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zenodo44/100

Systematic review data on the role of urban planning in the context of sustainability transformations and human-nature connections

This data publication belongs to the following research paper: Harms, P., Hofer, M. & Artmann, M. Planning cities with nature for sustainability transformations — a systematic review. Urban Transform 6, 9 (2024).  https://doi.org/10.1186/s42854-024-00066-2  We conducted a systematic literature review according to the PRISMA Statement 2020 (Page et al. 2021). The list shows the steps performed and the names of the corresponding datasets available here: Step A - Identification of Records A_01_PRISMA-protocoll.pdf A_02_searchstring.txt A_03_recordsidentified.ris Step B - Screening of Records B_01_recordsscreened-title-keywords.ris B_02_recordsscreened-abstract.ris B_03_recordsscreened-fulltext.ris B_04_studiesincluded.ris B_05_screeningdecisions-overview.xlsx Step C - Qualitative Analysis C_01_codingscheme.xlsx

opencc-by-4.0Sep 2023View details →

zenodo44/100

Supplementary Materials for "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor"

This work corresponds to the results described in paper "Measurements of LoRaWAN Technology in Urban Scenarios: A Data Descriptor": <a href="https://www.mdpi.com/2306-5729/6/6/62">https://www.mdpi.com/2306-5729/6/6/62</a> The provided open-access dataset consists of JavaScript Object Notation (JSON) records stored in Comma-Separated Values (CSV) files, and the data were gathered in a span of multiple hours during two days of measurements. Each JSON file contains parameters as described below. In addition to the payload itself, every record on the server also contains additional metadata. Metadata contains general information about the LoRaWAN message and the array of parameters that provide more detailed message reception information for each Gateway (GW) receiving the message separately. Notably, these names may differ between LoRaWAN service providers. In the case of Ceske Radiokomunikace (CRa), the metadata contains the following parameters: <ul> <li> cmd—Command (message type): Incoming (uplink) message from the ED via the GW to the server. This also contains metadata from receiving GWs. </li> <li> seqno—Sequence number: The sequence number of the message in the form of a 32-bit integer. The Network Server generates this number. </li> <li> EUI—Extended Unique Identifier: A global identifier (64-bit) of the terminal device, which the manufacturer or owner assigns. The Institute of Electrical and Electronics Engineers (IEEE) Registration Authority manages the assignment of identifier pools. It is given in hexadecimal format. This identifier is used similarly to the MAC address of the network interface. </li> <li> ts—Timestamp: The time of the received message recorded at the first receiving GW. The parameter indicates the number of milliseconds since the Unix epoch (1 January 1970). </li> <li> fcnt—Frame count: Sequential number of the message (16-bit integer) sent from the device. In the case of a device reset, the value of the counter starts from zero. The value of this parameter can be used to detect a failure to receive messages. </li> <li> port—The port number is used to distinguish the type of application payload message. It is, therefore, not necessary to explicitly add it to the application payload. The Port parameter’s (8-bit integer) possible values range from 1 to 223 for the users. Other values are reserved. </li> <li> freq—Frequency: A value that corresponds to the frequency (expressed in Hertz) of the given LoRaWAN channel. Before transmitting each message, the ED pseudo-randomly selects from the range of available LoRaWAN channels on which it will transmit the message. </li> <li> toa—Time on Air: Message transmission time in milliseconds. This value is directly proportional to the data rate and message size. </li> <li> dr—Data Rate: The string parameter specifying the spreading factor, bandwidth, and coding rate. The spreading factor fundamentally affects the data rate and thus, the message time on-air. The value can be selected from the interval 7 to 12. Bandwidth values are only 125, 250, and 500 kHz. The larger the bandwidth, the higher the data rate. </li> <li> ack—Acknowledge: The parameter is of a Boolean type and indicates whether the ED requires confirmation of the sent message. The default is to avoid using acknowledgments to reduce network traffic. </li> <li> gws—Gateways: Contain an array of information objects from individual GWs, especially information about the parameters of the received signal, timestamp, identifier, and location of the GW. <ul> <li> rssi—Received Signal Strength Indicator: The received signal level on the GW, expressed in dBm. The threshold value of the Semtech SX1301 receiver is −142 dBm [<a href="https://www.mdpi.com/2306-5729/6/6/62/htm#B44-data-06-00062">44</a>]. </li> <li> snr—Signal-to-Noise Ratio: This parameter gives the ratio between the received power signal and the noise floor power level in dB. If the SNR is greater than 0, the received signal level is higher than the noise level. </li> <li> ts—Timestamp: The time of the received message in milliseconds since the Unix era (1 January 1970). </li> <li> tmms—Time in ms: GPS time in milliseconds since 6 February 1980. The GW must have GPS connectivity. </li> <li> time—UTC of the received message, with microsecond precision in the ISO 8601 format. </li> <li> gweui—GW extended unique identifier: The 64-bit number in a hexadecimal format specific for each GW. </li> <li> lat—Latitude: GW GPS latitude parameter in decimal degrees. The GW must have GPS connectivity. </li> <li> lon—Longitude: GW GPS longitude parameter in decimal degrees. The GW must have GPS connectivity. </li> </ul> </li> <li> bat—Battery status of the ED 8-bit integer value (0—external power supply, 255—battery status is unknown, 1–254—correspond to battery status 0–100%). </li> <li> data—The field contains HEX data, which is unique for the LoRaWAN device in question. It consists of information related to temperature, position, battery level, etc. In the case of our device, it represents our unique data format, which is specifically designed for the purposes of our measurements. </li> <li> device_Lat—Latitude of the measurement point gathered from the GPS. </li> <li> device_Lon—Longitude of the measurement point gathered from the GPS. </li> </ul> The undeniable advantage of the JSON format is that it is in a human-readable form. Thus, without the need for complex parsing, necessary information can be read immediately.

