Find research datasets worth reusing

Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the South China Sea (2003-2019)

The South China Sea (SCS) is one of the largest marginal seas worldwide. It includes a river-dominated, highly productive marginal sea on the north shelf and a wide, oligotrophic ocean-dominated basin with various dynamic sub-regions. Based on an in situ seawater partial pressure of CO2 (pCO2) datasets of 44 cruises/legs collected for the last two decades in the SCS, we proposed a seawater pCO2 retrieval algorithm by combining the semi-mechanistic and machine learning (ML) methods (MeSAA-ML). The parameter selection strategy was based on the mechanistic analysis of pCO2 variation, separating impacts of thermodynamics, biological activities, water mixing, and the atmospheric CO2 forcing. We set a few semi-analytical parameters: pCO2_therm, which was a proxy for the combined effect of thermodynamics and the atmospheric CO2 forcing on seawater pCO2; an upwelling index (UISST) and mixing layer depth (MLD) to characterize the multiple mixing processes; chlorophyll-a concentration (Chl-a) with remote sensing reflectance at 443 and 555 nm (Rrs(443) and Rrs(555)), which were the inputs to proxy the biological effect and other characteristics for distinguishing shelf, basin, and sub-regions. As the seawater pCO2 and atmospheric pCO2 ( pCO2air) have similar data values and characteristics in the vast SCS oligotrophic basin, it will cause instability of the model if one is input and the other is output; thus the difference between them (ΔpCO2sea-air) was set as the output, and the seawater pCO2 was obtained finally by summing pCO2air and ΔpCO2sea-air. We compared several ML models, and the XGBoost model was confirmed as the best model. Completely independent cruise-based and observed datasets from Southeastern Asia Time-series Study (SEATS) were used to validate the satellite products, with low root mean square error (RMSE = 11.69 μatm) and mean absolute percentage deviation (APD = 1.59%). The increasing trend of satellite-derived pCO2 (2.44 ± 0.24 μatm/yr) at the location of SEATS was found to be consistent with observed data. We presented that the SCS as a whole is a source of atmospheric CO2, releasing an average of 11.00 ± 2.45 Tg C/yr from a total area of 3.32 × 106 km2, and the northern shelf is a sink (1.69 ± 0.53 Tg C/yr). The area-integrated CO2 efflux over the entire SCS may decrease with a rate of 0.34 Tg C/yr during 2003–2019. This high-accuracy dataset with 1 km resolution provides a refined understanding of the air-sea CO2 exchange dynamics in the SCS during 2003–2019.

Air pollution datasets

Processed air pollution datasets originally obtained from the <a href="https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=f3c0f7d512273410VgnVCM2000000c205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default">open data portal of the Madrid City Hall</a>. The pollutants include: <ul> <li>Fine particulate matter: PM2.5</li> <li>Coarse particulate matter: PM10</li> <li>Ozone: O3</li> <li>Nitrogen monoxide: NO</li> <li>Nitrogen dioxide: NO2</li> <li>Nitrogen oxides: NOx</li> <li>Sulfur dioxide: SO2</li> <li>Carbon monoxide: CO</li> <li>Toluene: TOL</li> <li>Benzene: BEN</li> <li>Ethylbenzene: EBE</li> </ul> The period covered goes from the 1st of January 2010 to the 30th of April 2022. Each pollutant is recorded by a variable number of sensors, between 6 and 24 of them (additional information <a href="https://datos.madrid.es/portal/site/egob/menuitem.c05c1f754a33a9fbe4b2e4b284f1a5a0/?vgnextoid=9e42c176313eb410VgnVCM1000000b205a0aRCRD&vgnextchannel=374512b9ace9f310VgnVCM100000171f5a0aRCRD&vgnextfmt=default">here</a>). They cover Madrid city and surroundings (see <a href="https://datos.madrid.es/egob/new/detalle/auxiliar/mapa.jsp?geoUrl=/egob/catalogo/212629-2-estaciones-control-aire.geo">this map</a>). Additional information about the pollutants can be found <a href="https://datos.madrid.es/FWProjects/egob/Catalogo/MedioAmbiente/Aire/Ficheros/Interprete_ficheros_%20calidad_%20del_%20aire_global.pdf">here</a>. The specific datasets in HDF5 format are: <ol> <li>01h_flat_raw.h5: Hourly raw data, one table per pollutant. The first column is the timestamp, which are in <a href="https://en.wikipedia.org/wiki/Unix_time">time-since-epoch</a>. 108049 rows and between 7 and 25 columns.</li> <li>01h_35x30_norm_linear_J0.0.h5: Hourly mesh-grid data, normalized and linearly interpolated. Single table of shape: (108049, 35, 30, 11).</li> <li>01h_35x30_norm_nearest_J0.0.h5: Hourly mesh-grid data, normalized and nearest-neighbors interpolated. Single table of shape: (108049, 35, 30, 11).</li> <li>01h_35x30_raw_linear_J0.0.h5: Hourly mesh-grid data, linearly interpolated. Single table of shape: (108049, 35, 30, 11).</li> <li>01h_35x30_raw_nearest_J0.0.h5: Hourly mesh-grid data, nearest-neighbors interpolated. Single table of shape: (108049, 35, 30, 11).</li> <li>01h_35x30_stand_linear_J0.0.h5: Hourly mesh-grid data, standardized and linearly interpolated. Single table of shape: (108049, 35, 30, 11).</li> <li>01h_35x30_stand_nearest_J0.0.h5: Hourly mesh-grid data, standardized and nearest-neighbors interpolated. Single table of shape: (108049, 35, 30, 11).</li> </ol> This datasets are prepared to work with the framework published at <a href="https://github.com/iipr/air-quality">https://github.com/iipr/air-quality</a>

