Find research datasets worth reusing

opencc-by-4.0Mar 2020View details →

Investigating dynamics between energy use and socio-demographic characteristics in spatial modeling of residential energy consumption

Files represent datasets (2017 Residential Building Stock Assessment and American Community Survey 2012-2017 5-year estimate) and R-code associated with the analysis.

opencc-by-4.0Jan 2024View details →

Determinants of rooftop solar uptake: a comparative analysis of the residential and non-residential sectors in the Basque Country (Spain)

Data for Determinants of rooftop solar uptake: a comparative analysis of the residential and non-residential sectors in the Basque Country (Spain) Rooftop solar, both in the residential and the non-residential sector, is emerging rapidly as a popular source of clean electricity. Together with utility-scale photovoltaics, its future growth is essential to achieve decarbonization targets. Therefore, understanding adoption determinants for firms and households is key to efficiently promoting its diffusion. There is a gap, however, in the knowledge of non-residential adoption determinants, as less attention has been given to this sector compared to the residential sector. As a result of this gap, there is an absence of comparative analysis across sectors. As determinants of adoption cannot be assumed to be the same in both sectors, the objective of this research is threefold. First, to analyze whether the residential and non-residential sectors share key determinants of rooftop solar investment; second, to compare the sectoral differences in these determinants; and third, to assess the policy implications of the results obtained to further promote distributed solar photovoltaic energy. For this purpose, a regional case study in the Basque Country (Spain) was conducted, applying key theoretical frameworks to both sectors in a way that maximized the comparability of the results obtained across them. The results showed that adoption determinants are very different across sectors and, therefore, sector-specific policy actions need to be taken in each sector to efficiently promote rooftop solar. For the residential sector, policy actions could build upon behavioral aspects; for the non-residential sector, economic incentives are expected to be more successful, especially among medium size businesses, which are identified as the most promising segment.

opencc-by-4.0Jun 2022View details →

Dataset of 30 energy customers with flexibility data, and distributed generation, considering residential, small commerce, large commerce, and industrial customers

The dataset has 30 customers: ten residential, ten small commerce, five large commerce, and five industrial customers. The combination of several energy customer types allows the creation of a dataset with different types of consumption profiles, generation, and flexibility, and, therefore, different values of participation in demand response events. The residential profiles of the considered customers use the data available in the Working Group on Intelligent Data Mining and Analysis (IDMA): https://site.ieee.org/pes-iss/data-sets/ The values represent a week period using 15 minutes reading periods. All the values are expressed in kWh and the matrixes were created as [customer x time_period].   We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.

opencc-by-4.0Jun 2022View details →

Demonstration Cases - Simulation data of energy consumption of residential building typologies

The dataset is about the energy analysis for retrofit strategies of 5 building typologies and the EDEA project located in 3 climates zones in Europe: South (Madrid), Central (Berlin) and North (Helsinki). The dataset includes: (1) Open Document Spreadsheet (.ods) file with the results of Heating Consumption (kWh/m2·year) and Cooling Consumption (kWh/m2·year) for the five buildings, in three locations and for several scenarios: - Locating external new insulation in walls and roof. - Replacing Windows. - Combination strategies: locating new insulation layers and replacing the existing windows. - Installing solar protection devices.

opencc-by-4.0Apr 2023View details →

Residential gasification of solid biomass: Influence of raw material on emissions

Submitted data was used to write an article: Drobniak, A., Jelonek, Z., Mastalerz, M., Jelonek, I., 2023. Residential gasification of solid biomass: Influence of raw material on emissions. International Journal of Coal Geology 271C, 104247. <a href="https://doi.org/10.1016/j.coal.2023.104247">https://doi.org/10.1016/j.coal.2023.104247</a>   Funding acknowledgments: The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland and the Green Horizon Program, Poland.   Article Abstract: With interest rising in biomass use, biomass gasification has the potential to become an imperative mechanism to deliver clean conversion of various types of solid biomass to gas. But as biomass gasification attracts growing interest, it is important to focus not only on the technological feasibility but also fully understand its environmental impact to eliminate avoidable air pollution. In this study, we investigated relationships between the composition of 14 types of solid biomass fuels and their gasification emissions in a small-scale residential outdoor setting. Our results show that the amount and type of produced emissions are strongly influenced by the gasified feed. Combining chemical and petrographic analysis proved to be a robust quality assessment method of solid biomass fuels, allowing for quick detection of their contaminants. These impurities can be directly correlated with elevated particulate matter emissions, CO, H2S, HCHO, NH3, SO2, NOx, and respiratory tract irritants. These observations show that quality testing of biomass fuels is critical not only for ensuring their high quality but also for predicting avoidable air pollution during their utilization. Although our data revealed relationships between the type of biomass fuel and gasification emissions, in general, our experiments show that small-scale gasification in a residential setting is a safe technology, and potential hazards can be eliminated by using certified fuels and ensuring appropriate distance from the source of emissions.