opencc-by-4.0Feb 2022View details →

zenodo44/100

Data and code for Bauer et al. (2022) Urban Ecosystems

Data and code for: Bauer M, Krause M, Heizinger V, Kollmann J (2022) Using crushed waste bricks for urban greening with contrasting grassland mixtures: no negative effects of brick-augmented substrates varying in soil type, moisture and acid pre-treatment. – Urban Ecosystems 25, 1369-1378. <a href="https://doi.org/10.1007/s11252-022-01230-x">DOI: 10.1007/s11252-022-01230-x</a> <a href="https://github.com/markus1bauer/2022_waste_bricks_seedmixtures/blob/master/README.md">GitHub README</a>

opencc-by-4.0Feb 2022View details →

zenodo44/100

Data for "Harmonized gap-filled dataset from 20 urban flux tower sites" for the Urban-PLUMBER project

Flux tower observations, model spin-up and site characteristics data for Urban-PLUMBER sites associated with the manuscript: <blockquote> "Harmonized, gap-filled dataset from 20 urban flux tower sites"  <a href="https://doi.org/10.5194/essd-14-5157-2022">https://doi.org/10.5194/essd-14-5157-2022</a> </blockquote> Use of any data must give credit through citation of the above manuscript and other site sources as appropriate (see below). We recommend data users consult with site contributing authors and/or the coordination team in the project planning stage. Relevant site contacts are included in site metadata.  Data can be downloaded from the bottom of this page.  <table> <tbody> <tr> <td> Sitename </td> <td> City </td> <td> Country </td> <td> Observed period </td> <td> References </td> </tr> <tr> <td> AU-Preston </td> <td> Melbourne </td> <td> Australia </td> <td> Aug 2003 – Nov 2004 </td> <td> (Coutts et al., 2007a, b) </td> </tr> <tr> <td> AU-SurreyHills </td> <td> Melbourne </td> <td> Australia </td> <td> Feb 2004 – Jul 2004 </td> <td> (Coutts et al., 2007a, b) </td> </tr> <tr> <td> CA-Sunset </td> <td> Vancouver </td> <td> Canada </td> <td> Jan 2012 – Dec 2016 </td> <td> (Christen et al., 2011; Crawford and Christen, 2015) </td> </tr> <tr> <td> FI-Kumpula </td> <td> Helsinki </td> <td> Finland </td> <td> Dec 2010 – Dec 2013 </td> <td> (Karsisto et al., 2016) </td> </tr> <tr> <td> FI-Torni </td> <td> Helsinki </td> <td> Finland </td> <td> Dec 2010 – Dec 2013 </td> <td> (Järvi et al., 2018; Nordbo et al., 2013) </td> </tr> <tr> <td> FR-Capitole </td> <td> Toulouse </td> <td> France </td> <td> Feb 2004 – Mar 2005 </td> <td> (Masson et al., 2008; Goret et al., 2019) </td> </tr> <tr> <td> GR-HECKOR </td> <td> Heraklion </td> <td> Greece </td> <td> Jun 2019 – Jun 2020 </td> <td> (Stagakis et al., 2019) </td> </tr> <tr> <td> JP-Yoyogi </td> <td> Tokyo </td> <td> Japan </td> <td> Mar 2016 – Mar 2020 </td> <td> (Hirano et al., 2015; Ishidoya et al., 2020) </td> </tr> <tr> <td> KR-Jungnang </td> <td> Seoul </td> <td> South Korea </td> <td> Jan 2017 – Apr 2019 </td> <td> (Jo et al., n.d.; Hong et al., 2020) </td> </tr> <tr> <td> KR-Ochang </td> <td> Ochang </td> <td> South Korea </td> <td> Jun 2015 – Jul 2017 </td> <td> (Hong et al., 2019, 2020) </td> </tr> <tr> <td> MX-Escandon </td> <td> Mexico City </td> <td> Mexico </td> <td> Jun 2011 – Sep 2012 </td> <td> (Velasco et al., 2011, 2014) </td> </tr> <tr> <td> NL-Amsterdam </td> <td> Amsterdam </td> <td> Netherlands </td> <td> Jan 2019 – Oct 2020 </td> <td> (Steeneveld et al., 2020) </td> </tr> <tr> <td> PL-Lipowa </td> <td> Łódź </td> <td> Poland </td> <td> Jan 2008 – Dec 2012 </td> <td> (Fortuniak et al., 2013; Pawlak et al., 2011) </td> </tr> <tr> <td> PL-Narutowicza </td> <td> Łódź </td> <td> Poland </td> <td> Jan 2008 – Dec 2012 </td> <td> (Fortuniak et al., 2013, 2006) </td> </tr> <tr> <td> SG-TelokKurau </td> <td> Singapore </td> <td> Singapore </td> <td> Feb 2015 – Feb 2016 </td> <td> (Roth et al., 2017) </td> </tr> <tr> <td> UK-KingsCollege </td> <td> London </td> <td> UK </td> <td> Apr 2012 – Jan 2014 </td> <td> (Bjorkegren et al., 2015; Kotthaus and Grimmond, 2014a, b) </td> </tr> <tr> <td> UK-Swindon </td> <td> Swindon </td> <td> UK </td> <td> May 2011 – Apr 2013 </td> <td> (Ward et al., 2013) </td> </tr> <tr> <td> US-Baltimore </td> <td> Baltimore </td> <td> USA </td> <td> Jan 2002 – Jan 2007 </td> <td> (Crawford et al., 2011) </td> </tr> <tr> <td> US-Minneapolis </td> <td> Minneapolis </td> <td> USA </td> <td> Jun 2006 – May 2009 </td> <td> (Peters et al., 2011; Menzer and McFadden, 2017) </td> </tr> <tr> <td> US-WestPhoenix </td> <td> Phoenix </td> <td> USA </td> <td> Dec 2011 – Jan 2013 </td> <td> (Chow, 2017; Chow et al., 2014) </td> </tr> </tbody> </table> For further site information and timeseries plots see <a href="https://urban-plumber.github.io/sites">https://urban-plumber.github.io/sites</a>. For processing code see <a href="https://github.com/matlipson/urban-plumber_pipeline">https://github.com/matlipson/urban-plumber_pipeline</a>. Data Two data archives are available on this page. <ul> <li>The full collection includes all observed, gap-filled, spin-up and site characteristic data, in both netcdf and text form.