Find research datasets worth reusing

openCC (other)Aug 2025View details →

Air and soil temperature data from the Reference Stand network at the Andrews Experimental Forest, 1971 to present

The current network of temperature measurement sites are designed to represent spatial variability of air and soil temperature in rugged mountain topography, and serve as second-level stations to capture specific microclimate temperatures in conjunction with a network of Benchmark Meteorological Stations (MS001). The air and soil thermograph network has been reduced from the historical network of 37 sites originally established. Currently there are 10 measurement sites with two of these sites measuring relative humidity in addition to air and soil temperature. An original network of 19 sites (RS01-RS19) were established during the International Biome Program in the early 1970's. Emphasis on phenology, plant moisture stress, and leaf nutrient content led to extending this network of air and soil temperature measurement. A plant community classification system (Dyrness et al., 1971) was used as a primary means of stratification, and a set of permanent vegetation plots (Reference Stands) was installed to represent forest communities with distinct vegetation and hypothesized different environments (Dyrness et al., 1974). A thermograph network was installed within the reference stands in the early 1970's (Zobel et al., 1974), and vegetation standing crop, tree growth and mortality, and plant succession were also measured. The majority of these sites were established to monitor micro-meteorological data under the canopy. The purpose of this network was to provide air and soil temperature data for modeling photosynthesis, respiration, phenology, and decomposition, and to measure environmental gradients.

openCC (other)Oct 2025View details →

Stream and air temperature data from stream gages and stream confluences in the Andrews Experimental Forest, 1950 to present

Stream and air temperature are measured in tandem at stream gauging stations and other selected locations and stream confluences within the Andrews Forest. Air temperature is generally measured over the stream or alongside. Currently, mean, max and min water and air temperature data are collected every 5 minutes at the gauging stations and instantaneous temperatures every 15 minutes at all other sites. Most measurements were collected hourly commencing in the later 1990s, but a few sites have daily data beginning in the late 1970s. Historic data collected 1949 to 1981 at Lookout Creek stream gauge are included with the daily summary data. Other Andrews Forest related databases: Long term air temperature data from the reference and benchmark climate stations are also available in MS001. Previous high resolution stream temperature data at some of the small watershed stream gages are available in HT001 and stream temperature data throughout the Andrews Forest stream networks during several years are available in HT002.

openCC (other)Apr 2023View details →

Cold air drainage transect studies at the Andrews Experimental Forest, 2002 to Present

Temperature data are being collected to investigate the effects of elevation and local topographic position on the patterns of cold air drainage and pooling in the HJ Andrews Forest, and how they vary with synoptic weather patterns. The main focus of the study is cold air drainage in the Lookout Creek area, but others sites have been added to sample temperature signatures at other locations of the forest with topographic positions that need to be better understood. Here, quality controlled 15-minute raw data and aggregated daily minimum and maximum temperatures are available from several stations along 4 separate transects or clusters with some additional single sensors also included. Several of the quality flags indicate that the data failed the test and should not be used in analysis.

openCC (other)Apr 2023View details →

Air temperature at core phenology sites and additional bird monitoring sites in the Andrews Experimental Forest, 2009 to present

The H.J Andrews phenology study air temperature network includes 16 core phenology sites, 40 core bird sites and 128 auxiliary bird sites. This study examines air temperatures at multiple sites within the Andrews Experimental Forest. Air temperatures were recorded 1.5 m above ground at 184 sites distributed on an 800-m incomplete grid throughout much of the Andrews Forest. Data were collected using automated sensors starting in June of 2009 at 56 sites and in June 2011 128 additional sensors were added. These data document the complex spatial and temporal patterns of air temperature variation within the Andrews Forest, which is governed by multiple processes including inversions, regional air mixing, cold air drainage and pooling, and the effects of vegetation on temperature extremes. The data entities provided indicate various methods of data quality checking over time.

openCC0Feb 2025View details →

Perceptions of heat and air pollution among older adults experiencing homelessness in Phoenix, Arizona (USA) (June 2024)