Data and code: Disentangling the impact of Atlantic Niño on sea-air CO2 flux

This repository share (1) post-processed data of Norwegian Earth system model version 2 (LM) and (2)  codes for calculating carbon system from NorESM variables (MATLAB format).

opencc-by-4.0Apr 2023View details →

Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning

The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the ever-expanding and nearly infinite chemical space of MOFs makes it extremely challenging to identify the most optimal materials for a given application. In this work, we present a novel approach using deep reinforcement learning for the inverse design of MOFs, our motivation being designing promising materials for the important environmental application of direct air capture of CO2 (DAC). We demonstrate that the reinforcement learning framework can successfully design MOFs with critical characteristics important for DAC. Our top-performing structures populate two separate subspaces of the MOF chemical space: the subspace with high CO2 heat of adsorption and the subspace with preferential adsorption of CO2 from humid air, with few structures having both characteristics. Our model can thus serve as an essential tool for the rational design and discovery of materials for different target properties and applications.

opencc-by-4.0May 2023View details →

FLEXPART 10.4 output for "Occurrence and backtracking of microplastic mass loads including tire wear particles in Northern Atlantic air"

The dataset consists of three (3) files: -- track3h.txt shows the position of the research vessel in each of the seven (7) ship tracks/campaigns in 3-hour resolution in ascii format structured in columns as follows: YEAR, MONTH, DAY, HOUR, MINUTE, SECOND, LONGITUDE, LATITUDE, SHIP TRACK NUMBER -- FLEXPART_720x360_fine.tar.gz shows the footprint emission sensitivities for fine particles (as described in the paper) in a gridded netCDF format of 0.5 degrees resolution for 80 release points matching the coordinates and times in the track3h.txt file. -- FLEXPART_720x360_coarse.tar.gz shows the footprint emission sensitivities for coarse particles (as described in the paper) in a gridded netCDF format of 0.5 degrees resolution for 80 release points matching the coordinates and times in the track3h.txt file.

opencc-by-4.0May 2023View details →

Air mass trajectory and connectivity data generated with tropolink (Richard et al., 2023)

Archive containing trajectory and connectivity data generated with tropolink for the preparation of the manuscript Richard et al. (2023, <a href="https://doi.org/10.1029/2023GH000885">https://doi.org/10.1029/2023GH000885</a>), as well as the corresponding specifications (node coordinates, dates and other tropolink options). The archive contains specifications, trajectories and connectivities for the three applications presented in the manuscript:- the study of airborne connectivity between areas of production of sugar beet, with starting altitude equal to 250m, 500m and 750m above ground level;- the study of airborne connectivity between potyvirus populations;- the study of invasion risk of Spodoptera frugiperda in Europe, North Africa and western Asia; Web application tropolink: https://tropolink.fr/Associated gitlab: https://forgemia.inra.fr/tropo-groupAccompanying wiki: https://forgemia.inra.fr/tropo-group/tropolink/-/wikisR code for analyzing tropolink output: https://forgemia.inra.fr/tropo-group/tropolink/-/wikis/ExamplesRichard H., Martinetti D., Lercier D., Fouillat Y., Hadi B., Elkahky M., Ding J., Michel L., Morris C.E., Berthier K., Maupas F.,  Soubeyrand S. (2023). Computing geographical networks generated by air-mass movement. GeoHealth 7:e2023GH000885. <a href="https://doi.org/10.1029/2023GH000885">https://doi.org/10.1029/2023GH000885</a>.

