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129 results for “urban greening”
Identifying the mechanisms by which irrigation can cool urban green spaces in summer
<p>This dataset contains the measured soil moisture and microclimate data from two (2021 and 2022) urban green space irrigation experiments conducted in Burnley, Melbourne, Australia. The experiments consisted of two treatments, irrigated turf and unirrigated turf. The purpose of the experiments was to provide testing (2021) and evaluation (2022) data for an urban ecohydrological model, UT&C. </p> <p><br>After evaluating the performance of UT&C in modelling soil moisture and microclimate, UT&C was used to model the surface energy balance and evapotranspiration processes of the irrigated and unirrigated turf. This dataset also contains the modelled soil moisture, microclimate, surface energy balance and evapotranspiration data, as well as the measured background climate data at the reference climate station and the forcing data for the model.</p> <p><br>The aims of this study were to:<br>i) identify the proportional contribution of different evapotranspiration processes to irrigation cooling effect, and <br>ii) quantify the impacts of different irrigation amounts (from 2 to 30 mm/d) on the cooling effect of irrigating turfgrass in Melbourne, Australia during normal summer conditions.</p> <p>This study was published in:<br>Pui Kwan Cheung, Naika Meili, Kerry A. Nice, Stephen J. Livesley (2024). Identifying the mechanisms by which irrigation can cool urban green spaces in summer. Urban Climate. 55,101914. https://doi.org/10.1016/j.uclim.2024.101914.</p>
Urban pluvial flood maps under different green cover scenarios
<p>This dataset provides pluvial flood water depth maps for the cities of Logroño, Spain; Gdynia, Poland; Milan Italy; and Athens Greece as a part of the REACHOUT project. The maps are generated using a Pluvial Flood Tool for different return periods estimated based on observations and EURO-CORDEX future climate change scenarios (Logroño only) under different nature-based green cover scenarios, depending on the city.</p> <p>Technical Info</p> <p>The pluvial flood hazard maps are generated for each event using rainfall intensity as input for the hydrostatic inundation model SaferRAIN (Samela et al., 2020). This is a simplified raster-based model based on a hierarchical filling and spilling algorithm, identifying inundated areas on the basis of high-resolution digital elevation model. It accounts for spatially distributed rainfall input and infiltration, building upon the pixel-based Green-Ampt model (Green and Ampt, 1911). It is suitable for applications over large urban areas.</p> <p>Rainfall input for the pluvial flood model is computed for return periods (RPs) of 2-, 5-, 10-, 25-, 50-, 100-, 200-years based on the historical rainfall data. Different datasets have been utilized in various cities to tailor the analysis to their specific needs. More specifically:</p> <ul> <li> <p>In the city of Gdynia, historical local station data (Climate data IMGW 1960-2021: https://danepubliczne.imgw.pl/) are used to estimate RPs and assess different precipitation events. </p> </li> </ul> <ul> <li> <p>For the cities of Milan and Athens, 2.2-km ERA5 downscaled data are employed to assess historical precipitation events under different RPs (Essenfelder et al., 2021). </p> </li> <li> <p>In the city of Logroño, historical local station data (SOS-Logroño precipitation data 1999-2022: https://www.larioja.org/emergencias-112/es/meteorologia/datos-actuales-rioja/detalle-estacion?homepage=9&cod_muni=89) are used to estimate RPs and assess different precipitation events. Additionally, here, future climate change projections have been analyzed. These projections are based on the precipitation Intensity-Duration-Frequency (IDF) curves computed from the EURO-CORDEX data (Pal J et al., 2024 - <a href="https://doi.org/10.5281/zenodo.14035736" target="_blank" rel="noopener">10.5281/zenodo.14035736</a>). Observations are then scaled according to the changes simulated between future and historical scenarios, using the median and 90th percentile values estimated from the EURO-CORDEX ensemble.