This dataset consists of survey responses from 40 older adults experiencing homelessness in Phoenix, Arizona (USA), assessing the perceptions of environmental hazards—specifically heat and air pollution—and attitudes toward coping resources and behaviors. The survey includes 51 questions co-created with community members across five categories: demographics and behavior, movement/transportation, climate perceptions, resource availability, and local knowledge mapping. Surveys were conducted indoors at a local service provider over two days in June 2024, when outdoor temperatures reached 42 degrees C and 45 degrees C. The dataset offers insights into potential public service reforms to mitigate heat and air pollution risks among Arizona’s unhoused population. The survey was approved by the Institutional Review Board of Arizona State University (IRB approval number: STUDY00018399).

openCC0Mar 2025View details →

Air and Soil Temperature in Hemlock Removal Experiment at Harvard Forest since 2004

The impending loss of hemlock trees due to hemlock woolly adelgid (Adelges tsugae) infestation is expected to lead to changes in the temperature regime of the forest understory. These changes will both drive succession and will themselves be altered by successional processes. Air temperature (1 m above forest floor) and soil temperature (at 10 cm depth) are measured at 1-minute intervals (hourly averages, minimum, and maximum are stored) using thermistors connected to Campbell 21-X dataloggers. Temperature measurements began 60-120 days prior to applications of the logging and girdling treatments, and will continue for the duration of the study.

openCC0Dec 2023View details →

Concentrations and Surface Exchange of Air Pollutants at Harvard Forest EMS Tower since 1990

In North America, anthropogenic activities such as fossil fuel combustion and high-intensity agriculture have increased the inputs of nitrogen oxides in the atmosphere far above natural, biogenic inputs. The effect of this excess N depends on how it is distributed through the environment. If fixed N is deposited as nitrate in forests, it may act as a "fertilizer", stimulating growth and thus enhancing carbon sequestration. But when accumulated deposition exceeds the nutritional needs of the ecosystem, nitrogen saturation may result. Soil fertility declines due to leaching of cations and thus, carbon uptake diminishes. The balance between fertilization and saturation depends on the spatial and temporal extent of nitrogen deposition. Measurements of nitrogen oxide concentrations and fluxes made at Harvard Forest are intended to quantify the deposition of nitrogen oxides and to examine the rates for oxidation and deposition of reactive nitrogen that are critical in controlling how far the influence of nitrogen oxide emission sources extends. Measurements made to date indicate that dry deposition of NOy to the Harvard Forest canopy is controlled by advection from source regions, vertical mixing, and chemical reaction. The input is about equally divided between wet and dry deposition depending on the amount of precipitation. Southwesterly winds bring air from the major urban areas along the mid-Atlantic coast, whereas northwesterly wind bring air from less populated regions of northern New England and Canada. As a result, southwesterly winds transport higher concentrations and fluxes of NOx and NOy than northwesterly winds. In the summer, aerodynamically rough forests intercept NOx and emit reactive hydrocarbons that accelerate the oxidation of NOx to rapidly depositing species. As a result, much of the NOx emitted by North America is retained by the region in the summer. This deposition leads to a summertime decrease in reactive nitrogen concentrations and fluxes relati

openCC (other)Feb 2025View details →

Air temperature data for C1 chart recorder, 1952 - ongoing.

Temperature data were collected on a daily time-scale from the C1 climate station (3018 m) since 1952. Over time various circumstances have led to days with missing values. Some missing values were estimated from redundant sensors and nearby climate stations using various methods. Greenland 1987 was a basis for the methodology. However when it was not possible to use this methodology, new methods were developed.

openCC (other)Feb 2025View details →

Air temperature data for D1 chart recorder, 1952 - ongoing.

