Find research datasets worth reusing

opencc-by-4.0May 2024View details →

T-Factor. WP4 Dataset: The use of Theory of Change in urban regeneration

This dataset presents 4 anonymized interviews performed online, with stakeholders involved in Work Package 4 of the T-Factor project. The content of the interviews is relevant to the analysis of the methodology of the scoping and ideating station, in which the objective was to provide city stakeholders involved in the T-Factor project with design tools to support strategic decision making. This dataset has also been used as a source of information for an article in a Scientific Journal. This article was published in Design Management Journal the 11th of November 2023 with the Title "Using Theory of Change to Support Participatory Visual Mapping in Urban transformation Projects".

opencc-by-4.0May 2024View details →

Urban heat hazard in the Global South

This dataset includes various estimates of fine-grained outdoor heat hazard and vegetation metrics for cities in the Global South, and also for U.S. cities. Extent of urban clusters based on the Global Human Settlement Layer (GHSL; RWI_ALL_v2.geojson), European Space Agency Climate Change Initiative (ESACCI; RWI_ALL_GLOB_v2.geojson) land cover data, and U.S. urbanized areas (GLOB_distance_US.geojson) in geojson format. The geojsons and data tables have several environmental and socioeconomic variables by grid and census tract (for the U.S.). The variables are: AT_max: 10-year (2010-2019) average maximum annual air temperature AT_max_summ: 10-year (2010-2019) average maximum summer air temperature AT_min: 10-year (2010-2019) average minimum annual air temperature AT_min_summ: 10-year (2010-2019) average minimum summer air temperature All_area: Area of the grid or census tract EVI: Mean 10-year (2010-2019) Enhanced Vegetation Index Grass_area: Area of grassland for ~2020 from the ESA WorldCover dataset ID: ID of urban cluster (U.S. urbanized areas havr a 'NAME' for the urbanized area instead) LST_day: 10-year (2010-2019) average daytime annual land surface temperature from MODIS Aqua LST_day_summ: 10-year (2010-2019) average daytime summer land surface temperature from MODIS Aqua LST_night: 10-year (2010-2019) average nighttime annual land surface temperature from MODIS Aqua LST_night_summ: 10-year (2010-2019) average nighttime summer land surface temperature from MODIS Aqua PM25: 10-year (2010-2019) average particulate matter below 2.5 micron (not used in paper) Pop: Population of grid or census tract REGION_WB: World Bank region SUBREGION: World Bank subregion (not used in paper) Tree: 10-year (2010-2019) average tree percentage from MODIS continuous vegetation fields Tree_area: Area of trees for ~2020 from the ESA WorldCover dataset Veg: 10-year (2010-2019) average non-tree vegetation percentage from MODIS continuous vegetation fields error: Error in Relative Wealth Index rwi: relative Wealth Index   In addition to these, the 'distance' files include a column for distance of the grids or census tract from the centroid of the cluster in belongs to.

opencc-by-4.0May 2024View details →

Global Urban Activity Changes from COVID-19 Physical Distancing Restrictions Dataset: TRacking Anomalous COVID-19 induced changEs in NightTime Lights (TRACE-NTL)

We use satellite-derived (NASA Black Marble) nighttime lights to identify, quantify, and map daily changes in human activity that are atypical for each urban area, globally, from the beginning of the pandemic until two years after its onset. The dataset TRACE-NTL consists of global daily urban disruption and recovery metrics as a response to COVID-19. TRACE-NTL: ├───ancillary ├───data └───metrics     ├───disruption     │   ├───change_segment     │   ├───city_uncertainty     │   ├───daily_change     │   └───daily_qa_flags     └───recovery <a title="Dataset description" href="https://github.com/srijac/covid-19_Nightlights">https://github.com/srijac/covid-19_Nightlights</a> Accompanying paper: Accompanying paper: Chakraborty, S., Stokes, E.C. & Alexander, O. Global urban activity changes from COVID-19 physical distancing restrictions. Sci Data 12, 98 (2025). https://doi.org/10.1038/s41597-025-04398-x

opencanada-crownMay 2024View details →

Ottawa climate data for building simulations with urban heat island effects and nature-based solutions