opencc-by-4.0Jul 2021View details →

Emission factors and chemical composition of particulate matter from residential biomass combustion

Emission factors and chemical composition of particulate matter from residential biomass combustion.

opencc-by-4.0Oct 2021View details →

TIMES-Ireland model: Residential data

This dataset is contained in a Tableau packaged workbook (.twbx) and uses Ireland's raw Building Energy Rating (BER) csv data, which is downloadable from https://ndber.seai.ie/BERResearchTool/ber/search.aspx and applies filters to this database. Tableau structures the filtered data, so that it can be exported into the residential file (VT_IE_RSD) for use in TIMES Ireland Model (TIM)

opencc-by-4.0Oct 2022View details →

openENTRANCE - Case Study 1 - Residential Demand Response - Data and Scripts

Data files and Python and R scripts are provided for Case Study 1 of the openENTRANCE project. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are full battery electric vehicles (EV), storage heater (SH), water heater with storage capabilitites (WH), air conditiong (AC), heat circulation pump (CP), air-to-air heat pump (HP), refrigeration (includes refrigerators (RF) and freezers (FR)), dish washer (DW), washing machine (WM), and tumble drier (TD). The data for the study uses represenative hours to describe load expectations and constraints for each residential device - hourly granularity from 2020 to 2050 for a representative day for each month (i.e. 24 hours for an average day in each month). The aggregated final results are in Full_potential.V9.csv and acheivable_NUTS2_summary.csv. The file metaData.Full_Potential.csv is provided to guide users on the nomenclature in Full_potential.V9.csv and the disaggregated data sets.The disaggregated loads can be found in d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv while the disaggregated maximum capacities p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv.  Full_potential.V9.csv shows the NUTS2 level unadjusted loads for the residential devices using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file. The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050). These summaries have allready adjusted the disaggregated loads with direct load participation rates from participation_rates_country.csv. A detailed overview of the data files are provided below. Where possible, a brief description, input data, and script use to generate the data is provided. If questions arise, first refer to the publication. If something still needs clarification, send an email to ryano18@vt.edu. Description of data provided <ol> <li>Achievable_NUTS2_summary.csv <ol> <li>Description <ol> <li>Average hourly achievable direct load potentials for each NUTS2 region and device for 2020, 2022, 2030,2040, 2050</li> </ol> </li> <li>Data input <ol> <li>Full_potential.V9.csv</li> <li>participation_rates_country.csv</li> <li>P_inc_SH.csv</li> <li>P_inc_WH.csv</li> <li>P_inc_HP.csv</li> <li>P_inc_DW.csv</li> <li>P_inc_WM.csv</li> <li>P_inc_TD.csv</li> </ol> </li> <li>Script <ol> <li>NUTS2_acheivable.R</li> </ol> </li> </ol> </li> <li>COP_.1deg_11-21_V1.csv <ol> <li>Description <ol> <li>NUTS2 average coefficient of performance estimates from 2011-2021 daily temperature</li> </ol> </li> <li>Data <ol> <li>tg_ens_mean_0.1deg_reg_2011-2021_v24.0e.nc</li> <li>NUTS_RG_01M_2021_3857.shp</li> <li>nhhV2.csv</li> </ol> </li> <li>Script <ol> <li>COP_from_E-OBS.R</li> </ol> </li> </ol> </li> <li>Country dd projections.csv <ol> <li>Description <ol> <li>Assumptions for annual change in CDD and HDD</li> <li>Spinoni, J., Vogt, J. V., Barbosa, P., Dosio, A., McCormick, N., Bigano, A., & Füssel, H. M. (2018). Changes of heating and cooling degree‐days in Europe from 1981 to 2100. International Journal of Climatology, 38, e191-e208.