Temperature data were collected on a daily time-scale from the D1 climate station (3743 m) since 1952. Over time, various circumstances have led to days with missing values. Some missing values were estimated from redundant sensors and nearby climate stations using various methods. Greenland 1987 was a basis for the methodology. However when it was not possible to use this methodology, new methods were developed.

zenodo56/100

Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2021-01-01 to 2021-12-31 [L2]

Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level. <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2021.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> For more details read `FRCHEM_2021_AirTemperature_MetaData.txt`. Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.

openCC (other)Oct 2025View details →

Air Quality Index (AQI) data from PurpleAir sensor at H.J. Andrews Experimental Forest LTER

This dataset contains hourly air quality and meteorological measurements collected from a PurpleAir sensor deployed at the H.J. Andrews Experimental Forest Long Term Ecological Research (LTER) site. The data includes four variables: timestamp (in Pacific Time), relative humidity (%), temperature (°C), and particulate matter concentrations (PM2.5 in µg/m³ using the CF=1 correction factor). The sensor provides continuous monitoring of local air quality conditions, with particular focus on fine particulate matter that can impact ecosystem health and visibility. Data are recorded at hourly intervals and timestamped in ISO 8601 format with UTC offset. PM2.5 values are reported using PurpleAir's CF=1 (Correction Factor 1) algorithm, which is optimized for atmospheric particulate matter. This dataset supports long-term environmental monitoring objectives at the Andrews Forest LTER and provides baseline air quality data for research on atmospheric conditions, wildfire smoke impacts, and climate-ecosystem interactions in Pacific Northwest forest ecosystems.

openCC (other)Oct 2025View details →

Air temperature and humidity, and soil temperature data from the Arctic LTER Moist Non-acidic Tussock Experimental plots (MNT97), Toolik Lake Field Station, Alaska, 1999-2025.

In 1999, a Campbell CR10x data logger was installed in block 2 of the Arctic LTER Toolik Moist Non-acidic Tussock Experimental plots(MNT97). The plots are located on a hillside near Toolik Lake (68 38' N, 149 36'W). Air temperature and relative humidity were measured at 3 meters (control), and inside the greenhouse, and fertilized greenhouse. Soil temperatures were measured with thermocouples placed in control, fertilized, greenhouse, and fertilized-greenhouse plots.

openCC (other)Dec 2025View details →

Minneapolis-St. Paul Air Quality Sensor output 2024-2025

In the summers since field research at the MSP LTER began, wildfire smoke has led to record highs in Minnesota's Air Quality Index and there has been increasing public concern over the acute and long-term impacts of exposure to toxins in the air. In 2024, the MSP LTER acquired a small network of PurpleAir sensors to place in key areas alongside transplanted lichens that are also used in air quality research. In addition to Particulate Matter, the sensor models used here also are equipped with experimental VOC detection (Volatile Organic Compounds). PurpleAir sensors were collocated with lichens at two locations in St. Paul MN, one in Roseville MN, and one at Cedar Creek Ecosystem Reserve in Northern Anoka County, MN. After lichen material was collected, the sensors remained at the site to keep reporting to community air quality tracking maps. Data from the sensors is being regularly appended to a file on the MSP LTER GitHub page and is planned to be archived long-term.

openCC (other)May 2025View details →

Homogenized, gap-filled, daily air temperature data for Saddle, 1986 - ongoing.

As part of its long-term climate data core collection, the Niwot Ridge LTER has collected daily air temperature at the Saddle site since 1981. The Saddle station is located at 3525 m.a.s.l. and is an important point location to capture local, ambient meteorological conditions for many biological and environmental datasets collected nearby. The location of the Saddle station has also presented challenges to its operation. Freezing temperatures, snow deposition from strong winds following storms, and exposure to lightning are some elements that have disrupted instrument functionality, affected data quality, and made access for research staff difficult over time, especially in winter months. These interruptions have led to missing or faulty data at times and inconsistent data gap-filling. Additionally, a mixture of mechanical hygrothermograph chart and temperature sensors with electronic data loggers have been used since the inception of the Saddle station to measure and record air temperature. Thus, a close inspection of potential influence from instrument turnover and relevant notes from research staff is required for a quality, daily air temperature time series for Saddle. Here we present a quality-controlled, gap-filled, daily time series of maximum, average, minimum, and diurnal air temperatures that accounts for instrument turnover at the Saddle. Methods follow those used to gap-fill long-term daily air temperature at the Niwot Ridge LTER D1 and C1 stations so there is consistency among core collection daily air temperature datasets. Metadata for this data package centralizes the most complete station history for Saddle air temperature and includes notes to data users on aspects and limitations of the dataset to consider when using these data in scientific analyses.