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Ottawa airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions. This dataset contains hourly historical and future weather files for use in building simulations for the city of Ottawa, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.  The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.     The historical and future global warming thresholds and their corresponding time periods are as follows: <table> <tbody> <tr> <td> Global Warming Scenario </td> <td> Time Period </td> </tr> <tr> <td> Historical </td> <td> 1991-2021 </td> </tr> <tr> <td> Global Warming 0.5ºC </td> <td> 2003-2033 </td> </tr> <tr> <td> Global Warming 1.0ºC </td> <td> 2014-2044 </td> </tr> <tr> <td> Global Warming 1.5ºC </td> <td> 2024-2054 </td> </tr> <tr> <td> Global Warming 2.0ºC </td> <td> 2034-2064 </td> </tr> <tr> <td> Global Warming 2.5ºC </td> <td> 2042-2072 </td> </tr> <tr> <td> Global Warming 3.0ºC </td> <td> 2051-2081 </td> </tr> <tr> <td> Global Warming 3.5ºC </td> <td> 2064-2094 </td> </tr> </tbody> </table>   The following variables are included in the files: <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>RUN</td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td>YEAR</td> <td>Year associated with the record</td> </tr> <tr> <td>MONTH</td> <td>Month associated with the record</td> </tr> <tr> <td>DAY</td> <td>Day of the month associated with the record</td> </tr> <tr> <td>HOUR</td> <td>Hour associated with the record</td> </tr> <tr> <td>YDAY</td> <td>Day of the year associated with the record</td> </tr> <tr> <td>DRI_kJPerM2</td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DHI_kJperM2</td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DNI_kJperM2</td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>GHI_kJperM2</td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>TCC_Percent</td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td>RAIN_Mm</td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>WDIR_ClockwiseDegFromNorth</td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td>WSP_MPerSec</td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td>RHUM_Percent</td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td>TEMP_K</td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td>ATMPR_Pa</td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td>SnowC_Yes1No0 </td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td>SNWD_Cm</td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>

Scientific production data on the COVID-19 pandemic in Brazilian favelas and urban communities (2020-2022)

Spreadsheet of data created for the dissertation ‘The COVID-19 pandemic in Brazilian favelas and urban communities: a bibliometric analysis’, PPGICS/ICICT, Fiocruz, to respond to the bibliometric analysis elaborated in the research, Brazil, Rio de Janeiro, May 2024.

opencc-by-4.0Mar 2023View details →

Mitigating urban heat island through neighboring rural land cover: Dataset

A dataset of the manuscript "Mitigating urban heat island through neighboring rural land cover". Includes: Land surface temperature acquisition code (Google Earth Engine) - Average_LST_GEE.sh; Codes for calculating urban development intensity - UrbanDevelopmentIntensity.py; Regression, Machine Learning, Interpretable Machine Learning Code - AllRegression.py, SHAP.py, ALE.py; a zip file containing the data used in the calculations - OperationalData.rar.

Existing Urban Hyperspectral Reference Data

This dataset supports the publication "Urban Hyperspectral Reference Data Availability and Reuse: State-of-the-Practice Review" DOI: 10.1111/phor.12508. The urban hyperspectral signatures of 9 pre-existing spectral libraries have been converted into .xlsx file formats and organized by material category. This reformatted and compiled dataset is uploaded to this open-source repository to promote FAIR (Findable, Accessible, Interoperable, Reusable) data principles and facilitate data preservation and reuse.