</p> </li> </ul> <p>Different urban green cover maps are used as input for the model to simulate the pluvial flood maps under the current land cover conditions and for different nature-based adaptation scenarios for each city to estimate their benefits. Nature-based adaptation scenarios are the result of codesign processes carried out within REACHOUT, involving local stakeholders, experts and representatives of local administrations. Urban green cover scenarios were identified based on areas that could be converted from built-up areas and concrete surfaces (no water infiltration) to green areas allowing for rainwater infiltration. In addition, during this process, local station precipitation, high-resolution digital elevation model and high-resolution land cover data were collected to configure and run the pluvial flood model.</p> <p>Short description of the datase:</p> <p>This dataset contains urban pluvial flood maps for return periods of 2-, 5-, 10-, 25-, 50-, 100-, 200-years for hourly and 15-minute events for different urban green cover scenarios and climate change scenarios depending on the city.</p> <p>Format:</p> <p>The format of this dataset is organized in a ZIP file: PluvialFloodMap_{Cityname}.zip. The zip file is organised into sub-folders, one for each urban green cover scenario, including raster (Tiff) files for the rainfall event associated with each return period.</p> <p>Logrono:</p> <ul> <li> <p>Precipitation events historical: 15-minute events – 9.79 mm (RP2), 13.51mm (RP5), 16.27 mm (RP10), 20.15 mm (RP25), 23.33 mm (RP50), 26.77 mm (RP100), 30.50 mm (RP200)</p> </li> <li> <p>Precipitation events climate change: 15-minute events – CC_Q50 (median): 10.49 mm (RP2), 14.91 mm (RP5), 18.32 mm (RP10), 23.18 mm (RP25), 26.61 mm (RP50), 31.25 mm (RP100), 35.40 mm (RP200): CC_Q90 (90th percentile): 11.83 mm (RP2), 16.76 mm (RP5), 20.96 mm (RP10), 26.87 mm (RP25), 32.27 mm (RP50), 38.99 mm (RP100), 46.65 mm (RP200)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS planned: baseline + additional 4 bioswales/ponds (= 29,850 m3) and a green corridor (5.3 km x 5 m) in the southern part of the city.</p> </li> <li> <p>NBS planned plus: NBS planned scenarios + additional small ponds/rain gardens (depth 0.5 m, 13,500 m3)</p> </li> <li> <p>All Green: baseline + all open spaces converted to green</p> </li> </ul> <p>Milan</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 33.36 mm (RP5), 38.52 mm (RP10), 45.04 mm (RP25), 49.88 mm (RP50), 54.68 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>DMG_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise economic damage reduction</p> </li> <li> <p>DMG_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise economic damage reduction</p> </li> <li> <p>POP_Green Buildings*: baseline + establishment of new green roofs, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green Spaces*: baseline + open, ground spaces converted to green, defined to prioritise exposed population reduction</p> </li> <li> <p>POP_Green City*: combination of Green Buildings and Green Spaces scenarios, defined to prioritise exposed population reduction</p> </li> </ul> <p>* Each green conversion scenario considers four different incremental conversion percentages: 25%, 50%, 75%, and 100% of all potential green areas.</p> <p>Gdynia</p> <ul> <li> <p>Precipitation events historical: 6-hours events – 24.89 mm (RP2), 35.93 mm (RP5), 43.55 mm (RP10), 53.19 mm (RP25), 60.60 mm (RP100), 75.74 mm (RP200) </p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + bioswales/ponds (+ 50,000 m3)</p> </li> <li> <p>All green: baseline + all open spaces converted to green</p> </li> <li> <p>NBS All green: all green + NBS</p> </li> </ul> <p>Athens</p> <ul> <li> <p>Precipitation events historical: 1-hour events – 28.05 mm (RP5), 34.08 mm (RP10), 42.28 mm (RP25), 48.83 mm (RP50), 55.74 mm (RP100)</p> </li> <li> <p>Baseline: current green cover</p> </li> <li> <p>NBS: baseline + ponds/rain gardens in existing green spaces (depth 1m) in the northern district of the city</p> </li> <li> <p>All green: baseline + all open spaces (>100 m2) converted to green</p> </li> </ul>
