityairandnoise.ie/

To convert datetime to unix timestamp, you can use this converter: https://wtools.io/convert-date-time-to-unix-time...

This dataset provides a list of observations/measurements for parameters collected across all stations. We have limited it to the latest results (10000 results). For a more complete set of data, or filtered data visit the API directly - https://apps.des.qld.gov.au/air-quality/ The Queensland Department of the Environment, Tourism, Science and Innovation (DETSI) in collaboration with industry partners operates an air quality monitoring network across the state.Data from the monitoring network is presented online as ambient concentration, air quality categories and smoke and dust health action levels which are updated hourly.

Context

OpenAQ has collected 231,965,688 air quality measurements from 8,469 locations in 65 countries. Data are aggregated from 105 government level and research-grade sources.

Disclaimers

• Some records contain encoding issues on specific characters; those issues are present in the raw API data and were not corrected.
• Some dates are set in the future: those issues also come from the original data and were not corrected.

Acknowlegement

Foto von JuniperPhoton auf Unsplash

Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.

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Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.

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From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new...

Data from autonomous buoy platforms deployed in sea and sea ice by Norwegian Polar Institute from 1976 to present.

The buoy data is published as JSON documents in a REST-style HTTP data API found at https://api.npolar.no/oceanography/buoy/?q=

Buoy data subsets * N-ICE2015 buoy data * Barneo-2016 buoy data * ICEX buoy data 1976-1979

Quality

A quality flag scheme is applied

# THE QUALITY FLAG SCHEME # 1 Good Passed documented required QC tests # 2 Not evaluated, not available or unknown Used for data when no QC test performed or the information on quality is not available # 3 Questionable/suspect Failed non-critical documented metric or subjective test(s) # 4 Bad Failed critical documented QC test(s) or as assigned by the data provider # 9 Missing data Used as place holder when data are missing

sea_ice_temperature => if number of uniq values are < 10 => set quality to 4

# set argos buoy IC_2015a to quality 3

A quality flag scheme has been applied * quality 1: good (used for quality-controlled data) * quality 2: not evaluated (used for unprocessed data) * quality 4: bad (used for bad sensor data) * quality 9: missing (used for missing positions)

Buoy API

See [] Publishing the buoy data in NPI's API platform brings several benefits:

• Data is available in a number of common formats, including CSV/TSV, GeoJSON.
• Data is available in any time-resolution (see below).
• Data is retrievable in multiple geometries (point, line string).
• Data may be subsetted by bounding box, timespan, or any variable in the data
• Data is described and validated (prior to publishing) by a machine readable schema

Examples of Buoy API usage * Last 10 buoy messages as GeoJSON points * GeoJSON LineString with 24h-average positions of buoy SVP_2015 * TSV with only the variables listed in the fields parameter (also limited to one type of buoy using filter-type= and with maximum number of results set to limit=100)

• Script for processing
• Platform deployment data
• Original data stored
• Identification: IMEI.

Variables

Space and time * measured: Observation time (UTC) RFC3339 * positioned: GPS time (UTC) RFC3339 * latitude: WGS84 decimal degrees * longitude: WGS84 decimal degrees

Meterology * air_pressure [ barometric_pressure (11538) barometric_pressure1 (11538) barometric_pressure2 (11538)] * air_pressure_tendency * air_temperature [air_temperature1 (11538) air_temperature2 (11538)] * air_pressure_at_sea_level

** Sea water ** * sea_surface_temperature

Reference * Intergovernmental Oceanographic Commission of UNESCO. (2013). Ocean Data Standards, Vol.3: Recommendation for a Quality Flag Scheme for the Exchange of Oceanographic and Marine Meteorological Data. (IOC Manuals and Guides, 54, Vol. 3.) 12 pp. (English.)(IOC/2013/MG/54-3)

Access Success.ai's 28M verified company profiles featuring comprehensive B2B contact data across all industries. With API integration, seamlessly incorporate up-to-date company data into your systems aligned to your needs, ensuring the most reliable information at the best price worldwide.