opencc-by-4.0Sep 2022View details →

AgrImOnIA: Open Access dataset correlating livestock and air quality in the Lombardy region, Italy

The AgrImOnIA dataset is a comprehensive dataset relating air quality and livestock (expressed as the density of bovines and swine bred) along with weather and other variables. The AgrImOnIA Dataset represents the first step of the <a href="http://www.agrimonia.net">AgrImOnIA project</a>. The purpose of this dataset is to give the opportunity to assess the impact of agriculture on air quality in Lombardy through statistical techniques capable of highlighting the relationship between the livestock sector and air pollutants concentrations. The building process of the dataset is detailed in the companion paper: A. Fassò, J. Rodeschini, A. Fusta Moro, Q. Shaboviq, P. Maranzano, M. Cameletti, F. Finazzi, N. Golini, R. Ignaccolo, and P. Otto (2023). Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. SCIENTIFIC DATA, 1-19. available <a href="https://rdcu.be/c7T9H">here</a>. This dataset is a collection of estimated daily values for a range of measurements of different dimensions as: air quality, meteorology, emissions, livestock animals and land use. Data are related to Lombardy and the surrounding area for 2016-2021, inclusive. The surrounding area is obtained by applying a 0.3° buffer on Lombardy borders. The data uses several aggregation and interpolation methods to estimate the measurement for all days. The files in the record, renamed according to their version (es. .._v_3_0_0), are: <ul> <li> Agrimonia_Dataset.csv(.mat and .Rdata) which is built by joining the daily time series related to the AQ, WE, EM, LI and LA variables. In order to simplify access to variables in the Agrimonia dataset, the variable name starts with the dimension of the variable, i.e., the name of the variables related to the AQ dimension start with 'AQ_'. This file is archived also in the format for MATLAB and R software.  </li> <li> Metadata_Agrimonia.csv which provides further information about the Agrimonia variables: e.g. sources used, original names of the variables imported, transformations applied. </li> <li> Metadata_AQ_imputation_uncertainty.csv which contains the daily uncertainty estimate of the imputed observation for the AQ to mitigate missing data in the hourly time series.   </li> <li> Metadata_LA_CORINE_labels.csv which contains the label and the description associated with the CLC class.   </li> <li> Metadata_monitoring_network_registry.csv which contains all details about the AQ monitoring station used to build the dataset. Information about air quality monitoring stations include: station type, municipality code, environment type, altitude, pollutants sampled and other. Each row represents a single sensor. </li> <li> Metadata_LA_SIARL_labels.csv which contains the label and the description associated with the SIARL class. </li> <li> AGC_Dataset.csv(.mat and .Rdata) that includes daily data of almost all variables available in the Agrimonia Dataset (excluding AQ variables) on an equidistant grid covering the Lombardy region and its surrounding area.  </li> </ul> The Agrimonia dataset can be reproduced using the code available at the GitHub page: <a href="https://github.com/AgrImOnIA-project/AgrImOnIA_Data">https://github.com/AgrImOnIA-project/AgrImOnIA_Data</a> UPDATE 31/05/2023 - NEW RELEASE - V 3.0.0 A new version of the dataset is released: Agrimonia_Dataset_v_3_0_0.csv (.Rdata and .mat), where variable WE_rh_min, WE_rh_mean and WE_rh_max have been recomputed due to some bugs. In addition, two new columns are added, they are LI_pigs_v2 and LI_bovine_v2 and represents the density of the pigs and bovine (expressed as animals per kilometer squared) of a square of size ~ 10 x 10 km centered at the station localisation. A new dataset is released: the Agrimonia Grid Covariates (AGC) that includes daily information for the period from 2016 to 2020 of almost all variables within the Agrimonia Dataset on a equidistant grid containing the Lombardy region and its surrounding area. The AGC does not include AQ variables as they come from the monitoring stations that are irregularly spread over the area considered. UPDATE 11/03/2023 - NEW RELEASE - V 2.0.2 A new version of the dataset is released: Agrimonia_Dataset_v_2_0_2.csv (.Rdata), where variable WE_tot_precipitation have been recomputed due to some bugs. A new version of the metadata is available: Metadata_Agrimonia_v_2_0_2.csv where the spatial resolution of the variable WE_precipitation_t is corrected. <ul> </ul> UPDATE 24/01/2023 - NEW RELEASE - V 2.0.1 minor bug fixed UPDATE 16/01/2023 - NEW RELEASE - V 2.0.0 A new version of the dataset is released, Agrimonia_Dataset_v_2_0_0.csv (.Rdata) and Metadata_monitoring_network_registry_v_2_0_0.csv. Some minor points have been addressed: <ul> <li>Added values for LA_land_use variable for Switzerland stations (in Agrimonia Dataset_v_2_0_0.csv)</li> <li>Deleted incorrect values for LA_soil_use variable for stations outside Lombardy region during 2018 (in Agrimonia Dataset_v_2_0_0.csv)</li> <li>Fixed duplicate sensors corresponding to the same pollutant within the same station (in Metadata_monitoring_network_registry_v_2_0_0.csv)</li> </ul>

Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)

Based on in situ seawater pCO2 data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater pCO2 was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater pCO2 (pCO2,therm ) and upwelling index (UISST), to characterise the combined effect of atmospheric CO2 forcing, thermodynamic effects, and multiple mixing processes on seawater pCO2. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived pCO2, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater pCO2.

opencc-by-4.0Jul 2023View details →

Wintertime Arctic warm air intrusion detection algorithm for satellite sea ice concentration analysis

This Dataset is related to the Article Relevance of warm air intrusions for Arctic satellite sea ice concentration time series in The Cryosphere (https://doi.org/10.5194/tc-2023-69). Provided are the core detection algorithm and a minimal working example as well as a list of all detected warm air intrusions between November 1979 and April 2020 (monthly data).

opencc-by-4.0Jul 2023View details →

Indoor/outdoor air temperature dataset - IN-HALE Project

IN-HALE project aims to identify the thermal summer conditions inside the residences of residents over 65 years of age and to identify the individual determinants of exposure to heat, as well as the barriers (material and/or immaterial) for thermal adaptation. To this end, we will monitor the indoor thermal environment of 20 dwellings, through visits of objective assessment of the thermal behavior of the dwellings and using the installation of thermal data loggers during the summer period. Further info: <a href="http://www.in-hale.org">www.in-hale.org</a>

opencc-by-4.0Sep 2023View details →

NO2, O3, PM10 and PM2.5 concentrations - Daily geographical aggregates at ZIP-code level from CAMS European Air Quality Re-analyses.

This dataset offers daily aggregated measurements of air pollutants – NO2, O3, PM10, and PM2.5 – across distinct ZIP-code areas in Germany. The temporal coverage spans from January 1, 2013, to December 31, 2022, providing a comprehensive temporal context for analyzing long-term air quality dynamics. Each daily entry comprises key statistical descriptors, encompassing mean, maximum, minimum, and standard deviation values of pollutant concentrations specific to each ZIP-code area. Additionally, for O3, the dataset includes an eight-hour rolling mean daily maximum. Spatial reference is established via shapefiles provided by ESRI Deutschland (<a href="https://opendata-esri-de.opendata.arcgis.com/datasets/5b203df4357844c8a6715d7d411a8341_0">https://opendata-esri-de.opendata.arcgis.com/datasets/5b203df4357844c8a6715d7d411a8341_0</a>). These shapefiles link the air quality data to precise ZIP-code areas . The concentration data spanning from 2018 to 2022 originate from the European Air Quality Reanalyses dataset of the Atmosphere Data Store (ADS), an initiative by the Copernicus Atmosphere Monitoring Service (CAMS). Accessible via <a href="https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc">https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-europe-air-quality-reanalyses?tab=doc</a>, this dataset offers a robust foundation for assessing air quality. For the years 2013 to 2017, data were previously obtained from a former download platform for the same dataset. Important: in future all data will be migrated to the Atmosphere Data Store (ADS) platform. The native resolution of the CAMS data is 0.1° x 0.1° spatially and hourly temporally. To enhance spatial accuracy, the spatial resolution was virtually increased by a factor of 5 using bilinear interpolation, resulting in a refined grid. The daily mean concentrations were subsequently computed for this augmented grid. Aggregated statistics were derived for each ZIP-code polygon, employing all grid cells intersecting with the polygons. The computation was based on the proportion of cell area included within the respective polygons. This dataset constitutes a valuable resource for conducting ecologically designed epidemiological studies, as it facilitates the exploration of potential associations between air quality and health trends across broad geographical areas. Generated using Copernicus Atmosphere Monitoring Service Information 2013-2022

opencc-by-4.0Dec 2022View details →

Data from air, englacial and permafrost temperature measurements on Mt. Ortles (Eastern European Alps)