</li> <li>Expectations for future HDD and CDD used the long-run averages and country level expected changes in the rcp45 scenario</li> </ol> </li> </ol> </li> <li>EV NUTS projectionsV5.csv <ol> <li>Description <ol> <li>NUTS2 level EV projections 2018-2050</li> </ol> </li> <li>Data input <ol> <li>EV projectionsV5_ave.csv <ol> <li>Country level EV projections</li> </ol> </li> <li>NUTS 2 regional share of national vehicle fleet <ol> <li>Eurostat - Vehicle Nuts.xlsx</li> </ol> </li> </ol> </li> <li>Script <ol> <li>EVprojections_NUTS_V5.py</li> </ol> </li> </ol> </li> <li>EV_NVF_EV_path.xlsx <ol> <li>Description <ol> <li>Country level – EV share of new passenger vehicle fleet</li> <li>From: Mathieu, L., & Poliscanova, J. (2020). Mission (almost) accomplished. Carmakers’ Race to Meet the, 21.</li> </ol> </li> </ol> </li> <li>EV_parameters.xlsx <ol> <li>Description <ol> <li>Parameters used to calculate future loads from EVs</li> <li>Wunit_EV – represents annual kWh per EV</li> <li>evLIFE_150kkm <ol> <li>number of years</li> <li>represents usable life if EV only lasted 150 thousand km. Hence, 150,000/average km traveled per year with respect to country (this variable is dropped and not used for estimation).</li> </ol> </li> <li>Average age/#years assuming 150k life – represents <ol> <li>Number of years</li> <li>Average between evLIFE_150kkm and average age of vehicle with respect to the country</li> </ol> </li> </ol> </li> </ol> </li> <li>full_potentialV9.csv <ol> <li>Description <ol> <li>Final data that shows hourly demand (Maximum Reduction) and (Maximum Dispatch for each device, region, and year. <ol> <li>This data has not been adjusted with participation_rates_country.csv</li> <li>Maximum dispatch is equal to max capacity – hourly demand with respect to the device, region, year, and hour.</li> </ol> </li> </ol> </li> <li>Script <ol> <li>Full_potentialV9.py</li> </ol> </li> </ol> </li> <li>gils projection assumptions.xlsx <ol> <li>Description <ol> <li>Data from: Gils, H. C. (2015). Balancing of intermittent renewable power generation by demand response and thermal energy storage.</li> <li>A linear extrapolation was used to determine values for every year and country 2020-2050. AC – Air Conditioning, SH – Storage Heater, WH – Water heater with storage capability, CP – heat circulation pump, TD – Tumble Drier, WM – Washing Machine, DW -Dish Washer, FR – Freezer, RF – Refrigerator. The results are in the files shown below. <ol> <li>nflh – full load hours <ol> <li>nflh_ac.csv</li> <li>nflh_cp.csv</li> </ol> </li> <li>wunit – annual energy consumption <ol> <li>Wunit_rf_fr.csv</li> </ol> </li> <li>Pcycle – power demand per cycle <ol> <li>Pcycle_wm.csv</li> <li>Pcycle_dw.csv</li> <li>Pcycle_td.csv</li> </ol> </li> <li>Punit – power damand for device <ol> <li>Punit_ac.csv</li> <li>Punit_cp.csv</li> </ol> </li> <li>r – country level household ownership rates of residential device <ol> <li>rfr.csv</li> <li>rrf.csv</li> <li>rwm.csv</li> <li>rtd.csv</li> <li>rdw.csv</li> <li>rac.csv</li> <li>rwh.csv</li> <li>rcp.csv</li> <li>rsh.csv</li> </ol> </li> <li>Script <ol> <li>openENTRANCE projections.py</li> </ol> </li> </ol> </li> </ol> </li> </ol> </li> <li>heat_pump_hourly_share.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand</li> <li>From ENTROS TYNDP – Charts and Figures <ol> <li><a href="https://2020.entsos-tyndp-scenarios.eu/download-data/#download">https://2020.entsos-tyndp-scenarios.eu/download-data/#download</a></li> </ol> </li> </ol> </li> </ol> </li> <li>hourlyEVshares.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand</li> <li>From My Electric Avenue Study <ol> <li><a href="https://eatechnology.com/consultancy-insights/my-electric-avenue/">https://eatechnology.com/consultancy-insights/my-electric-avenue/</a></li> </ol> </li> </ol> </li> </ol> </li> <li>HP_transitionV2.csv <ol> <li>Description <ol> <li>Used to create Qhp_thermal_MWh_projectedV2.csv</li> <li>Final_energy_15-19 <ol> <li>Average final energy demand for the residential heating sector between 2015-2019</li> </ol> </li> <li>Final_energy_15-19_nonEE <ol> <li>Average final energy demand for the residential heating sector for energy sources that are not energy efficient between 2015-2019 (see paper for sources)</li> </ol> </li> <li>Final_energy_15-19_nonEE_share <ol> <li>share of inefficient heating sources</li> </ol> </li> <li>HP_thermal_2018 <ol> <li>Thermal energy