Data on public understandings of and attitudes towards carbon-smart urban green infrastructure in Kumpula, Helsinki
<p>A public participatory GIS -survey dataset detailing public understandings of and attitudes towards carbon-smart urban green infrastructure in Kumpula, Helsinki, Finland.</p>
Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland
<p>This repository contains data described in the article "Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland" (Heikinheimo et al. 2023) and used in the research article "Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions" (Viinikka et al. 2023). <br> <br> This repository contains data on green space quality and path distances to different types of green spaces. The path distances represent green space accessibility using active travel modes (walking, cycling). The path distances were calculated using the pedestrian street network across the seven largest urban regions in Finland. We derived the green space typology from the Urban Atlas Data that is available across functional urban areas in Europe and enhanced it with national data on water bodies, conservation areas and recreational facilities and routes from Finland. We extracted the walkable street network from OpenStreetMap and calculated shortest paths to different types of green spaces using open-source Python programming tools. Network distances were calculated up to ten kilometers from each green space edge and the distances were aggregated into a 250 m x 250 m statistical grid that is interoperable with various statistical data from Finland. The geospatial data files representing the different types of green spaces, network distances across the seven urban regions, as well as the processing and analysis scripts are shared in an open repository. These data offer actionable information about green space accessibility in Finnish city regions and support the integration of green space quality and active travel modes into further research and planning activities.</p> <p> </p> <p><strong>Data description article: </strong></p> <p>Heikinheimo, V., Tiitu, M., & Viinikka, A. (2023). Data on different types of green spaces and their accessibility in the seven largest urban regions in Finland. <em>Data in Brief</em>, <em>50</em>, 109458. <a href="https://doi.org/10.1016/j.dib.2023.109458">https://doi.org/10.1016/j.dib.2023.109458</a></p> <p><strong>Related research article:</strong> </p> <p>Viinikka, A., Tiitu, M., Heikinheimo, V., Halonen, J. I., Nyberg, E., & Vierikko, K. (2023). Associations of neighborhood-level socioeconomic status, accessibility, and quality of green spaces in Finnish urban regions. <em>Applied Geography</em>, <em>157</em>, 102973. <a href="https://doi.org/10.1016/j.apgeog.2023.102973">https://doi.org/10.1016/j.apgeog.2023.102973</a></p>
Survey data on behaviours and attitudes towards green food consumption of participants of the SmartFood Urban Living Lab in Warsaw, Poland
<p>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</p>
Dateset on 'Disentangling associations of human wellbeing with green infrastructure, degree of urbanity, and social factors around an Asian megacity'
<p>The data was collected a part of the baseline survey on household socio-economics among the Bengalurian along the rural-urban interface. </p>
Data on public perceptions of, attitudes towards, and values for managing urban green infrastructure for carbon, biodiversity, and well-being outcomes in Helsinki, Finland
<p>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.</p>
UBGG-3m: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network