The *.csv files report the temperature data recorded between 2010 and 2016 and presented in the paper “Modern air, englacial and permafrost temperatures at high altitude on Mt. Ortles, (3905 m a.s.l.) in the Eastern European Alps” (Carturan et al., 2023, submitted). The data were used to display the time series reported in the paper, which details variable names, data quality flags, maintenance logs of field operations, and characteristics of measurement sites. The data files contain measurements of air temperature, englacial temperature, soil surface temperature and rockwall temperature. The file named ‘Ortles_Temperature_Metadata.pdf’ contains information regarding variable names, structure of data files, quality codes, geolocation, and topographic and geomorphological characteristics of sites instrumented for temperature measurements. Reference: Carturan, L., De Blasi, F., Dinale, R., Dragà, G., Gabrielli, P., Mair, V., Seppi, R., Tonidandel, D., Zanoner, T., Zendrini, T. L., and Dalla Fontana, G.: Modern air, englacial and permafrost temperatures at high altitude on Mt. Ortles, (3905 m a.s.l.) in the Eastern European Alps, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-164, in review, 2023.

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 (other)Apr 2022View details →

High-frequency water quality and air parameters from large lake Võrtsjärv, Estonia: 2010-2019

This high frequency water-air dataset was collected from large shallow Lake Võrtsjärv (Estonia) with an automated lake monitoring buoy system and was used in the analyses described in the manuscript by Thayne, M.W., B. Kraemer, J.P. Mesman, A. Laas, E. de Eyto, B. W. Ibelings, R. Adrian. “Trophic state effects on antecedent lake characteristics shape the resistance and resilience of lakes following extreme storms”. Dataset contains every 10-minute measurements from the open water periods in years 2010-2019. Underwater measurements were conducted with Yellow Springs Instrument multiparameter sonde model YSI 6600 V2-4. Underwater measurements contain the collected values of pH, dissolved oxygen, dissolved oxygen saturation, water temperature, specific conductance, water turbidity, chlorophyll concentration (calculated by the sensor from fluorescence values), and the abundance of the bluegreen algae (calculated by the sensor from phycocyanin fluorescence values). Air parameters were collected with the Vaisala multiparameter weather station model WXT520 and contain measurements of wind direction, wind speed, air temperature, atmospheric pressure, and cumulative amount of rain between measurement periods. All air measurements were collected 2 m above from the water surface. Additionally, solar irradiance measurements were collected from the automated buoy system with the Licor pyranometer model LI-200, those measurements contain values in two different columns: average solar irradiance in W/m2 and average photon flux density values in micromoles per square meter per second.

openCC (other)Apr 2022View details →

NEON Provisional aquatic biology and single aspirated air temperature data 2020-2021

NEON field and taxonomic and abundance data for the periphyton (DP1.20166.001), macroinvertebrate (DP1.20120.001), and zooplankton (DP1.20219.001) collection for all aqatic sites, as well as single aspirated air temperature for 15 sites, were downloaded from the NEON data portal. Provisional data, not included in the 2022 data release, are included in this upload. Data are from all 34 aquatic sites across the network. Data are from NEON sources only. NEON is sponsored by the National Science Foundation (NSF) and operated under a cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program. NEON (National Ecological Observatory Network). Periphyton, seston, and phytoplankton collection (DP1.20166.001). https://data.neonscience.org (accessed March 12, 2022) NEON (National Ecological Observatory Network). Macroinvertebrate collection (DP1.20120.001). https://data.neonscience.org (accessed March 12, 2022) NEON (National Ecological Observatory Network). Zooplankton collection (DP1.20219.001). https://data.neonscience.org (accessed March 12, 2022) NEON (National Ecological Observatory Network). (2022e). Single aspirated air temperature (DP1.00002.001). https://data.neonscience.org (accessed March 12, 2022) NEON field and taxonomic and abundance data for the periphyton (DP1.20166.001), macroinvertebrate (DP1.20120.001), and zooplankton (DP1.20219.001) collection were downloaded from the NEON data portal. Provisional data, not included in the 2021 data release, are included in this upload. Data are from all 34 aquatic sites across the network. Data are from NEON sources only. NEON is sponsored by the National Science Foundation (NSF) and operated under cooperative agreement by Battelle. This material is based in part upon work supported by NSF through the NEON Program. NEON (National Ecological Observatory Network). Periphyton, seston, and phytoplankton collection (DP1.20166.001). https://data.neonscience.org (accesse