</li> <li>The "obs_only" archive includes a duplicate of site observation timeseries (after quality control) in a single netcdf file.</li> </ul> Full collection The full archive includes site folders with: <ul> <li><code>index.html</code>: A summary page with site characteristics and timeseries plots.</li> <li><code>SITENAME_sitedata_v1.csv</code>: comma separated file for numerical site characteristics e.g. location, surface cover fraction etc.</li> <li><code>timeseries/</code> (following files are available as netCDF and txt) <ul> <li><code>SITENAME_raw_observations_v1</code>: site observed timeseries before project-wide quality control.</li> <li><code>SITENAME_clean_observations_v1</code>: site observed timeseries after project-wide quality control.</li> <li><code>SITENAME_metforcing_v1</code>: gap-filled and prepended (10yr spinup) site observation forcing dataset for model evaluation.</li> <li><code>SITENAME_era5_corrected_v1</code>: site ERA5 surface data (1990-2020) with bias corrections as applied in the final dataset.</li> </ul> </li> </ul> "Obs Only" This archive contains duplicate data from the full collection (observations after QC): <ul> <li><code>UP_all_clean_observations_UTC_v1.nc</code>: in coordinated universal time (UTC)</li> <li><code>UP_all_clean_observations_localstandardtime_v1.nc</code>: in local standard time</li> </ul> Site references Bjorkegren, A. B., Grimmond, C. S. B., Kotthaus, S., and Malamud, B. D.: CO2 emission estimation in the urban environment: Measurement of the CO2 storage term, Atmospheric Environment, 122, 775–790, https://doi.org/10.1016/j.atmosenv.2015.10.012, 2015. Chow, W.: Eddy covariance data measured at the CAP LTER flux tower located in the west Phoenix, AZ neighborhood of Maryvale from 2011-12-16 through 2012-12-31, https://doi.org/10.6073/PASTA/FED17D67583EDA16C439216CA40B0669, 2017. Chow, W. T. L., Volo, T. J., Vivoni, E. R., Jenerette, G. D., and Ruddell, B. L.: Seasonal dynamics of a suburban energy balance in Phoenix, Arizona, International Journal of Climatology, 34, 3863–3880, https://doi.org/10.1002/joc.3947, 2014. Christen, A., Coops, N. C., Crawford, B. R., Kellett, R., Liss, K. N., Olchovski, I., Tooke, T. R., van der Laan, M., and Voogt, J. A.: Validation of modeled carbon-dioxide emissions from an urban neighborhood with direct eddy-covariance measurements, Atmospheric Environment, 45, 6057–6069, https://doi.org/10.1016/j.atmosenv.2011.07.040, 2011. Coutts, A. M., Beringer, J., and Tapper, N. J.: Characteristics influencing the variability of urban CO2 fluxes in Melbourne, Australia, Atmospheric Environment, 41, 51–62, https://doi.org/10.1016/j.atmosenv.2006.08.030, 2007a. Coutts, A. M., Beringer, J., and Tapper, N. J.: Impact of Increasing Urban Density on Local Climate: Spatial and Temporal Variations in the Surface Energy Balance in Melbourne, Australia, J. Appl. Meteor. Climatol., 46, 477–493, https://doi.org/10.1175/JAM2462.1, 2007b. Crawford, B. and Christen, A.: Spatial source attribution of measured urban eddy covariance CO2 fluxes, Theor Appl Climatol, 119, 733–755, https://doi.org/10.1007/s00704-014-1124-0, 2015. Crawford, B., Grimmond, C. S. B., and Christen, A.: Five years of carbon dioxide fluxes measurements in a highly vegetated suburban area, Atmospheric Environment, 45, 896–905, https://doi.org/10.1016/j.atmosenv.2010.11.017, 2011. Fortuniak, K., Kłysik, K., and Siedlecki, M.: New measurements of the energy balance components in Łódź, in: Preprints, sixth International Conference on Urban Climate: 12-16 June, 2006, Göteborg, Sweden, Sixth International Conference On Urban Climate, Göteborg, Sweden, 64–67, 2006. Fortuniak, K., Pawlak, W., and Siedlecki, M.: Integral Turbulence Statistics Over a Central European City Centre, Boundary Layer Meteorology; Dordrecht, 146, 257–276, https://doi.org/10.1007/s10546-012-9762-1, 2013. Goret, M., Masson, V., Schoetter, R., and Moine, M.-P.: Inclusion of CO2 flux modelling in an urban canopy layer model and an evaluation over an old European city centre, Atmospheric Environment: X, 3, 100042, https://doi.org/10.1016/j.aeaoa.2019.100042, 2019. Hirano, T., Sugawara, H., Murayama, S., and Kondo, H.: Diurnal Variation of CO2 Flux in an Urban Area of Tokyo, Sola, 11, 100–103, https://doi.org/10.2151/sola.2015-024, 2015. Hong, J., Lee, K., and Hong, J.-W.: Observational data of Ochang and Jungnang in Korea, 2020. Hong, J.-W., Hong, J., Chun, J., Lee, Y. H., Chang, L.-S., Lee, J.-B., Yi, K., Park, Y.-S., Byun, Y.