<p>The UBGG dataset provides easily access and leverage to researchers and analysts, which is stored in the following Zenodo repository (<a href="https://doi.org/10.5281/zenodo.8053333">https://doi.org/10.5281/zenodo.8352777</a>). The UBGG dataset consists of two main components:</p> <ul> <li><strong>UBGG-3m: the fine-grained UBGG map product of 36 metropolises in China.</strong> The UBGG-3m dataset captures the intricate urban landscape features with remarkable precision, providing a detailed representation at an impressive 3-meter resolution. Fig. 1 in User Guides shows the classification results for 36 Chinese metropolises. Researchers can delve into the nuances of the UBGG continuum, gaining invaluable insights into the interplay between the blue, green, and gray elements of urban environments in each metropolis.</li> </ul> <ul> <li><strong>UBGGset:</strong> <strong>the large-volume sample dataset to support the UBGG deep learning research.</strong> Complementing the UBGG-3m dataset, UBGGset serves as a large-volume sample dataset specifically tailored to support and foster UBGG research endeavors (Fig. 2). The UBGGset consists of 14,627 sample images (without data augmentation), with dimensions of 256 pixels in length and width, covering an urban area of approximately 2,272 km<sup>2</sup>. The UBGGset was constructed with co-registered pairs of 3 m Planet images and fine-annotated urban landscapes labeled on 1 m Google Earth image. This dataset encompasses 15 typical cities, offering researchers a rich and diverse resource to drive exploration, analysis, and innovation in the field of urban landscape studies.</li> </ul> <p> </p> <p><strong>Citation format for paper and dataset:</strong></p> <p>[1] Zhiyu Xu, Shuqing Zhao. Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network. <em>Sci Data</em> 11, 266 (2024). https://doi.org/10.1038/s41597-023-02844-2</p> <p>[2] Zhiyu Xu, Shuqing Zhao, Fine-grained urban landscape mapping reveals broad-scale homogeneity in urban environments,<br>Science Bulletin, (2024). https://doi.org/10.1016/j.scib.2024.03.060</p> <p>[3] Zhiyu Xu, Shuqing Zhao. UBGG-3m: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network (v1.0) [Data set]. (2023). Zenodo. https://doi.org/10.5281/zenodo.8352777</p>
Questionnaire for surveys on Urban Green Space use and survey raw data for Brussels (Belgium), Luxembourg-city (Luxembourg) and Rouen (France)
<p>The repository contains the xml files of survey questionnaires on the use of urban green spaces. All survey files are translated into three languages (English, French and German).</p> <p>At the time of this publication, these questionnaires have already been used for conducting face-to-face surveys in 2016 in Brussels (Belgium), in 2017 in Luxembourg-city (Luxembourg) and in 2017 in Rouen (France).</p> <p>The results of these surveys are provided in raw data format (csv files), after anonymisation (home and workplace locations have been removed).</p> <p>Please feel free to contact us for any supplementary info.</p>
Expanding urban green space with superblocks
<p>The street geometries are processed geometries originating from OpenStreetMap. Map data<br> copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org.</p> <p>If you use this data, make sure to cite OpenStreetMap as outlined:<br> https://wiki.openstreetmap.org/wiki/Researcher_Information.</p> <p>All data is provided as GeoJSON files.</p> <p>The block files contain the following attributes:</p> <p> Attribute Explanation<br> --------- ------------<br> b_type Classification of either super- or miniblock<br> inter_id Random id<br> area area (m2)</p>
Fig. 2 in Species Complexes Of Predatory Phytoseiid Mites (Parasitiformes, Phytoseiidae) In Green Urban Plantations Of Uman' (Ukraine)
Fig. 2. Phytoseiid mites occurrence on plants in green urban plantations of Uman': 1 — E. finlandicus, 2 — T. aceri, 3 — T. tiliarum, 4 — D. echinus, 5 — K. aberrans, 6 — P. incognitus, 7 — A. andersoni, 8 — P. soleiger, 9 — T. laurae, 10 — A. herbarius, 11 — G. longipilus, 12 — A. rademacheri.
Fig. 6 in Urban Green Areas, Recreational Use And Health Impact Of Victory Gardens (Córdoba - Spain)
Fig. 6. Daily pollen concentrations of the principal pollen types in the city of Córdoba during 2017, related to ornamental flora in the Gardens of Victory.
Fig. 2 in Urban Green Areas, Recreational Use And Health Impact Of Victory Gardens (Córdoba - Spain)
Fig. 2. Aerial photo of the Victory Gardens (Google earth) and architectural plan (own elaboration).