opencc-zeroJun 2024View details →

dryad36/100

Urban and rural chickadee response to novel object and simulated predator

Urbanization is changing natural landscapes worldwide, pushing species to quickly acclimate or adapt if they are to survive in urban environments. Mountain chickadees (Poecile gambeli) readily nest in both urban and rural environments without suffering apparent reproductive costs. However, whether urban-nesting chickadees are successful in these environments due to differences in behaviour between urban and rural birds remains untested. We examined the behavioural responses of urban and rural nesting mountain chickadee females when presented with a novel object (red plastic cup) or simulated predator (imitation squirrel model) at the nest. Behavioural responses depended on both the type of model and the habitat. As expected, mountain chickadees responded more strongly to squirrel models than novel objects; however, the magnitude of the difference in response depended on habitat. Urban birds seemingly ignored the novel object, spending little time investigating, and re-entering the nest box quickly. In contrast, rural birds spent more time reacting to the novel object and alarm calling within 5 m of the nest. When presented with a predator model, the urban birds reacted relatively more strongly (compared to the novel stimulus) than rural birds, spending more time within 5 m of the nest and alarm calling. These results suggest that either mountain chickadees in urban environments quickly acclimatize to the presence of novel objects or, potentially, that less neophobic birds disproportionately settle in urban environments or experience positive selection in urban areas. Either way, reduced neophobia may aid in mountain chickadees' ability to readily and successfully nest in such habitats.

Hematological and biochemical profiles, infection and habitat quality in an urban rat population

Dataset employed in the analysis of "Hematological and biochemical profiles, infection and habitat quality in an urban rat population". For details in sampling methodology, see article of same name. Composed of three dataset files, with codebooks associated: <ul> <li>animal_data: morphological and parasitological data of captured individuals</li> <li>hematological_data: data on hematological parameters of captured individuals</li> <li>biochemical_data: data on biochemical markers of captured individuals</li> </ul> All individuals are identified by an individual code (rat_ID), and can be cross-linked through the bases. Files are composed of two sheets -data sheet: original data, with regular labels -codebook: comprehensive codebook for the data sheet.

Data for irrigation impacts on urban heat stress in North America

Includes all model simulation results, summaries by urban clusters, and evaluation results of the study.  The netcdf files are for various model variables and simulations (noURB for no urban simulation, IRR for irrigation simulation, and CTRL for the urban with no irrigation simulation, which is treated as the baseline), the csv files are the summaries for various cases (by urban cluster, and by world and climate zone for model evaluations), and the geotiff files are the raster images for key variables shown in the manuscript. The python notebook has all the scripts to estimate the heat stress metrics from the netcdf files.

Fig. 7 in Urban Green Areas, Recreational Use And Health Impact Of Victory Gardens (Córdoba - Spain)

Fig. 7. Principal pollination pediod for ornamental flora in the studied gardens.

Fig. 3 in Urban Green Areas, Recreational Use And Health Impact Of Victory Gardens (Córdoba - Spain)

Fig. 3. Origin for ornamental flora in the studied gardens.

Fig. 4 in Urban Green Areas, Recreational Use And Health Impact Of Victory Gardens (Córdoba - Spain)

Fig. 4. Pollination strategy for ornamental flora in the studied gardens.

Fig. 1 in Urban Green Areas, Recreational Use And Health Impact Of Victory Gardens (Córdoba - Spain)

Fig. 1. Map showing the location of Córdoba city.

opencc-by-4.0Dec 2016View details →

Fig. 3 in The Relation Of Forest And Air Pollution With Human Health In Urban Territories Of Lithuania

Fig. 3. The correlation between the forest coverage and air pollution indicators.

opencc-by-4.0Dec 2016View details →

Fig. 2 in The Relation Of Forest And Air Pollution With Human Health In Urban Territories Of Lithuania

Fig. 2. The correlation between the forest and human health indicators.

Figure 4 in Far from urban areas: plastic uptake in fish populations of subtropical headwater streams

Figure 4. Length frequency distribution of the fibers.

Figure 2 in Far from urban areas: plastic uptake in fish populations of subtropical headwater streams

Figure 2. Plastic fibres from fish intestines.

Figure 3 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)

Figure 3. Flow diagram for calculation of WF in Van province.

Figure 2 in An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)

Figure 2. Monthly average air temperature, total and effective precipitation values in Van.