-H., and Joo, S.: Comparative assessment of net CO2 exchange across an urbanization gradient in Korea based on eddy covariance measurements, Carbon Balance and Management, 14, 13, https://doi.org/10.1186/s13021-019-0128-6, 2019. Ishidoya, S., Sugawara, H., Terao, Y., Kaneyasu, N., Aoki, N., Tsuboi, K., and Kondo, H.: O2 : CO2 exchange ratio for net turbulent flux observed in an urban area of Tokyo, Japan, and its application to an evaluation of anthropogenic CO2 emissions, Atmospheric Chemistry and Physics, 20, 5293–5308, https://doi.org/10.5194/acp-20-5293-2020, 2020. Järvi, L., Rannik, Ü., Kokkonen, T. V., Kurppa, M., Karppinen, A., Kouznetsov, R. D., Rantala, P., Vesala, T., and Wood, C. R.: Uncertainty of eddy covariance flux measurements over an urban area based on two towers, Atmospheric Measurement Techniques, 11, 5421–5438, https://doi.org/10.5194/amt-11-5421-2018, 2018. Jo, S., Hong, J.-W., and Hong, J.: The observational flux measurement data of suburban and low-residential areas in Korea (in preparation), n.d. Karsisto, P., Fortelius, C., Demuzere, M., Grimmond, C. S. B., W., O. K., Kouznetsov, R., Masson, V., and Järvi, L.: Seasonal surface urban energy balance and wintertime stability simulated using three land‐surface models in the high‐latitude city Helsinki, Q.J.R. Meteorol. Soc., 142, 401–417, https://doi.org/10.1002/qj.2659, 2016. Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part I: Temporal variability of long-term observations in central London, Urban Climate, 10, Part 2, 261–280, https://doi.org/10.1016/j.uclim.2013.10.002, 2014a. Kotthaus, S. and Grimmond, C. S. B.: Energy exchange in a dense urban environment – Part II: Impact of spatial heterogeneity of the surface, Urban Climate, 10, Part 2, 281–307, https://doi.org/10.1016/j.uclim.2013.10.001, 2014b. Masson, V., Gomes, L., Pigeon, G., Liousse, C., Pont, V., Lagouarde, J.-P., Voogt, J., Salmond, J., Oke, T. R., Hidalgo, J., Legain, D., Garrouste, O., Lac, C., Connan, O., Briottet, X., Lachérade, S., and Tulet, P.: The Canopy and Aerosol Particles Interactions in TOulouse Urban Layer (CAPITOUL) experiment, Meteorol Atmos Phys, 102, 135, https://doi.org/10.1007/s00703-008-0289-4, 2008. Menzer, O. and McFadden, J. P.: Statistical partitioning of a three-year time series of direct urban net CO2 flux measurements into biogenic and anthropogenic components, Atmospheric Environment, 170, 319–333, https://doi.org/10.1016/j.atmosenv.2017.09.049, 2017. Nordbo, A., Järvi, L., Haapanala, S., Moilanen, J., and Vesala, T.: Intra-City Variation in Urban Morphology and Turbulence Structure in Helsinki, Finland, Boundary-Layer Meteorol, 146, 469–496, https://doi.org/10.1007/s10546-012-9773-y, 2013. Pawlak, W., Fortuniak, K., and Siedlecki, M.: Carbon dioxide flux in the centre of Łódź, Poland—analysis of a 2-year eddy covariance measurement data set, International Journal of Climatology, 31, 232–243, https://doi.org/10.1002/joc.2247, 2011. Peters, E. B., Hiller, R. V., and McFadden, J. P.: Seasonal contributions of vegetation types to suburban evapotranspiration, Journal of Geophysical Research: Biogeosciences, 116, https://doi.org/10.1029/2010JG001463, 2011. Roth, M., Jansson, C., and Velasco, E.: Multi-year energy balance and carbon dioxide fluxes over a residential neighbourhood in a tropical city, Int. J. Climatol., 37, 2679–2698, https://doi.org/10.1002/joc.4873, 2017. Stagakis, S., Chrysoulakis, N., Spyridakis, N., Feigenwinter, C., and Vogt, R.: Eddy Covariance measurements and source partitioning of CO2 emissions in an urban environment: Application for Heraklion, Greece, Atmospheric Environment, 201, 278–292, https://doi.org/10.1016/j.atmosenv.2019.01.009, 2019. Steeneveld, G.-J., Horst, S. van der, and Heusinkveld, B.: Observing the surface radiation and energy balance, carbon dioxide and methane fluxes over the city centre of Amsterdam, Copernicus Meetings, https://doi.org/10.5194/egusphere-egu2020-1547, 2020. Velasco, E., Pressley, S., Grivicke, R., Allwine, E., Molina, L. T., and Lamb, B.: Energy balance in urban Mexico City: observation and parameterization during the MILAGRO/MCMA-2006 field campaign, Theor Appl Climatol, 103, 501–517, https://doi.org/10.1007/s00704-010-0314-7, 2011. Velasco, E., Roth, M., Tan, S. H., Quak, M., Nabarro, S. D. A., and Norford, L.: The role of vegetation in the CO2 flux from a tropical urban neighbourhood, Atmospheric Chemistry and Physics, 13, 10185–10202, https://doi.org/10.5194/acp-13-10185-2013, 2013. Velasco, E., Perrusquia, R., Jiménez, E., Hernández, F., Camacho, P., Rodríguez, S., Retama, A., and Molina, L. T.: Sources and sinks of carbon dioxide in a neighborhood of Mexico City, Atmospheric Environment, 97, 226–238, https://doi.org/10.1016/j.atmosenv.2014.08.018, 2014. Ward, H. C., Evans, J. G., and Grimmond, C. S. B.: Multi-season eddy covariance observations of energy, water and carbon fluxes over a suburban area in Swindon, UK, Atmospheric Chemistry and Physics, 13, 4645–4666, https://doi.org/10.5194/acp-13-4645-2013, 2013.

