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openCC (other)Nov 2025View details →

Soil and litter microclimate data from NEON and LTER Sites Across Eight U.S. Ecoregions (CliMush Project), 2022–2023

Data include soil and litter measurements for moisture, pH, and carbon-to-nitrogen ratio. Samples were collected from 8 different ecoregions, as determined by NEON, at various NEON/LTER and/or other experimental sites. Soil cores and litter samples were taken in the spring and fall of 2022.

Cascade Project at North Temperate Lakes LTER cross-lakes comparison carbon Data 1988 - 2007

Data on dissolved organic and inorganic carbon as well as particulate organic matter and the partial pressure of CO2. Samples were collected with a Van Dorn bottle. Organic samples were collected from the epilimnion, metalimnion, and hypolimnion. Inorganic samples were collected at depths corresponding to 100%, 50%, 25%, 10%, 5%, and 1% of surface irradiance, as well as one sample from the hypolimnion. Samples for the partial pressure of CO2 were collected from two meters above the lake surface (air) and just below the lake surface (water).

Cascade Project at North Temperate Lakes LTER High Frequency Sonde Data from Food Web Resilience Experiment 2008 - 2011

High-frequency sonde data collected from the surface waters of two lakes in Upper Peninsula of Michigan during the summers of 2008-2011. The food web of Peter Lake was slowly transformed by gradual additions of Largemouth bass (Micropterus salmoides) while Paul Lake was an unmanipulated reference. Sonde data were used to calculate resilience indicators to evaluate the stability of the food web and to calculate ecosystem metabolism.

Cascade project at North Temperate Lakes LTER - High Frequency Data for Whole Lake Nutrient Additions 2013-2015

High frequency continuous data for temperature, dissolved oxygen, pH, chlorophyll a, and phycocyanin in Paul, Peter, and Tuesday lakes from mid-May to early September for the years 2013, 2014 and 2015. Inorganic nitrogen and phosphorus were added to Peter and Tuesday lakes each year while Paul Lake was an unfertilized reference.

Cascade project at North Temperate Lakes LTER - Daily Chlorophyll Data for Whole Lake Nutrient Additions 2013-2015

Daily chlorophyll for surface water samples in Paul, Peter, and Tuesday lakes from mid-May to early September for the years 2013, 2014 and 2015. Inorganic nitrogen and phosphorus were added to Peter and Tuesday lakes each year while Paul Lake was an unfertilized reference.

Cascade Project at North Temperate Lakes LTER – High-resolution Spatial Data for Whole Lake Experiments 2018 - 2019

Spatial measurements of water quality from Peter and Paul lakes in 2018 and 2019. In 2019, inorganic nitrogen and phosphorus were added to Peter Lake daily to cause an algal bloom while Paul Lake was an unmanipulated reference lake. In 2018, both lakes were sampled 1 time per week, while in 2019 lakes were sampled three times per week. Measurements were taken using the FLAMe sampling platform (Crawford et al. 2015, Environmental Science and Technology 49:442-450), which was driven in a grid pattern and recorded GPS coordinates and water measurements at 1Hz to create high resolution spatial maps.

openCC (other)Nov 2022View details →

Cascade Project at North Temperate Lakes LTER: Process Data 1984 - 2007

Data on chlorophyll, primary productivity, and alkaline phosphatase activity from 1984-95. Samples were collected with a Van Dorn bottle at 6 depths determined from the percent of surface irradiance (100%, 50%, 25%, 10%, 5% and 1%) and in the hypolimnion (12 m in Peter, East Long, West Long, and Tuesday lakes; 9 m in Paul Lake; and 4.5 m in Central Long Lake). Sampling Frequency: varies Number of sites: 8

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-4.0Jul 2024View details →

Survey data on climate policy in three countries (Peru, Ghana, Philippines) within the project "Sustainable Middle Classes in Middle Income Countries: Transforming Carbon Consumption Patterns (SMMICC)"

The unprecedented growth of the new middle classes in middle income developing countries implies a strong growth in both consumption and carbon emissions. The research project Sustainable Middle Classes in Middle Income Countries (SMMICC) investigates the drivers of carbon consumption choices of the new middle classes and policy options to decrease their carbon footprints, including the implementation of carbon taxes The research of the authors generated quantitative data on the acceptability of carbon taxes in three countries (Peru, Ghana, Philippines).   The data is provided in the following formats: - 2024-07-26_malerba_10.5281/zenodo.12662722_ghana.csv - 2024-07-26_malerba_10.5281/zenodo.12662722_peru.csv - 2024-07-26_malerba_10.5281/zenodo.12662722_philippines.csv - 2024-07-26_malerba_10.5281/zenodo.12662722_ghana.dta - 2024-07-26_malerba_10.5281/zenodo.12662722_peru.dta - 2024-07-26_malerba_10.5281/zenodo.12662722_philippines.dta Additionally, the codebooks on variables of questionnaire and political parties in each country are attached in a csv format.

opencc-by-4.0Nov 2022View details →

[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Raw Data

Explanation/Overview: Corresponding raw data for the analyses described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised. The raw data is in the <code>.json</code> format and can be read by most languages/tools. It is recommended to import the data into a MongoDB to work with it. Purpose: The purpose of this dataset is to provide the basis for possible further examinations, involving additional (not yet analysed) features such as the content of the comments etc. and also new ways of extracting networks. Relatedness: The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'. Content: The dataset contains three files: <ul> <li><code>Comments.json</code> <ul> <li>contains the basic data representation with multiple fields (e.g., <code>time_created</code>, <code>user_login</code>). Each data field represents a comment.</li> </ul> </li> <li><code>Discussions.json</code> <ul> <li><code></code>contains all discussions. Each data field is a discussion, with multiple fields (e.g., <code>comments_count</code>, <code>user_login</code>)</li> </ul> </li> <li><code>Projects.json</code> <ul> <li><code></code>contains all projects. Each data field is a project, with multiple fields (e.g., <code>project_id</code>, <code>description</code>)</li> </ul> </li> </ul> Grouping: The projects (and thus the corresponding discussions and comments) were collected on the basis of common forum features such as the discussion boards.