The impact of small-scale green infrastructure on the affective wellbeing associated with urban sites
<p>The database contains participants' reported affective perceptions of 18 images of street images with different levels of green coverage.</p>
Datasets of sap flow, meteorological, leaf gas measurements in urban green areas in Helsinki
<p>Datasets of sap flow, meteorological, leaf gas measurements used in the manuscript “Sap flow and leaf gas exchange response to drought and heatwave in urban green spaces in a Nordic city”. Data contains cleaned half-hourly sap flow data and half-hourly meteorological datasets (Tair, Tsoil, RH, soil temperature, soil moisture) at four different urban green areas in Helsinki.</p> <p>Manual measurements of leaf gas exchanges using GFS instruments. Datasets contains mainly the Amax parameters derived from curve fitting and instantaneous values of G and E at PAR 1100 W m<sup>-2</sup>.</p> <p>Folders contain:</p> <ul> <li>Leaf gas exchange data <ul> <li>Leaf_gas_data_all_v2.csv</li> <li>Metadata_leaf gas exchange data.csv</li> </ul> </li> <li>Meteo data <ul> <li>Meteo_Forest_data_30min.csv</li> <li>Meteo_Orchard_data_30min.csv</li> <li>Meteo_Park_data_30min.csv</li> <li>Meteo_Street_data_30min.csv</li> <li>Metainfo_meteo.xlsx</li> </ul> </li> <li>Sap flow data <ul> <li>Sap_Forest_30min_cleaned.csv</li> <li>Sap_Orchard_30min_cleaned.csv</li> <li>Sap_Park_30min_cleaned.csv</li> <li>Sap_Street_30min_cleaned.csv</li> <li>Metadata_info_sap.csv</li> </ul> </li> </ul>
Large positive ecological changes of small urban greening actions
<p><strong>Updated data and R codes associated with our article <em>Large positive ecological changes of small urban greening actions</em>:</strong></p> <p><strong>Ecological Solutions and Evidence</strong></p> <p><strong>Abstract</strong></p> <p>The detrimental effects of human-induced environmental change on people and other species are acutely manifested in urban environments. While urban greenspaces are known to mitigate these effects and support functionally diverse ecological communities, evidence of the ecological outcomes of urban greening remains scarce. We use a longitudinal observational design to provide empirical evidence of the ecological benefits of greening actions. We show how a small greening action quickly led to large positive changes in the richness, demographic dynamics, and network structure of a depauperate insect community. We demonstrate how large ecological benefits may be derived from investing in small greening actions and how these contribute to bring indigenous species back to greenspaces where they have become rare or locally extinct. Our findings provide crucial evidence that support best practice in greenspace design and contribute to re-invigorate policies aimed at mitigating the negative impacts of urbanisation on people and other species. </p>
Sentinel-2 urban green training dataset
<p>Training dataset for urban green land cover and land use detection for Sentinel-2 satellite images. Samples are pixel-wise labelled scenes over the city of Prague, including bigger parks and smaller vegetation patches within high-density urban areas.</p> <p> </p> <p>Contains four classes:</p> <p>* 0: Non-vegetated pixels<br> * 1: Low recreational vegetation<br> * 2: High recreational vegetation<br> * 3: Non-recreational vegetation</p>
Data for: Urban form and its impacts on air pollution and access to green space: A global analysis of 462 cities
Open the record for dataset details and reuse information.