opencc-by-4.0Dec 2021View details →

zenodo44/100

URBANWASTE - Dataset 1 URBAN_METABOLISM_DATA

General description Within URBANWASTE Work Package 2 data from the pilot cases needed to perform the metabolic analysis was collected. In this sense, mainly data regarding waste generation and management, tourism (accommodation capacity, tourist flows, tourism economy) and socio-economic data of each pilot was collected. The indicator sets finally collected were previously cross-checked with the 11 URBANWASTE Pilot Cases regarding data availability on pilot case scale to ensure their suitability and practicability to answer specific URBANWASTE questions. This cross-checking was done by the means of performing a “Survey on data availability” within Task 2.3. Origin, Nature and scale of data For data collection, all pilot cases partners received an empty excel database divided into three thematic areas (waste related data, socio-economic data and tourism related data). In case the pilot case partners did not have access to the requested information, other organisations such as local municipal departments, waste management companies, tourism associations, national statistical agencies etc. were contacted by them for support in data provision. The data collected with these databases mainly represent statistical data. For transparency reasons, data sources were to be specified as well. The spatial scale of the collected data was supposed to be the pilot case area (meaning for the whole city, municipality or metropolitan area). As data on this small scale was not available for all data sets, some of the provided data is on regional or even national level. For ensuring transparency, the spatial scale had to be specified for each data set. According to the type of indicators, the temporal scale varies from annual data to monthly data. For selected data sets, time series data at annual scale were collected for the period 2000 – 2015. For some selected data sets (e.g. waste quantities, tourist arrivals & overnight stays), additionally, also time series on monthly scale were collected for the period 2013 – 2015. Data Format The database prepared to collect the data needed for performing the metabolic analysis was divided into three thematic areas, which are further divided in categories as indicated below: Waste related data - Waste generation and waste quantities [number] - Waste prevention [text] [number] - Waste management [number] [%] [€] Socio-economic data - Description of the pilot case [number] [km²] - Economy [number] [%] [€] - Society [number] [%] - Building statistics [%] Tourism related data - Tourism economy [€] - Accommodation capacity [number] - Tourist flows [number] - Other tourism related information [number] Each category contains a lot of indicators, each indicator being identified by a data ID, a unit, and a spatial scale. Data sources had to be specified as well. When needed, the definitions of these indicators were added directly in the database template. In total, 48 data sets (some of them further divided into sub-sets) were collected. Most of the collected data represent quantitative data in the format of [number], [%] [€] or [km²]. The data on urban metabolism received from the pilot cases is stored in 1 excel database. Further Information and Contact The data on waste generation and management, socio-economic data and tourism data used for all the assessments performed within Work Package 2 and presented in this report was provided by the URBANWASTE pilot cases. More detailed information is contained within the database. In case of questions related to this database please contact: abf@boku.ac.at For more information on the URBANWASTE project please visit: http://www.urban-waste.eu/

opencc-by-4.0Nov 2017View details →

zenodo44/100

Survey data on behaviours and attitudes towards green food consumption of participants of the SmartFood Urban Living Lab in Warsaw, Poland

In this dataset, we present raw data of a survey on behaviours and attitudes towards green food consumption, conducted between June 2023 and April 2024 among a group of 21 households from Warsaw, participating in a SmartFood Urban Living Lab (ULL). The dataset is complemented with results collected from two control groups. The SmartFood Urban Living Lab was an intervention aimed at providing residents of urban blocks of flats with a novel technology for growing their own food. The ULL served as an experimental ground for testing and refining innovations such as hydroponic cabins, rainwater management systems, solar energy systems, and insect farming units. Residents actively participated in the lab, providing valuable insights into the practical challenges and benefits of urban farming, which helped refine and adapt the technologies for broader application. After each month of the intervention, a survey was conducted to check participants' behaviours and attitudes towards green food consumption

opencc-by-4.0Apr 2024View details →

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Raw data of the study: Categorizing urban avoiders, utilizers, and dwellers for identifying bird conservation priorities in a northern Andean city

This datasheet contains raw data on bird count records made from 2016 and 2019. Data were taken in urban and adjacent non-urban areas of Medellín, Colombia. It was part of a collaborative sampling effort during environmental assessments and personal research, summarizing systematic information on 139 sampling points (124 within the city and 15 in adjacent non-urban areas). All points were sampled under the same protocol in order to facilited data for research; in all cases, sampling was in charge of ornithologist with at least 4 years of previous experience in bird surveys. This protocol consisted in sampling during 10 minutes, four times per point (i.e., repetitions), using a fixed radius of 25 m.  Information on bird surveys (Count_Data within the corresponding datasheet tab) contains the ID of each site; whether corresponded to a urban or non-urban site; in what category of urban development the site was located, based on 1000, 500 and 200 m buffers (from the observer during bird counts: moderate, low or high); the taxonomic information of each species (order, family, scientific name); the number of recorded individuals;  the repetition or number of the visit (1, 2, 3, or 4); the name of the project; the name of the observer, and the date of sampling.  Information on categorization of bird species (Categorization within the corresponding datasheet tab) represents additional information on altitudinal ranges, trophic guilds, distribution, and others. In addition, information on frequency for each bird species is given, according to the location of each sampling site and the way it was grouped. This information was the base for categorizing bird species as urban avoider, utilizer, or dweller, under the calculations and decision rules that are also given within the corresponding cells of the datasheet. Any further information or questions about this data could be ask directly, writing to the e-mails: jgarizabal@unal.edu.co or njmacer@unal.edu.co.

opencc-by-4.0Jul 2024View details →

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Data sets used for: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry

Original videos and reference bulk velocity and water depth data sets used to develop the study: Urban runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry. The reference bulk velocity and water depth data sets were obtained with the Nivus OFR Radar and Nivus NivuCompact sensors, respectively.

opencc-by-4.0May 2018View details →

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Data sets for simulation of urban construction consolidation centres