provided by residential heat pumps in 2018</li> </ol> </li> <li>HP_thermal_2019 <ol> <li>Thermal energy provided by residential heat pumps in 2019</li> </ol> </li> <li>See publication for data sources</li> </ol> </li> </ol> </li> <li>Nflh_ac.csv, nflh_cp.csv <ol> <li>See gils projection assumptions.xlsx</li> </ol> </li> <li>nhhV2 <ol> <li>Description <ol> <li>Expected number of households for NUTS2 regions for 2020-2050</li> <li>See publication for data sources</li> </ol> </li> <li>Script <ol> <li>EUROSTAT_POP2NUTSV2.R</li> </ol> </li> </ol> </li> <li>NUTS0_thermal_heat_annum.csv <ol> <li>Description <ol> <li>Country level residential annual thermal heat requirements in kWh</li> <li>Used to determine maximum dispatch in openENTRANCE final V14.py</li> <li>Mantzos, L., Wiesenthal, T., Matei, N. A., Tchung-Ming, S., Rozsai, M., Russ, P., & Ramirez, A. S. (2017). JRC-IDEES: Integrated Database of the European Energy Sector: Methodological Note (No. JRC108244). Joint Research Centre (Seville site).</li> </ol> </li> </ol> </li> <li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv <ol> <li>Description <ol> <li>Maximum capacity – load for a device can never exceed maximum capacity</li> </ol> </li> <li>Data <ol> <li>gils projection assumptions.xlsx</li> </ol> </li> <li>Script <ol> <li>openENTRANCE final V14.py</li> </ol> </li> </ol> </li> <li>P_inc_DW.csv, P_inc_HP.csv, P_inc_SH.csv, P_inc_TD.csv, P_inc_WH.csv, P_inc_WM.csv, SAMPLE_PINC.csv <ol> <li>Description <ol> <li>Unadjusted average hourly potential for increase by NUTS2 region for 2018-2050</li> </ol> </li> <li>Data <ol> <li>d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv <ol> <li>Theoretical maximum reduction / load of the respective device</li> </ol> </li> <li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv <ol> <li>Maximum capacity</li> </ol> </li> </ol> </li> <li>Script <ol> <li>P_increaseV2.py</li> </ol> </li> </ol> </li> <li>Pcycle_dw.csv, Pcycle_td.csv, Pcycle_wm.csv <ol> <li>Description <ol> <li>power demand per cycle kWh</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>Punit_ac.csv, Punit_cp.csv <ol> <li>Description <ol> <li>Unit capacities kWh</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>Qhp_thermal_MWh_projectedV2.csv <ol> <li>Description <ol> <li>NUTS2 expectations for thermal energy demand met by heat pumps for 2022-2050</li> <li>Assumes a linear decomposition of non-renewable and non-energy efficient heating sources until 2050</li> </ol> </li> <li>Data <ol> <li>HP_transitionV2.csv</li> <li>nhhV2.csv</li> </ol> </li> <li>Script <ol> <li>HP_projection_nuts.py</li> </ol> </li> </ol> </li> <li>rac.csv, rcp.csv, rdw.csv, rfr.csv, rrf.csv, rsh.csv, rtd.csv, rwh.csv, rwm.csv <ol> <li>Description <ol> <li>Household ownership rates</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol> </li> <li>s_hdd nutsV3.csv, s_cdd nutsV3.csv, yr_hdd nutsV3.csv, yr_cdd nutsV3.csv <ol> <li>Description <ol> <li>s_hdd nutsV3.csv and s_cdd nutsV3.csv – months share of total heating and cooling degree days (yr_hdd and yr_cdd respectively)</li> <li>yr_hdd nutsV3.csv and yr_cdd nutsV3.csv – annual heating and cooling degree days respectively</li> <li>long run (2011-2021) average NUTS 2 level hdd and cdd</li> </ol> </li> </ol> </li> <li>s_wash nuts_V2.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand for washing machine, tumble drier, and dishwasher</li> </ol> </li> <li>Data <ol> <li>stamminger_V2.xlsx</li> </ol> </li> <li>Script <ol> <li>S_wash_nuts_V2.py</li> </ol> </li> </ol> </li> <li>Stamminger_2009.csv <ol> <li>Description <ol> <li>Hours share of daily energy demand for water heater – WH, storage heater – SH, air conditioner AC, heat circulation pump – CP</li> <li>From Stamminger, R. (2009). Synergy potential of smart domestic appliances in renewable energy systems.</li> </ol> </li> </ol> </li> <li>Time_index.csv <ol> <li>Used to create the appropriate timestamp for representative hours</li> </ol> </li> <li>Wunit_rf_fr.csv <ol> <li>Annual energy consumption for refrigeration and freezers</li> <li>See gils projection assumptions.xlsx</li> </ol> </li> </ol>

