Find research datasets worth reusing

opencc-by-4.0Nov 2021View details →

Weather and Air Quality data for Ireland as RDF data cube

Weather, Air Pollution and Events data represented as RDF data cube. The original weather data has been downloaded from https://www.met.ie//climate/available-data/historical-data and the Air Quality data from <a href="https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm">https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm</a> and <a href="https://discomap.eea.europa.eu/map/fme/AirQualityExportAirbase.htm">https://discomap.eea.europa.eu/map/fme/AirQualityExportAirbase.htm</a>. The Events data refers to random events within the Republic of Ireland. The data has then been uplifted by running the {eeaMapping, metMapping, eventsMapping}.py scripts, which generate R2RML mappings to convert the CSV data to RDF. The mappings re-use vocabularies and ontologies that are W3C recommendations for dataset descriptions (DCAT, https://www.w3.org/TR/vocab-dcat-2/), statistical data (RDF Data Cube, https://www.w3.org/TR/vocab-data-cube/) and provenance data (PROV-O, https://www.w3.org/TR/prov-o/). The scripts use the R2RML engine from https://github.com/chrdebru/r2rml to execute the mappings which generate a data and metadata files for each of the datasets.

opencc-by-4.0Mar 2022View details →

Modified WRF/Chem source code, output data, and post-processing scripts for the GMD manuscript "Evaluation of WRF/Chem model (v3.9.1.1) real-time air quality forecasts over the Eastern Mediterranean"

Here you will find the modified WRF/Chem code used in the simulations, the scripts used for post-processing and the model output data used in the manuscript.  Two modifications have been made in module_aerosols_soa_vbs.F: <ol> <li>ch_dust is set to1.0D-9*0.36</li> <li>The model is set not to initialize during restarts</li> </ol> The model data directory includes: <ol> <li>Two csv files (Winter and Summer) with the hourly concentrations of atmospheric pollutants at the locations of the ground stations. These data were used to produce Figures 4-8 in the manuscript as well as all the metrics.</li> <li>Two netcdf files (Winter and Summer) with the average ground concentrations of atmospheric pollutants over Cyprus. These data were use to produce Figure 3 in the manuscript. </li> </ol>

opencc-by-4.0Oct 2024View details →

COMPAIR traffic and air quality sensor data

Sensor data regarding traffic and air quality was gathered as part of the <a href="https://cordis.europa.eu/project/id/101036563">EU Horizon2020 COMPAIR project</a> in Europe. The pilot cities/regions are Berlin, Athens, Sofia, Plovdiv, and Flanders. During the project, the data was published through an <a href="https://sensorthings.wecompair.eu/FROST-Server/v1.1/Things">OGC SensorThings API</a>. To persist after the project, the air quality related are available as CSV exports, with the retention of the API's structure (Location, Thing, Datastream, Sensor, ObservedProperty, and Observation). Observations about air quality contain sensor readings regarding nitrodioxide (NO2), black carbon (BC), particulate matter (PM1.0, PM2.5 and PM10), humidity and temperature. The NO2 observations are calibrated data streams. The traffic observations remain available through the <a href="https://app.swaggerhub.com/apis-docs/telraam/Telraam-API/1.2.0">API of the Telraam platform</a>.

opencc-by-4.0Apr 2023View details →

Air quality, soil moisture, green roof moisture and weather data from Meetjestad

Soil moisture sensors were developed by citizen science collective Meet je Stad (Measure your City). Measure your City was started in 2015 by inhabitants of the City of Amersfoort, with the goal of measuring climate related indicators. To be able to do so, collaboration was sought with the City of Amersfoort (COA), the local Water Authority and the University of Applied Sciences of Amsterdam. For the first three years the initiative focused on measuring temperature and humidity. Importantly, citizens develop their own research questions, analyze the data together with professionals and discuss potential implications. By doing so, the collective uses citizen science to spread knowledge on both technology and climate change in the most grass-roots manner possible. Within the SCOREwater project, Measure your City was asked to expand measurements with soil moisture measurements and additional temperature and humidity sensors. An important note here is that Measure your City develops their own sensors, has developed their own data platform and uses its own gateways purchased from the Things Network. As a result, much effort is put into constructing sensors that are reliable, low-maintenance and accurate. The latter is important for the City of Amersfoort as well, which intends to not only work on shared knowledge and understanding, but also use the data for policy making. To do so the data has to be reliable. By deploying both these sensors and purchasing company-built sensors, we can compare the data to assess how reliable the Measure your City sensors are. The Measure your City can also be deployed on green roofs to measure soil moisture. Whereas the soil moisture sensor measures soil moisture on two depths (10 centimeter and 40 centimeter), the sensor on a roof only measures soil moisture on one depth. In addition to soil moisture, Measure your City also measures air temperature and relative humidity. Some sensors also measure air quality (particle matter).

opencc-by-4.0Dec 2023View details →

Weekly county-level pollution data for China from Zhang, Carleton, Lin, and Zhou (accepted, Nature Sustainability), "Estimating the role of air quality improvements in the decline of suicide rates in China"