Data from SUCCESS H2020 project used as input in the simulation activities of the Work Package 4 This data sets is a public version of the data used in the simulation activities of the workpackage 4 in the SUCCESS project. It can be used to simulate the options of using one, several or no construction consolidation centers in an urban area. The dataset is composed of 7 distinct CSV files. All CSV files have headers. <ol> <li>CCC_options_data.csv</li> <li>construction sites_data.csv</li> <li>material_demand.csv</li> <li>material_demand_periods.csv</li> <li>origin_destination.csv.csv</li> <li>suppliers_data.csv</li> <li>trucks_data.csv</li> </ol> construction sites_data file This file contains descriptions of construction sites that would be candidate to use the services of a Construction Consolidation Center (CCC). This file contains 99 observations of 11 fields : <ol> <li> site_id  (String) a unique identifier of the construction site, composed of: <ul> <li> one letter,  </li> <li> an underscore, and  </li> <li> 3 digits. The letter represents the success pilot that provided the data. The digits sequence is the numeric identifier for the pilot. </li> </ul> </li> <li> private_public (String) The mention whether the site builds a public building, a private building or a mixed building (both public and private) </li> <li> site_profile (String) The profile of the building under construction: <ul> <li> Profile I is an apartments building, </li> <li> Profile II is an offices building, </li> <li> Profile III is a leisure construction, </li> <li> Profile IV is a specific building like an hospital </li> </ul> </li> <li> Y1 (Integer) the turnover of the construction site on the first year of operations in EUR </li> <li> Y2 (Integer) the turnover of the construction site on the second year of operations in EUR </li> <li> Y3 (Integer) the turnover of the construction site on the third year of operations in EUR </li> <li> start (Date) The start date of the construction project </li> <li> end (Date) The end date of the construction project </li> <li> duration (Integer) The duration of the construction project in months </li> <li> total_value_eur (Integer) The total value of the construction project in EUR </li> <li> size_sqm (Integer) The size of the construction project in square meters </li> </ol> CCC_options_data file This file contains descriptions of Construction Consolidation Centers that could service construction sites. This file contains 25 observations of 40 fields : <ol> <li>ccc_id (String) a unique identifier of the CCC, composed of: <ul> <li>one letter,</li> <li>an underscore, and</li> <li>3 digits. The letter represents the success pilot that provided the data. The digits is the numeric identifier for the pilot.</li> </ul> </li> <li>capacity_sqm (Integer) The storage area capacity of the CCC in square meters</li> <li>capacity_cubic_meters (Integer) The storage volume capacity of the CCC in cubic meters</li> <li>Activation_Cost (Integer) The CCC activation costs in EUR</li> <li>Accessories (Integer) The storage capacity for Accessories</li> <li>Bitumen (Integer) The storage capacity for Bitumen</li> <li>Bricks (Integer) The storage capacity for Bricks</li> <li>Cement (Integer) The storage capacity for Cement</li> <li>Coating (Integer) The storage capacity for Coating</li> <li>Electrical (Integer) The storage capacity for Electrical</li> <li>Epoxi (Integer) The storage capacity for Epoxi</li> <li>External_Doors (Integer) The storage capacity for External_Doors</li> <li>Fences (Integer) The storage capacity for Fences</li> <li>Fire_Doors (Integer) The storage capacity for Fire_Doors</li> <li>Gabions (Integer) The storage capacity for Gabions</li> <li>Garden_Equipment (Integer) The storage capacity for Garden_Equipment</li> <li>Geotexil (Integer) The storage capacity for Geotexil</li> <li>Glass_wool (Integer) The storage capacity for Glass_wool</li> <li>Hydraulic (Integer) The storage capacity for Hydraulic</li> <li>Internal_Doors (Integer) The storage capacity for Internal_Doors</li> <li>Lift (Integer) The storage capacity for Lift</li> <li>Metal (Integer) The storage capacity for Metal</li> <li>Metal_1 (Integer) The storage capacity for Metal_1</li> <li>Paint (Integer) The storage capacity for Paint</li> <li>Parquet (Integer) The storage capacity for Parquet</li> <li>Pipes (Integer) The storage capacity for Pipes</li> <li>Plants (Integer) The storage capacity for Plants</li> <li>Plaster (Integer) The storage capacity for Plaster</li> <li>Polystyrene (Integer) The storage capacity for Polystyrene</li> <li>Precasted_Concrete (Integer) The storage capacity for Precasted_Concrete</li> <li>Roof (Integer) The storage capacity for Roof</li> <li>Scaffolding (Integer) The storage capacity for Scaffolding</li> <li>Signals (Integer) The storage capacity for Signals</li> <li>Steel (Integer) The storage capacity for Steel</li> <li>Stone (Integer) The storage capacity for Stone</li> <li>Store_Equipment (Integer) The storage capacity for Store_Equipment</li> <li>Tar (Integer) The storage capacity for Tar</li> <li>Tiles (Integer) The storage capacity for Tiles</li> <li>Windows (Integer) The storage capacity for Windows</li> <li>Wood (Integer) The storage capacity for Wood</li> </ol>   suppliers_data file   This file contains description of suppliers that provide materials to the above construction sites. This file contains 407 observations of 3 fields : <ol> <li>supplier_id (String) an identifier of the supplier, composed of: <ul> <li>one letter,</li> <li>an underscore, and</li> <li>3 digits. The letter represents the success pilot that provided the data. The digits is the numeric identifier for the pilot.</li> </ul> </li> <li>Material_delivered (String) the material delivered by the supplier</li> <li>Truck (Integer) the truck identifeir of the usual truck used by the supplier to deliver the material</li> </ol> trucks_data file This file contains description of truck used by suppliers to deliver construction sites. This file contains 5 observations of 6 fields: <ol> <li>Truck_id (Integer) a unique identifier for the truck</li> <li>Vehicle (String) description of the vehicle (including the number of axles)</li> <li>Capacity_(kg) (Integer) the material transport capacity of the truck in kilograms</li> <li>Capacity_(m3) (Integer) the material transport capacity of the truck in cubic meters</li> <li>FlagFirstEchelon (String) a flag indicating if the truck is used in 1st echelon</li> <li>FlagSecondEchelon (String) a flag indicating if the truck is used in 2nd echelon</li> </ol> origin_destination file This file contains the quantitative data of distance and time to travel from construction sites, suppliers, and CCCs to construction sites, suppliers and CCCs using a delivery truck. This file contains 38640 observations of 4 fields: <ol> <li>origin (String) A composite identifier of the origin location, composed of: <ul> <li>the type of location ('site', 'ccc' or 'supplier'),</li> <li>an underscore, and</li> <li>the id of such location type</li> </ul> </li> <li>destination (String) A composite identifier of the destination location, composed of: <ul> <li>the type of location ('site', 'ccc' or 'supplier'),</li> <li>an underscore, and</li> <li>the id of such location type</li> </ul> </li> <li>meters (Integer) The drive distance from origin to destination in meters</li> <li>seconds (Integer) The driving time from origin to destination in seconds</li> </ol> material demand file This file contains the qualitative data representing the material demand of construction sites per construction site profile . This file contains 1277 observations of 7 fields: <ol> <li>demand_id (Integer) a unique identifier for the material demand</li> <li>profile (String) the profile of the construction site for such demand</li> <li>start_date (Date) the start date of the activity</li> <li>end_date (Date) the end date of the activity</li> <li>number_of_days (Integer) the duration of the activity in days</li> <li>material (String) the type of material requested</li> <li>supplier_id (String) the identifier of the supplier providing the material</li> </ol> material_demand_periods file This file contains the quantitative demand data per demand and per period. Units of periods are weeks. This file contains 93663 observations of 5 fields: <ol> <li>demand_id (Integer) the identifier for the material demand</li> <li>profile (String) the profile type of construction for the demand</li> <li>period (Integer) the period of the construction project during which the material has to be delivered (in number of weeks from the beginning of the construction project)</li> <li>demand_m3 (Integer) the volume of material to be delivered during the period</li> <li>demand_kg (Integer) the weight of material to be deliverd during the period</li> </ol>