This API provides access to the Aquifer Mapping Program's groundwater level and water chemistry database. The database is updated on a semi-regular basis with new manually measured depth to groundwater measurements. Some of the data collected by NMBGMR is private and requires appropriate privileges and authentication for access. All endpoints that begin with /authorized require authentication. At NMBGMR, we use different tools to collect groundwater level measurements, including continuous data recorders and manual measurements. All data provided here are in feet, depth to water, below ground surface (BGS). We use pressure transducers to record pressure of water over a device installed in the well, which is converted to feet of water and depth to water. We provide here up to one measurement per hour where the data are that frequent. In some locations we have more data available. We also use continuous acoustic sounder devices which convert a sound reflection into a measurement of depth to water. These can be used for long term trends in groundwater levels. While we do our best to review and quality check these data, please use these data with caution. Site-specific conditions should be verified, especially for legally binding decisions. Data are subject to changes, deletion, or being moved without notice at any time and should not be relied on for any critical application. Any opinions expressed may not necessarily reflect the official position of the New Mexico Bureau of Geology and Mineral Resources, New Mexico Tech, or the State of New Mexico. No warranty, expressed or implied, is made regarding the accuracy or utility of the data for general or scientific purposes.

OpenAQ has collected 231,965,688 air quality measurements from 8,469 locations in 65 countries. Data are aggregated from 105 government level and research-grade sources. https://medium.com/@openaq/where-does-openaq-data-come-from-a5cf9f3a5c85 Note: this dataset is temporary not updated. We're currently working to update it as soon as possible.Disclaimers:- Some records contain encoding issues on specific characters; those issues are present in the raw API data and were not corrected.- Some dates are set in the future: those issues also come from the original data and were not corrected.

Data prepared for the implementation of the Bathing Water Directive.

The Environment Monitoring API published by the Environment Protection Authority (EPA) Victoria provides access to real-time information related to environment quality, notices and forecasts collected from monitoring stations all over Victoria. Air quality data comes directly from air monitoring stations across Victoria using various types of monitoring equipment and methods. This data may be adjusted according to set criteria to account for instrument errors, power interruptions and other …Show full descriptionThe Environment Monitoring API published by the Environment Protection Authority (EPA) Victoria provides access to real-time information related to environment quality, notices and forecasts collected from monitoring stations all over Victoria. Air quality data comes directly from air monitoring stations across Victoria using various types of monitoring equipment and methods. This data may be adjusted according to set criteria to account for instrument errors, power interruptions and other technical issues. Additional environment monitoring data, such as water quality, will be progressively made available via this API.

2017 primary validated assessment data for all sampling points where measurement data is collected for the purpose of the assessment and reciprocal exchange of information.

On https://maq-observations.nl/ we offer all operational measurements from three sites: Veenkampen, Loobos and Amsterdam. These observations include, but do not limit themselves to, temperature, humidity, wind, radiation, soil moisture, soil temperature, energy and carbon fluxes and air quality (e.g. NOx, O3, PM). We offer up-to date charts of the current weather and air quality, as well as a historic dataset to explore, plot and download. You can visualize and download the data with our carefully designed graphical user interface, or download it using a custom tailored API key. We offer up to 627 data streams divided over the three stations at a temporal resolution of up to 20 seconds. Loobos is located in a Pine forest the Veluwe natural park, one of the largest European natural areas. Pine are the dominant species in the Veluwe. Hence Loobos is representative for a much larger acreage. The site was first established in 1995 and was home to one of the 3 first ecosystem flux sites globally. In 2021 a new tower was built and equipped. The research at Loobos aims at understanding the role of the ecosystem in the carbon and water cycle. As the forest, planted in the 1910’s, grows, it takes up CO2 from the atmosphere. This is an important feedback to climate change. But the amount of uptake depends on availability of water, temperature, radiation and air quality. Hence it is a very dynamic system, which we try to understand and predict. The research is done by MAQ at Wageningen University with Utrecht and Delft University as partners via the Ruisdael Observatory. Forest research is done by other groups in Wageningen as well, e.g. the GIS and Remote Sensing group collects LIDAR measurements of ecosystem geometry. We welcome other experimental groups and campaigns. A 36 m tall tower is built at the site in 2021, hosting eddy covariance instruments (CO2, heat, evaporation, Volatile Organic Compounds), radiation instruments, profiles of temperature, water vapour, CO2, wind speed. Soil temperature, moisture and heat flux and water table depth measurements are collected around the tower.

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