This dataset contains weekly, county-level air pollution data for 2,839 counties from 2013 to early 2018. These data are used and described in Zhang, Carleton, Lin, and Zhou (accepted, Nature Sustainability), "Estimating the role of air quality improvements in the decline of suicide rates in China". When the paper is published a link to the manuscript will be added here.  The manuscript Methods section details data construction. In summary, these county-level observations are obtained from monitoring stations maintained by the China National Environmental Monitoring Center (CNEMC), which is affiliated with the Ministry of Ecology and Environment of China. CNEMC began publishing hourly air pollution data in 2013, including the Air Quality Index, PM2.5, PM10, ozone, sulfur dioxide, nitrogen dioxide, and carbon monoxide. We average hourly data to the station-day level and use inverse-distance weighting with a radius of 200km to convert data from station to the county level. We average across days to generate county-level weekly values. Any missing station-hour observations in the raw data are omitted in this spatial and temporal aggregation. Our main analysis relies on PM2.5, but all pollutants are released here.

opencc-by-4.0Feb 2022View details →

Data used to create figures and tables in the ACP manuscript "Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality" by Gao et al. (2022)

This dataset contains the original data that extracted from all collected papers refering applications of two-way coupled models in Asia. It is supplied to the review paper, which titled as "Review on two-way coupled meteorology and air quality models in Asia: impacts of aerosol feedbacks on meteorology and air quality". The dataset includes three excel files (in the format of xlsx) as follows: 1. Basic information of literatures (Table S1.xlsx) 2. Model performance metrics (Table S2.xlsx) 3. Quantitative results of aerosol effects on meteorological and air quality variables (Table S3.xlsx) 4. Basic information of model setup for two-way coupled model applications in Asia (Table S4.xlsx) 5. Summary of aerosol-induced variations of simulated shortwave and longwave radiative forcing at the bottom and top of atmosphere and in the atmosphere in Asia (Table S5.xlsx) .

opencc-by-4.0Jul 2018View details →

UK shale gas air and water quality data

Datasets for UK coal bed methane compositions (Airth field), shale gas composition from Bowland shale operations and produced water composition from UK Airth field.

opencc-by-4.0Sep 2018View details →

Air quality data created by the hackAIR Horizon2020 project

The current datasets comprise of air quality data collected or created within the hackAIR project (https://platform.hackair.eu/) all around Europe from February 2018 until November 2018. i) "measurements_arduino.xlsx": PM10 and PM2.5 measurements collected by hackAIR users with stationary hackAIR sensors (https://www.hackair.eu/hackair-home-v2/). These sensing devices are based either on an Arduino or a Wemos board. The first column is the unique identifier for the measurement in the hackAIR database. The date/time is in UTC timezone, while the unit of the pollutant value is μg/m3. ii) "measurements_bleair.xlsx": PM10 and PM2.5 measurements collected by hackAIR users with mobile hackAIR sensors (https://www.hackair.eu/hackair-mobile/). The first column is the unique identifier for the measurement in the  database. The date/time is in UTC timezone, while the unit of the pollutant value is μg/m3. iii) "measurements_sky_photos.xlsx": Air pollution estimations from photos depicting sky. The hackAIR platform estimates the particulate matter content in the air from Flickr photos, photos from webcams and sky photos that users upload on the hackAIR mobile application, based on the colour of the sky. This is expressed as Aerosol Optical Depth (AOD). In the current dataset, the timezone is UTC, while AOD is unitless. The pollutant index is based on a scale created for the purposes of the hackAIR project.

opencc-by-4.0Jul 2024View details →

AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations

This datalake comprises several datasets from 37 networked low cost air quality multisensors (30 mobile ENEA MONICA(tm) + 7 fixed) along with 3 (fixed) + 1 (mobile) reference stations operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file. Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a pervasive, multi-site, multi-seasonal deployment in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy. The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with   deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. . Further description of sensors and reference instruments are reported in the accompanying paper (see citation request). The dataset can be used for  <ul> <li> advanced (remote/universal/in field) data driven calibration strategies test or development including machine learning models</li> <li>mobile opportunistic data fusion methods development</li> <li>geomatics and data assimilation models studies</li> </ul> as well as low cost sensor characterization performance studies.

opencc-by-4.0Oct 2023View details →

Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. Data Set for Final Data Report

The 43 txt-files included in this dataset relate to the report: Ryhl-Svendsen, Jensen, Bøhm, and Klenz Larsen (2012): Low-energy Museum Storage Buildings: Climate, Energy Consumption and Air Quality. UMTS Research Project 2007–2011: Final Data Report, Kgs. Lyngby: National Museum of Denmark, 122 pp. The document <a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/00_List-of-data-files.pdf?versionId=a3e9691f-6e73-4a7b-aaab-c8ccee7c419b">00_List-of-data-files.pdf</a> contain a full list of the data files with a description of their structure and content, and is the key to how the individual data files relate to the report.  The research project focussed on four modern museum storage facilities in Denmark, for which the indoor climate, air quality, and the energy consumption of the climate control systems was measured at several locations, typically for a period of between two and four years. The storage facilities were Museum of Southwest Jutland’s storage building in Ribe (‘Ribe’), The Shared Storage Facility at The Centre for Preservation of Cultural Heritage in Vejle (‘Vejle’), The Joint Storage Facility for museums in East Jutland/ Museum Østjylland (‘Randers’), and from The National Museum of Denmark the storage building Hall P at the Ørholm Storage Facility (‘Ørholm’). For description of the sites, monitoring campaigns, and graphed data, the report should be consulted. For completeness, the report is included with the dataset (<a href="https://zenodo.org/api/files/145584b0-46b5-4341-8b02-7dfea90fa97c/Report_low-energy-museum-storage-buildings.pdf?versionId=44097d39-775b-4031-9e07-6978c68912a9">Report_low-energy-museum-storage-buildings.pdf</a>).