opencc-by-4.0Apr 2024View details →

CoMobility project data: Warsaw road traffic, road traffic emissions, and air concentrations for greater Warsaw area

Introduction Data here are for the Greater Warsaw area, Poland originating in the CoMobility project. It contains data relevant to traffic activity, emissions, air quality and related health studies in the area. Files contain road properties along with traffic volume and rushhour delays as well as emissions of NOx, NO2 and PM from road traffic on individual road segment level. Also 500m gridded surface air concentrations are included for PM2.5 and PM10, and for NOx, NO2. Data production Roads are from the macroscopic traffic model MTAW (Warsaw Municipality, 2016) (Model Transportowy Aglomeracji Warszawskiej in Polish). It was developed based on the 2015 comprehensive travel survey in Warsaw and it is the main strategic transport model for the Greater Warsaw area, revised most recently in 2019.  The NERVE model (Grythe et al, 2022), developed by NILU, provides detailed estimates of greenhouse gas and air pollutant emissions specifically from road traffic. Using a bottom-up approach, it combines data from regional traffic model (RTM), vehicle fleet composition, and emission factors from the Handbook Emission Factors for Road Transport (HBEFA). NERVE can be set up to calculate emissions at various levels, including road link, municipality, or national levels. It is a tool researchers and policymakers use this model for environmental assessments, policy decisions, and constructing different emission scenarios. Its high level of detail makes it valuable not only for practical emissions estimation but also as a research tool. Emissions for other sources came from the Central Emission Database by the Environmental Protection - National Research Institute (IEP-NRI) in Poland (Gawuc et al., 2021). The background concentrations were taken from the Copernicus Atmospheric Monitoring Services (CAMS) ensemble forecast for 2019 (Marécal et al., 2015) The EPISODE model (Hamer et al. 2020), developed by NILU, is an Eulerian urban dispersion model designed to address the need for an accurate urban air quality model in support of policy, planning, and air quality management. EPISODE operates as a 3D grid model coupled with numerical weather prediction (NWP) data. It simulates dispersion from point and line sources to receptor points, with a focus on the photochemical production of ozone in urban areas. The model’s CityChem extension enhances its capabilities for complex pollution sources, incorporating numerical chemistry solvers, sub-grid photochemistry, and a simplified street canyon model. EPISODE serves as a valuable tool for understanding and managing air quality in urban environments. Data files The data on road traffic contains 60 084 road links that cover the Greater Warsaw area. The file input is a traffic file from the MTAW model and is processed and formatted with NREVE. The format is an ESRI shapefile with the following road parameters: “DISTANCE” -length of road segment in kilometers. “CAPACITY” -Hourly capacity of the road. “SLOPE” -Vertical gradientor slope of the road (in %) “SPEEDLIM” -Signed speed on the road (kilometers per hour) In addition there are traffic volume parameters; “ADT_LIGHT” – Annual Daily Traffic, light vehicles (personal cars + light duty vans) average derived from morning and evening peak hours 2019. “ADT_HEAVY” – Annual Daily Traffic, heavy duty vehicles average derived from morning and evening peak hours 2019. “ADT_BUSES” – Annual Daily Traffic, public transport buses average 2019. “MRN_delay” – delay during morning rush hour peak (%) “EVE_delay” – delay during evening rush hour peak (%) The files also contain the annual emissions: “EM_NOx” – 2019 annual emissions of NOx (gram). “EM_ NO2” – 2019 annual emissions of NOx (gram). “EM_PM” – 2019 annual emissions of NOx (gram). EPISODE output files for atmospheric concentration files are given on NetCDF file format.  Concentrations are given as annual average grid concentration for each of the components. In addition, 42 000  spatially spread out receptor points gives the 2 meter concentrations to allow for surface air concentration levels at individual point locations. Furthermore, these allows for downgridding concentrations to higher resolution. The source contribution files are from EPISODE and gives atmospheric concentration fields for NOx, PM10 and PM2.5 from individual sources. The individual sources are “RDU” -Road dust (PM only) “EXT” – Exhaust  (PM only) “TRA” - Exhaust  (NOx only) “IND” – Industry “RES” – Residential “OTH” – Other (all other sources within the domain combined ) “BGC” – Background (all sources outside the domain combined )