opencc-by-nc-sa-4.0Jun 2018View details →

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Data for: Generation of sanitation system options for urban planning considering novel technologies

This data has been used (1) to quantify the appropriateness of a set of sanitation technologies for a small town (Katarnyia) in Nepal and (2) to generate sanitation system options from the appropriate technologies as an input into strategic sanitation planning using a structured decision making process. For (1), the appropriateness is quantified based on a set of criteria, also called screening criteria. These criteria include technical, socio-demographic, climatic, and institutional aspects and are quantified using uncertainty functions in order to account for the quality and quantity of available input information. The data contains raw data as well as modelling results. The raw data is a compilation of information collected from literature, information collected through a household survey in the small town, field observations. They are all used to describe the screening criteria for the studied sanitation technologies and the small town. Results include: (1) the outcome of the technology appropriateness assessment (technology appropriateness scores); and (2) the sanitation system options (all possible sanitation systems built from the appropriate technologies, and a smaller set of divers and highly appropriate sanitation system options as an input into decision-making).

opencc-zeroDec 2017View details →

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Urban Vegetation Data - Canopy Height Model (Brussels Capital Region, 2021)

This GIS dataset was created for the following scientific publication, as part of the EU-funded <a href="https://coolschools.eu/">Cool Schools</a> research project (under Grant Agreement No. 101003758) : Gallez, E., Canters, F., Gadeyne, S., & Baró, F. (2024). <a href="https://www.sciencedirect.com/science/article/pii/S2212041624000846?via%3Dihub">A multi-indicator distributive justice approach to assess school-related green infrastructure benefits in Brussels - ScienceDirect</a>. Ecosystem Services, 70, 101677. https://doi.org/10.1016/j.ecoser.2024.101677.  Very-High Resolution Canopy Height Model (resolution : 25cm), distinguishing between 4 vegetation types (trees, high shrubs, low shrubs and grass) in the Brussels Capital Region. Coordinate system : Lambert_Belge_72. The CHM was built on : <ul> <li>VHR aerial orthophotos (visible RGB and NIR) (“UrbIS-Ortho N-S, 2021”) for the Brussels Capital Region, of 5x5cm resolution  Source: Paradigm. (2021). UrbIS-Ortho N-S. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/fec72767-d6b6-41b9-a767-616df2779aae#access">https://datastore.brussels/web/urbisdownload</a>.  and;</li> <li>digital terrain models (DSM and DTM) of 50x50cm, captured on 22/09/2021. Paradigm.Brussels. Source: Paradigm. (2021). DSM / DTM. Paradigm.Brussels. <a href="https://datastore.brussels/web/data/dataset/1d7bd49d-fe83-4388-af85-6f5dc8ec7909#access">https://datastore.brussels/web/urbisdownload.</a></li> </ul> Both the orthophotos and digital terrain models were resampled to a 25x25cm resolution, using a bilinear interpolation method. The Canopy Height Model was then created by selecting NDVI values of 0.2 and higher, - a commonly used threshold value to distinguish vegetated land from built land (Hashim et al., 2019) -, and vegetation height thresholds of < 0.5m (for grass), 0.5 - 2m (for low shrubs), 2 - 5m (for high shrubs), and > 5m (for trees) (Derkzen et al., 2015; Sankey et al., 2018). Green roofs were excluded.The CHM raster was then converted to polygon features.  Classification : <ul> <li>From 0 to 0.5 m (nDSM value) : gridcode 1 = grass</li> <li>From 0.5 to 2 m (nDSM value): gridcode 2 = low shrubs</li> <li>From 2 to 5 m (nDSM value): gridcode 3 = high shrubs</li> <li>From 5 to 113.96 m (nDSM value): gridcode 4 = trees</li> </ul>

opencc-by-4.0Sep 2024View details →

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Data and code from: "Building multidimensional tolerance landscapes to predict the population dynamics of bacteria exposed to antibiotics in urban sewers"