opencc-by-4.0May 2023View details →

Emission of volatile organic compounds from residential biomass burning and their rapid chemical transformations.

Volatile Organic Compounds (VOCs) were monitored during the Ioannina 2022/23 winter campaign, in north-east Greece. The campaign was carried out between December 6th, 2021, and January 10th, 2022. Nitrogen oxides, carbon monoxide, carbon dioxide, methane, PM10 and Black carbon were also monitored, as well as meteorological variables. Ioannina is nested within the Dinaric mountains and suffers from intense winter pollution events, due to the topology which traps the pollution over the city. The instruments deployed included a Proton Transfer Time-of-Flight Mass Spectrometry (PTR-ToF-MS 4000 – Ionicon GmbH, Austria), a greenhouse gas monitor (G2301 – Picarro Inc., USA), a suite of carbon monoxide, ozone, nitrogen oxide analyzers (APMA-360, APNA-360 and APOA-360 – Horiba Ltd., Japan), a PM10 monitor (F-701-20 – DURAG, Germany), an aethalometer (AE33 – Magee Scientific, USA) and a weather station. Radiation, historical temperature data from the University of Ioannina, and PMF analysis results are also submitted.

opencc-by-4.0May 2023View details →

2021 New York City Daily Residential Parcel Volume and Stops

These datasets give 2021 NYC residential parcel volume and stops information. The four datasets represent the estimated volume and stops served by Amazon, FedEx, UPS, and USPS respectively. The dataset is generated by using the Pluto 2021, 2020 census, 2020 USPS postal diary, and the market share among the four companies in 2021. In total,1.92 million daily residential parcels are estimated in the whole NYC area. Each row represents a unique stop. It contains the census tract ID the stop belongs to, its coordinates, and the delivery (FTA) and pickup (FTP) volume. We assume that no pickup volume is assigned to Amazon and USPS due to their service characteristics. In the case of USPS, no direct pickup service will be provided. Instead, the parcel will be directly handed to post offices for pickup. The pickup volume proportional to Amazon's market share is evenly distributed to FedEx and UPS pickup services. "myGraph.pickle": pickle file storing the NYC OSM network graph. "Shapfile.zip": Shapefiles containing different geographic features of NYC. "Centroid_OSM.zip": OSM maps storing the centroid of NYC census tracts. "NTA_dist.zip": VKT result based on NTA.  "Service Zone": facility locations represented by OSM node ID and the service areas defined by census tracts

openCC (other)Feb 2020View details →

American Residential Macrosystems - Soil chemistry data within parks and residential yards in six major metropolitan areas in the United States, 2017-2018

"In six major U.S. metropolitan cities (Boston, Baltimore, Los Angeles, Miami, Minneapolis St. Paul, and Phoenix), 1 meter soil cores were collected to evaluate soil microbial carbon and nitrogen cycle processes that are sensitive to land management. Laboratory methods followed those used by Raciti et al. (2011a,b) to measure microbial biomass carbon and nitrogen content, microbial respiration, potential net nitrogen mineralization, potential net nitrifcation, potential denitrifcation, and pools of extractable ammonium and nitrate. "

openCC (other)Feb 2020View details →

American Residential Macrosystems - Tree Survey Data for six major metropolitan areas, 2012-2013