opencc-by-4.0Sep 2024View details →

Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output without snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC

The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 without snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured an ideal cold-air pool formation followed by a interuption by a wind disturbance around midnight. Simulation output for the same night, but with snow cover is also available (Rauchöcker et al., 2024a). The temperature evolution of the measurements agreed much better with the simulation with snow cover and otherwise the same model setting compared to the simulation without snow cover (Rauchöcker et al. 2024d). Also available in a different dataset is output from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period. <h3>WRF Simulation Output</h3> This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al.,  2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021).  WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020. Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the simulation without snow cover. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the file namelist.input that was used to generate the simulation results. Standard WRF output can be found in wrfout_40m_jan16_nosnow. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in windout_40m_jan16_nosnow. These variables were contained in the  unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in tend_40m_jan16_nosnow.nc.

opencc-by-4.0Oct 2024View details →

Dataset of "Nickel-cobalt spinel-based oxygen evolution electrode for zinc-air flow battery"

Following dataset provides all measured data that were collected on nickel (Ni) based electrodes for the oxygen evolution reaction. The electrodes were following: nickel (Ni) pristine mesh (PM), catalysed mesh (CM), nickel pristine foam (PF), catalysed foam (CF). Catalyst was NiCo2O4. Firstly, the catalysed electrodes were prepared and characterized by SEM, EDS and XRD. The electrodes were characterized in three different arrangements: in electrolysis non-flow arrangement, in a flow electrolysis cell and in ZAFB according to the manuscript.

Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2022-01-01 to 2022-12-31 [L2]

Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level. <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2022.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> For more details read `FRCHEM_2022_AirTemperature_MetaData.txt`. Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.

Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2023-01-01 to 2023-12-31 [L2]

Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level. <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2023.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> For more details read `FRCHEM_2023_AirTemperature_MetaData.txt`. Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.

Air temperature measurements from Automatic Weather Station (AWS) at Freiburg – Chemiehochhaus (FRCHEM) from 2020-01-01 to 2020-12-31 [L2]

Quality controlled and gap-filled continuous air temperature data from the urban rooftop weather station at Freiburg-Chemiehochhaus (FRCHEM, 7.8486ºE, 48.0011ºN, 323.5 m) using an actively ventillated and shielded psychrometer operated 2m above roof level. <ul> <li>Quality controlled air temperature data are available and aggregated at 10min, 30min, hourly, daily, monthly and yearly resolution for the year 2020.</li> <li>Average, minimum and maximum air temperatures are provided on hourly, daily, monthly and annual scales.</li> <li>Characteristic hours and days are reported on daily, monthly and annual scales (e.g. summer days with T_max > 25ºC, hot days with T_max > 30º, desert days with T_max > 35ºC, tropical nights with T_min > 20°, frost days with T_min < 0ºC and ice days with T_max < 0ºC, all based on 00:00 - 24:00 UTC).</li> <li>Detailed information on gap-filled data is provided.</li> <li>Note: All times are provided in UTC, not local time.</li> </ul> For more details read `FRCHEM_2020_AirTemperature_MetaData.txt`. Version 1.1.0 contains additionally air temperature data aggregated at 10min and 30min.