opencc-by-4.0Mar 2023View details →

Uncertainty in Migration Scenarios. QuantMig Project Deliverable D9.2 Data Description

This open data deposit contains the data and code accompanying used in the report: Barker and Bijak (2021), Uncertainty in Migration Scenarios, QuantMig Project Deliverable D9.2. The cover note should be read in conjunction with the report, available via www.quantmig.eu, and with the individual readme files in the data folders that can be found within this Zenodo repository (DOI: 10.5281/zenodo.7709443).

The Jefferson Project 2017 water quality data from two vertical profiler stations in Lake George, NY, USA.

The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake’s food web and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project deployed two vertical profiler stations on the lake, collecting data on water quality and meteorology. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2017. These vertical profiler stations are named VP_AnthonysNose and VP_TeaIsland. The water quality data are collected by EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data is transferred in near real-time to an off-site database for monitoring and review. The data provided have undergone data correction by Jefferson Project researchers.

The Jefferson Project 2017 weather data from seven surface weather stations on Lake George, NY, USA.

The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake’s food web and overall water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2017, The Jefferson Project had five weather monitoring stations around the lake collecting data on precipitation, temperature, wind, and air quality. These stations are 'WX-CedarLane', 'WX-DFWI', 'WX-GullRock', 'WX-MossyPoint' and 'WX-WhaleRock'. Weather data from two vertical profiler sites, 'VP-AnthonysNose' and 'VP-TeaIsland', are also included in this dataset. The stations have a sensor payload that include some combination of the following sensors: HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, LiCor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which has undergone data correction and down sampling to an hourly frequency.

The Jefferson Project 2018 hydrologic, water quality, and soil quality data from 11 Tributary Stations within the Lake George basin, NY, USA.

The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at <https://jeffersonproject.rpi.edu/> In 2018, The Jefferson Project had eleven tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.

The Jefferson Project 2019 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.

The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which underwent data correction and downsampling to an hourly frequency.

The Jefferson Project 2018 weather data from eight surface weather stations on Lake George, NY, USA.

The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at <https://jeffersonproject.rpi.edu/> In 2018, The Jefferson Project had six weather monitoring stations around the lake collecting data on precipitation, temperature, wind, and air quality. These stations are WX_CedarLane, WX_DFWI, WX_PilotKnob, WX_GullRock, WX_MossyPoint, and WX_WhaleRock. Weather data from two vertical profiler sites, VP_AnthonysNose and VP_TeaIsland, are also included in this dataset. The stations have a sensor payload that include some combination of the following sensors: Rotronic HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, Li-Cor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which has undergone data correction and downsampling to an hourly frequency.

The Jefferson Project 2019 weather data from ten surface weather stations on Lake George, NY, USA.

The Jefferson Project at Lake George – a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association – combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project had seven weather monitoring stations around the lake collecting data on precipitation, temperature, wind speed, wind direction, barometric pressure, and relative humidiity. These stations are WX_CedarLane, WX_DFWI, WX_GullRock, WX_MossyPoint, WX_WhaleRock, WX_PilotKnob, and WX_Glenburnie. Weather data from three vertical profiler sites (VP_AnthonysNose, VP_CalvesPen, and VP_TeaIsland) are also included in this dataset. The stations have a sensor payload that include some combination of the following sensors: Rotronic HC2-S3 sensor, Campbell Scientific CS616 soil moisture sensor, Li-Cor LI-200R pyranometers, RM Young 85006 anemometer, Vaisala Weather Transmitter WXT series (520 & 530 models), HyQuest TB3 tipping bucket rain gauge, N-Con wet deposition sampler. The sensors collect data at high-frequency (~1 sample per minute) and the data is transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. Data provided is level 4 data which has undergone data correction and downsampling to an hourly frequency.

openCC (other)Sep 2024View details →

The Jefferson Project 2018 water quality data from two vertical profiler stations in Lake George, NY, USA.

The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2018, The Jefferson Project deployed two vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2018. These vertical profiler stations are named VP_AnthonysNose and VP_TeaIsland. The water quality data are collected by YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data is transferred in near real-time to an off-site database for monitoring and review. The data provided have undergone data correction by Jefferson Project researchers.

openCC (other)Sep 2024View details →

The Jefferson Project 2019 water quality data from three vertical profiler stations in Lake George, NY, USA.

The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2019, The Jefferson Project deployed three vertical profiler stations on the lake, collecting data on water quality and weather. Meteorological data have been included with the Jefferson Project Weather Station dataset for 2019. These vertical profiler stations are named VP_AnthonysNose, VP_CalvesPen, and VP_TeaIsland. The water quality data are collected by a YSI EXO2 Multi-parameter sonde sensors. The sensors collect data at 1 meter depth increments, starting at 1 meter and proceeding to 2 meters off bottom. The data are transferred in near real-time to an off-site database for monitoring and review. The data provided here have undergone data correction by Jefferson Project researchers.

The Jefferson Project 2020 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.

The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2020, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.