"This dataset contains measurements and iTree output (www.itreetools.org) of woody plant species observed in residential yards and nearby natural areas. Data were collected to assess biotic ecological homogenization in six cities across the U.S. that span major ecological biomes and climatic regions: Baltimore, MD, Boston, MA, Los Angeles, CA, Miami, FL, Minneapolis/St. Paul, MN, and Phoenix, AZ."

openCustomSep 2021View details →

Ecological Survey of Central Arizona: a survey of key ecological indicators in parcels of residential areas in the greater Phoenix metropolitan area, ongoing since 2010 (Reformatted to a Darwin Core Archive)

This data package is formatted as a Darwin Core Archive (DwC-A, event core). For more information on Darwin Core see https://www.tdwg.org/standards/dwc/. This Level 2 data package was derived from the Level 1 data package found here: https://pasta.lternet.edu/package/metadata/eml/edi/115/2, which was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-cap/653/2. The abstract below was extracted from the Level 0 data package and is included for context: The Ecological Survey of Central Arizona (ESCA) is an extensive field survey and integrated inventory designed to capture key ecological indicators of the CAP LTER study area consisting of the urbanized, suburbanized, and agricultural areas of metropolitan Phoenix, and the surrounding Sonoran desert. The survey is conducted every five years at approximately 200 sample plots (30m x 30m) that were located randomly using a tessellation-stratified dual-density sampling design. Study plots cover habitats throughout the CAP LTER study area ranging from native Sonoran desert sites to residential yards to an airport tarmac. In 2010, the survey was expanded to include an assessment of residential parcels overlapping the survey plot at sites in residential areas. Many of the same variables that are measured in the 30m x 30m survey plot are measured in the parcel, including an inventory of perennial plants, and the biovolume of trees. In addition, a detailed assessment of characteristics of the parcel is performed. Investigators interested in data from the broader Ecological Survey of Central Arizona that includes all survey plots should should search the data catalog for 'ecological survey of central arizona' or 'survey 200' to locate those and other data related to the CAP LTER's ESCA.

Year 2001, 15 minute measurements of stage, water temperature, conductivity, dissolved oxygen, and pH in a small headwater stream draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.

Year 2001 continuous measurements, every 15 minutes, were made of stage, water temperature, conductivity, dissolved oxygen, and pH in a small headwater stream, Saw Mill Brook, Burlington, draining a highly suburban catchment (72% residential). Measurements are for part of the ice free season (June – December). Discharge is determined from stage using discharge vs stage regressions. Several day gaps occur periodically due to removal for recalibration.

Year 2002, 15 minute measurements of stage, water temperature, conductivity, dissolved oxygen, and pH in a small headwater stream draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.

Year 2002, continuous measurements, every 15 minutes, were made of stage, water temperature, conductivity, and dissolved oxygen in a small headwater stream, Saw Mill Brook, Burlington, MA, draining a highly suburban catchment (72% residential). Measurements are for part of the ice free season (June – December). Discharge is determined from stage using discharge vs stage regressions. Several day gaps occur periodically due to removal for recalibration.

Year 2003, 15 minute measurements of stage, water temperature, conductivity, dissolved oxygen, and pH in a small headwater stream draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.

Year 2003, continuous measurements, every 15 minutes, were made of stage, water temperature, conductivity, and dissolved oxygen in a small headwater stream, Saw Mill Brook, Burlington, MA, draining a highly suburban catchment (72% residential). Measurements are for part of the ice free season (June – December). Discharge is determined from stage using discharge vs stage regressions. Several day gaps occur periodically due to removal for recalibration.

Year 2004, 15 minute measurements of stage, water temperature, conductivity, dissolved oxygen, and pH in a small headwater stream draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.

Year 2004 continuous measurements, every 15 minutes, were made of stage, water temperature, conductivity, and dissolved oxygen in a small headwater stream, Saw Mill Brook, Burlington, MA, draining a highly suburban catchment (72% residential). Measurements are for part of the ice free season (June – December). Discharge is determined from stage using discharge vs stage regressions. Several day gaps occur periodically due to removal for recalibration.

Year 2010, 15 minute measurements of stage in a small headwater stream draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.

Year 2011, continuous measurements, every 15 minutes, were made of depth and stream temperature in a small headwater stream, Saw Mill Brook, Burlington, MA, draining a highly suburban catchment (72% residential). Discharge is determined from stage using discharge vs stage regressions.

Year 2011, 15 minute measurements of stage in a small headwater stream draining a highly suburban catchment (72% residential), Saw Mill Brook, Burlington, MA.