openCC (other)Aug 2024View details →

New Hampshire Soil Sensor Network: Air Temperature, Soil Temperature, Soil Water Content, and Soil Electrical Conductivity, 2012 - ongoing

The goal of the New Hampshire Soil Sensor Network is to examine spatial and temporal changes in soil properties and processes as the climate changes. Data collected can also calibrate and validate models that examine how ecosystems may respond to changing climate and land use. To determine how soil processes are affected by climate change and land management, this soil sensor network measures snow depth, air temperature, soil temperature, soil volumetric water content, and soil electrical conductivity, as well as soil CO2 fluxes. This data package includes data from the air temperature, soil temperature, soil volumetric water content, and electrical conductivity sensors. Data were collected at the following sites: BRT = Bartlett Experimental Forest, Bartlett, NH; BDF = Burley-Demmerit Farm, Lee, NH; DCF = Dowst Cate Forest, Deerfield, NH; HUB = Hubbard Brook Experimental Forest, Woodstock, NH; SBM = Saddleback Mountain, Deerfield, NH; THF = Thompson Farm, Durham, NH; and Trout Pond Brook, Strafford, NH.

openCC (other)Mar 2024View details →

Cold-air pooling characterization and forest composition, New England, USA

This dataset corresponds to a project investigating whether cold-air pooling influences forest composition and function. The data include hourly sub-canopy air temperatures (measured continuously via ibuttons) and forest forest composition data for 48 plots along 9 transects in 3 sites across New England, USA. The temperature data also include surface lapse rates and temperature gradients across transects, as well as a designation indicating the presence or absence of a temperature inversion. We found that sites with the most frequent temperature inversions also displayed vegetation inversions across slopes, with more cold-adapted species at low instead of high elevations.

openCC (other)Jan 2026View details →

Seasonality of in-lake and meteorological data from seven lakes, including daily measurements of water temperature, chlorophyll-a, dissolved oxygen, ice cover, air temperature, and solar radiation

This data product supports the manuscript "Seasons and seasonality in lakes: a synthesis amid global change" (Lewis et al. 2026; in review). Data were analyzed to understand how seasonality varies among diverse lakes and variables. Specifically, this data publication includes daily mean water temperature, chlorophyll-a, and dissolved oxygen at multiple depths, ice cover (binary), air temperature and solar radiation. Data availability and collection methods differ among lakes, as described in the Methods.

openCustomDec 2015View details →

Mean monthly maximum and minimum air temperature spatial grids (1971-2000), Andrews Experimental Forest

Mean monthly maximum and minimum air temperature spatial grids (1971-2000), adjusted for the effects of solar radiation and sky view factors, Andrews Experimental Forest. Maps were created using PRISM (Parameter-elevation Regressions on Independent Slopes Model), developed by Dr. Christopher Daly at Oregon State University’s PRISM Climate Group in 2010 (prism.oregonstate.edu). Grids were exported into ASCII format from GRASS GIS software; values are in degrees C x 100. Spatial resolution is 50 meters. Two sets of temperature values are available: (1) values derived from an interpolation of point station temperature values accounting for elevation; and (2) values from (1), adjusted for effects of solar radiation exposure and sky view factors. Radiation exposure and sky view factors were calculated from a two-stream solar radiation model that accounts for elevation, slope, aspect, and shading from adjacent pixels on a 50-m digital elevation model. Temperature data were obtained from selected benchmark and reference stand climate stations within the HJ Andrews, as well as National Weather Service Cooperative (COOP) and USDA NRCS Snow Telemetry (SNOTEL) stations in the vicinity. Due to the sparseness of the station data outside the Andrews, values outside the Andrews are considered to have high uncertainty. Temperature values assume an open site with no canopy cover, so are not appropriate for describing temperatures within the forest canopy. See MS033 for radiation grids used to make the radiation adjustments.