City sewers harbor diverse bacterial communities exposed to various antibiotic residues resulting from human consumption and excretion. Although these residues typically occur at sub-inhibitory concentrations, they can still impact the growth rate and yield of susceptible wastewater bacteria. Many bacteria exhibit antibiotic tolerance through transient phenotypic changes. Antibiotic residues, combined with complex environmental factors like temperature and salinity, especially in coastal cities, contribute to non-additive interactions that modulate antibiotic tolerance and affect population dynamics. To better understand these interactions, we developed continuous multivariate tolerance landscapes for three bacterial species: Escherichia coli, the emerging pathogen Streptococcus suis, and the sewer-inhabiting Arcobacter cryaerophilus. We modeled their intrinsic growth rates and carrying capacities across complex environments, incorporating temperature, salinity, and concentrations of two antibiotics (ciprofloxacin and azithromycin). Using these multivariate tolerance curves, we predicted microbial population dynamics in two sewers of Barcelona, highlighting the importance of environmental complexity in shaping microbial responses to antibiotic stressors.   Usage Users can perform the analysis by running the R script (TC3D.R) after the installation of all package mentioned in the preamble,  This folder contains: - 3 datasets with OD measures for the 3 species:               * data_acrya.xlsx               * data_ecoli.xlsx               * data_ssuis.xlsx - 1 excel files with metadata (plate, well, species, environmental conditions)               * map_plate_all.xlsx - 4 datasets giving time series of the flow and several measures including  conductivity and   temperaturefor 2 sewers of Barcelona obtained from sample cabines  set during the implementation of SCOREWATER (ID:820751)               * carmel_flow.csv               * carmel_quality.csv               * poblenou_flow.csv               * poblenou_quality.csv - 1 C++ script compiled and run with the R TMB package:               * fit_growth_r_K_SS_treatment.cpp : computes the negative loglikelihood for r and K, and state DOs, given the observed DO, for the populations under one same environmental treatment (salinity * temperature * antibiotic), and computes the density-dependence parameter alpha from r and K using the Delta Method.

restrictedcc-by-4.0Jul 2024View details →

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Data: The Role of Urban Trees in Reducing Land Surface Temperatures in European Cities

Data on the LST differences between urban fabric, urban trees and urban green spaces for each city and the LST differences between urban fabric, rural forests and rural pastures (for hot days and JJA (June, July and August) average). In addition, estimates of the evapotranspiration of forests and pastures of each city and albedo estimates of urban fabric and forests are provided. The description of the column names is provided in the readme file.

opencc-by-4.0Dec 2020View details →

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Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient - Data and code

Dataset and code used in the article "Seasonal trajectories of plant-pollinator interaction networks differ following phenological mismatches along an urbanization gradient", by A. Fisogni et al., published in Landscape and Urban Planning (2022, 226:104512, <a href="https://www.sciencedirect.com/science/article/pii/S016920462200161X?via%3Dihub">https://doi.org/10.1016/j.landurbplan.2022.104512</a>)

opencc-by-4.0May 2021View details →

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Data files for: The Urban Lightning Effect Revealed with Geostationary Lightning Mapper Observations

Warm season (June, July, August; JJA) Geostationary Lightning Mapper (GLM) observations from 2018-2021. Original processing of 20-second Level 2 GLM packets into 5-min files and quality control performed by CPTEC/INPE. Complete description provided by Oda et al. (2022). Further processing conducted locally to isolate GLM flash data for the Southeast U.S., accumulate the 5-minute files into yearly and 4-year bins, and to derive total flash count ("flash"), flash days ("fday"), and average flashes per flash day ("fpfd"). Included files: <ul> <li>GOES-16 Full Disk <ul> <li>Yearly files containing all GLM data classes (flash, group, and event) with 5-minute timesteps</li> <li>Yearly files containing only GLM flash data with 5-minute timesteps</li> </ul> </li> <li>Southeast (lat-lon bounds: -96.00, -74.00, 41.00, 24.00)  <ul> <li>Final 4-year aggregate file containing derived total lightning metrics ready for analysis in GIS</li> </ul> </li> </ul>

opencc-by-4.0Nov 2022View details →

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[Raw data] Media Coverage of 3D Visual Tools Used in Urban Participatory Planning

Raw information on the articles used for the publication: Media Coverage of 3D Visual Tools Used in Urban Participatory Planning

opencc-by-4.0Jan 2023View details →

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Data on public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland

A public participatory GIS -survey dataset detailing public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland.

opencc-by-4.0Feb 2023View details →

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Data from: Complex climate-mediated effects of urbanization on plant reproductive phenology and frost risk

This dataset comprises crowdsourced data using digitized herbarium specimen images from two comprehensively digitized regional floras; the Consortium of Northeastern Herbaria (CNH; <a href="http://portal.neherbaria.org/portal/">http://portal.neherbaria.org/portal/</a>) and Southeast Regional Network of Expertise and Collections (SERNEC; <a href="http://sernecportal.org/portal/index.php">http://sernecportal.org/portal/index.php</a>) for 200 plant species in the eastern United States, and four reproductive phenophases (i.e., flowering, peak flowering, fruiting, and peak fruiting) extracted from the herbarium specimens with associated climate data from PRISM and human population density from US Census Bureau.

opencc-by-4.0Mar 2023View details →

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Data part of the manuscript Anaerobic methanotrophy is stimulated by graphene oxide in a brackish urban canal sediment

We surveyed three canals in the city of Amsterdam (Netherlands) for it methane emissions and potential to filter methane through anaerobic oxidation of methane in the canal sediment. To unravel the mechanisms involved we characterised the sediment geochemically. All data present in the manuscript is available in the Excel file.

opencc-by-4.0Dec 2022View details →

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TOM.D: Taking Advantage of Microclimate Data for Urban Building Energy Modeling

Data required to rebuild the study: "TOM.D: Taking Advantage of Microclimate Data for Urban Building Energy Modeling". In this dataset of New York City, one can find building footprints, monthly energy consumption data for each of these buildings, and matching / cleaned microclimate data from a variety of data sources which are referenced in the work. Among them, thermal infrared measurements may be found, climate models from NOAA and ERA5 may be found, and preprocessed vision systems from Google are used.

opencc-by-4.0May 2023View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

Annotated Behaviour and Observability Dataset (ABODe)

ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

DANDI Archive for NWB datasets

DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

International Brain Laboratory public data

The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

OpenNeuro

OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record