Urban Green Area Accessibility Prioritization in Helsinki Metropolitan Area, Finland
<p>R scripts and Zonation input and output files for the research article Jalkanen, Fabritius, Vierikko, Moilanen & Toivonen (2020), “Analyzing fair access to urban green areas using multimodal accessibility measures and spatial prioritization”, <em>Applied Geography</em> (doi:10.1016/j.apgeog.2020.102320). See also the GitHub page for possible updates: <a href="https://github.com/DigitalGeographyLab/urban-green-area-accessibility-prioritization/">https://github.com/DigitalGeographyLab/urban-green-area-accessibility-prioritization/</a></p> <p><strong>R scripts</strong></p> <ul> <li> <p>01_Distance-decays_of_travel_modes.r: Code for defining distance-decay functions for travels from home to a recreational area. Functions are defined separately for different travel modes (walking, biking, public transport) and they are based on a travel survey by Helsinki Region Transport Authority (Brandt et al. 2019).</p> </li> <li> <p>02_Green_area_accessibility_layers_from_cell-specific_travel_times.r: Code for creating raster layers depicting the accessibility of green areas in the Helsinki Metropolitan Area, separately from the point of view of all the metropole’s districts. Accessibility is based on modeled travel times (Tenkanen & Toivonen 2020) and distance-decay functions (previous code).</p> </li> <li> <p>03_Green_area_buffer_analysis_for_comparison.r: Code for calculating the number of people living within 500m buffer around different green area pixels in the Helsinki Metropolitan Area.</p> </li> </ul> <p><strong>Zonation files</strong></p> <p>Each folder contains standard Zonation input and output files for different analysis versions described in the article. The .bat files that execute each Zonation run are located in the corresponding folders. The “input” subfolders include the features_list.spp and settings.dat files for each run. The “output” subfolders include all files generated and named automatically by the Zonation software. For instance, the priority rank maps shown in the article are found in these subfolders. See the Zonation manual (Moilanen et al. 2014) for details about e.g. the usage, naming, or structure of the different files.</p> <p>Zonation analysis versions are named as follows:</p> <ul> <li>walk = Analysis includes the accessibility of all green areas based on walking.</li> <li>bike = Analysis includes the accessibility of all green areas based on biking.</li> <li>pt = Analysis includes the accessibility of large forests based on public transportation.</li> <li>weights = The population-weighted version of the analysis. Here, each input raster layer (showing the accessibility of green areas from different city districts) is weighted by the population of the corresponding district.</li> </ul> <p><strong>References</strong></p> <p>Brandt E, Kantele S & Räty P (2019). Liikkumistottumukset Helsingin seudulla 2018 (Travel habits in the Helsinki region in 2018). HSL Publications 9/2019. <a href="https://www.hsl.fi/sites/default/files/hsl_julkaisu_9_2019_netti.pdf">https://www.hsl.fi/sites/default/files/hsl_julkaisu_9_2019_netti.pdf</a></p> <p>Tenkanen, H & Toivonen T (2020). Longitudinal spatial dataset on travel times and distances by different travel modes in Helsinki Region. Scientific Data 7: 1–15. <a href="https://doi.org/10.1038/s41597-020-0413-y">https://doi.org/10.1038/s41597-020-0413-y</a></p> <p>Moilanen, Pouzols FM, Meller L, Veach V, Arponen A, Leppänen J, Kujala H (2014) Zonation Version 4 user manual. C-BIG, University of Helsinki, Helsinki.</p>
Data from: A place-based participatory mapping approach for assessing cultural ecosystem services in urban green space
1. Cultural Ecosystem Services (CES) encompass a range of social, cultural and health benefits to local communities, for example recreation, spirituality, a sense of place and local identity. However, these complex and place-specific CES are often overlooked in rapid land management decisions and assessed using broad, top–down approaches. 2. We use the Toolkit for Ecosystem Service Site-based Assessment (TESSA) to examine a novel approach to rapid assessment of local CES provision using inductive, participatory methods. We combined free-listing and participatory geographic information systems (GIS) techniques to quantify and map perceptions of current CES provision of an urban green space. The results were then statistically compared with those of a proposed alternative scenario with the aim to inform future decision-making. 3. By identifying changes in the spatial hotspots of CES in our study area, we revealed a spatially-specific shift toward positive sentiment regarding several CES under the alternative state with variance across demographic and stakeholder groups. Response aggregations in areas of proposed development reveal previously unknown stakeholder preferences to local decision-makers and highlight potential trade-offs for conservation management. Free-listed responses revealed deeper insight into personal opinion and context. 4. This work serves as a useful case study on how the perceptions and opinions of local people regarding local CES could be accounted for in the future planning of an urban greenspace and how thorough analysis of CES provision is important to fully-inform local-scale conservation and planning for the mutual benefit of local communities and nature.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.
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.
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.
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.
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.