opencc-by-4.0Mar 2023View details →

Data for the submitted paper by Yasunari et al., "Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments"

The dataset contains some of the outputs from the global climate model experiments by MIROC5 on changing Siberian wildfire severities, the other data used in the paper (see READ_ME files on the data sources), the analyzed data, and the scripts for analyses, which were used in the following submitted paper. Note that this dataset also includes unused data for the paper: Yasunari, T. J., D. Narita, T. Takemura, S. Wakabayashi, and A. Takeshima, Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments, submitted. Please read the READ_ME files for detailed information in each directory (especially see the "about_figures_and_tables/" directory first). Because of their large sizes, the data were separated into three zipped files.

opencc-by-4.0Jun 2022View details →

Data for "Measurement Report: A Multi-Year Study on the Impacts of Chinese New Year Celebrations on Air 1 Quality in Beijing, China."

These are the datasets that have been used for the article "Measurement Report: A Multi-Year Study on the Impacts of Chinese New Year Celebrations on Air 1 Quality in Beijing, China," which is published in the journal Atmospheric Chemistry and Physics, by Foreback et al. (2022).

opencc-by-4.0Sep 2022View details →

Air quality and noise data in Santo Domingo and Santiago de los Caballeros, Dominican Republic from May 18,2020 to January 26, 2021

Air and noise pollution affect the quality of life of any community. The data presented observes the noise level, eight air quality parameters and three weather parameters. The air quality parameters are carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2); three particle-matter variables: ultrafine particulate matter (PM1), fine particulate matter (PM2.5), and coarse particulate matter (PM10). The weather parameters are temperature, relative humidity and atmospheric pressure. The empirical measurements were collected every ten or twenty minutes in two cities of the Dominican Republic from May 18th, 2020 to January 26th, 2021. The data can provide insight on the changes in air quality and noise level and can be used to compare with other variables such as traffic conditions.

openmit-licenseApr 2022View details →

Code and data used in "A Tool for Air Pollution Scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies"

Data and code for Tool for Air Pollution Scenarios (TAPS v1.0) as submitted to Geoscientific Model Development for publication. See the enclosed README and full user manual (https://github.com/watkin-mit/TAPS/wiki) for more information.

opencc-by-4.0May 2024View details →

Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States: Data

The data in this repository are associated with the manuscript from Huber et al. (2024) titled "Soil Moisture, Soil NOx and Regional Air Quality in the Agricultural Central United States" in the Journal of Geophysical Research: Atmospheres. Additional information regarding these data can be found in the attached readme file.

opencc-by-4.0Jul 2018View details →

Marcellus shale water and air quality data

Data summary for water and air quality of the Marcellus shale

opencc-by-4.0Sep 2024View details →

Data from multi-sensor devices and reference station to monitoring urban air quality

Data from electrochemical and optical sensors. <h3>Files names</h3> <ul> <li>ECT01, ECT02, ECT06, ECT07 = device name</li> <li>ISSEP = reference station <ul> <li>"c" = calibration data</li> <li>"v" = validation data</li> </ul> </li> </ul> <h3>Variable description</h3> <table> <tbody> <tr> <td>Electrochemical sensor</td> <td>Optical sensor</td> <td>Probe</td> <td>Reference</td> </tr> <tr> <td> AE = auxiliary electrode (mV) WE = working electrode (mV) N = temperature correction  <ul> <li>ch0 = CO sensor</li> <li>ch1 = OX sensor</li> <li>ch2 = NO2 sensor</li> <li>ch3 = NO sensor</li> </ul> </td> <td> PM1, PM2.5 and PM10 in µg/m³ </td> <td> Prs_mbar = pressure (mbar) Temp = temperature (°C) RH = relative humidity (%) </td> <td> DV30 = wind direction @ 30m (°) HR = relative humidity (%) NO, NO2, O3, PM10 and PM2.5 (µg/m³) Precipita = precipitation (mm) TC3 = temperature @ 3m (°C) VV30 = wind speed @ 30m (m/s) </td> </tr> </tbody> </table>

openmit-licenseFeb 2023View details →

Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk, Data

This dataset accompanies the publication, "Nuclear Power Generation Phaseouts Redistribute U.S. Air Quality and Climate Related Mortality Risk", and can be used with the code located at https://zenodo.org/badge/latestdoi/248010532 to reproduce our results.