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3,709 results for “urbanization.”
Fig. 6 in Effects of urbanization on the diversity of testate amoebae (Protist, Rhizopoda) in a stream of the southwestern Amazon basin (Igarapé São Francisco in Acre state, Brazil)
Fig. 6. Biplot of the first two axes of the Redundancy Analysis of the scores of the streams according to the abiotic variables (TC = Thermotolerant Coliforms; EC = Electrical Conductivity, DO = Oxygen; pH = Hydrogen potential; TotP = Total Phosphorus; Temp = Temperature; Turb = Turbidity; NH 4 = Ammonia; NO 2 = Nitrate), the legend explains the symbols corresponding to the family names.
Fig. 3 in Effects of urbanization on the diversity of testate amoebae (Protist, Rhizopoda) in a stream of the southwestern Amazon basin (Igarapé São Francisco in Acre state, Brazil)
Fig. 3. Plot of the results of the Principal Components Analysis (PCA) of the environmental variables of the São Francisco stream in Rio Branco e Bujari, Acre (Brazil): TC = Thermotolerant Coliforms; EC = Electrical Conductivity; pH = Hydrogen potential; DO = Oxygen; TotP = Total Phosphorus; Phos = Phosphate; Temp = Temperature; Transp = Transparency; Turb = Turbidity; FR = Flow Rate; Dep = Depth; NH 4 = Ammonia; NO 3 = Nitrite; NO 2 = Nitrate.
Figure 3 in Shell size differences in Helix lucorum Linnaeus, 1758 (Mollusca: Gastropoda) between natural and urban environments
Figure 3. Error bars of mean values of SOS and AOS variables are shown with 99% confidence intervals.
Figure 4 in Shell size differences in Helix lucorum Linnaeus, 1758 (Mollusca: Gastropoda) between natural and urban environments
Figure 4. Individual scores of the Helix lucorum along the first 2 PCA axes defined by size variables and 2 ratios.
Figure 2 in Shell size differences in Helix lucorum Linnaeus, 1758 (Mollusca: Gastropoda) between natural and urban environments
Figure 2. Measurements of the shells of Helix lucorum used in the analysis: SH, shell height; SW, shell width; AH, aperture height; AW, aperture width.
Future Projections of Temperature Extremes and Urban Heat Island in Paris using Deep Learning
<p>Future projections of 2-meter maximum and minimum temperature and land surface temperature in Paris, France, using Deep Learning, under four Shared Socioeconomic Pathways. ERA5 and GCM ensemble data at their original resolution are also included. The DL (Convolutional Neural Network) model architecture and trained weights are also available. The Python script to generate the boxplots of the future projections is also included.</p>
Evaluation of high-resolution WRF simulation in urban areas - Effect of different physics schemes on simulation performance in the Rhine-Main-Neckar area
<p>This dataset contains data sampled from a WRF sensitivity study saved in NetCDF format. The study was run over 4 months of the year 2020. The folders contain the following data:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Datasets</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>wrf_met_sample_full.nc</td> <td>all</td> <td>WRF meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>wrf_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>wrf_met_sample_full_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_met_sample_full_and_quant_ucmheights.nc</td> <td>only 2020_12</td> <td>Same as wrf_met_sample_full_and_quant.nc but only for the run using the vertical layer distribution of UCM</td> </tr> <tr> <td>wrf_pblh_sample_full.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain</td> </tr> <tr> <td>wrf_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_met_sample_full.nc</td> <td>all</td> <td>ERA5 meteorology sampled at the 19 weather stations in the simulation domain</td> </tr> <tr> <td>era5_met_sample_full_and_quant.nc</td> <td>all</td> <td>As above, but resampled onto the measured meteorology and with summary statistics</td> </tr> <tr> <td>era5_pblh_sample_full_and_quant.nc</td> <td>all</td> <td>WRF PBLH (and custom PBLH_RIB) sampled at the 2 radio sonde stations in the simulation domain, resampled onto the measured meteorology and with summary statistics</td> </tr> </tbody> </table> <p>Each of these files contains the samples and statistics as NetCDF Variables. These Variables have multiple dimensions, which describe the individual datapoints. For the WRF samples, these dimensions are:</p> <table> <tbody> <tr> <td><strong>Dimension</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Time</td> <td>time since start of simulation</td> </tr> <tr> <td>station_id</td> <td>the ID of the station where the sample was taken (meteo - length 19, PBLH - length 2)</td> </tr> <tr> <td>pbl</td> <td>Planetary Boundary Layer scheme (Bou-Lac / MYJ / YSU)</td> </tr> <tr> <td>lsm</td> <td>Land Surface Model scheme (N / NMP)</td> </tr> <tr> <td>slm</td> <td>Surface Layer Model scheme (MM5 / MO)</td> </tr> <tr> <td>urb</td> <td>Urban Parametrization scheme (SLUCM / BEP)</td> </tr> </tbody> </table> <p>Not all combinations between different simulation schemes exist, so some values in the NetCDF Variables are NaNs.</p>
Replication package for Transport and urban growth in the First Industrial Revolution
<p><span>Replication package for Alvarez-Palau, E. J., Bogart, D., Satchell, A.E., Shaw-Taylor, L.</span><strong><span> </span></strong><span>‘Transport and urban growth in the First Industrial Revolution’ Economic Journal<span> </span></span></p>
Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI)
<h1>Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI)</h1> <p>The <code>Class</code> is a custom dataset class that brings together information from two distinct domains into a unified dataset. This class is designed to streamline the process of working with data from different sources and enable users to seamlessly access and analyze combined datasets.</p> <p>The Durham Urban Canopy Analysis and Enhancement Initiative (DUCAEI) is committed to utilizing the Trees & Planting Sites dataset for a comprehensive geospatial analysis of Durham's urban tree canopy. Through Python within Google Colab, our aim is to identify key locations for canopy expansion, evaluate the impact of urban development on green spaces, and deliver informed recommendations for the sustainable growth of urban tree coverage.</p> <h2><a href="https://huggingface.co/datasets/Ziyuan111/DurhamTrees#background-and-rationale" rel="nofollow"></a>Background and Rationale</h2> <p>Durham's urban tree canopy is a crucial component that contributes to environmental quality, public health, and overall city aesthetics. This canopy is under threat due to ongoing urban development and natural wear. A systematic, data-driven approach is critical for strategic planning and conservation of the urban forest to ensure its vitality for generations to come.</p> <h2><a href="https://huggingface.co/datasets/Ziyuan111/DurhamTrees#data-sources-and-methodology" rel="nofollow"></a>Data Sources and Methodology</h2> <p>These data files are from durham open.</p> <p>And for the .py file:</p> <p>The provided Python script defines a dataset class named <code>DurhamTrees</code> using the <code>datasets</code> library. This class combines information from two different domains ("class1_domain1" and "class2_domain1") and includes features from both domains.</p> <p>Trees & Planting Sites Dataset: Hosted on the Durham Open Data portal, this dataset includes location, species, size, and health of street trees, alongside designated future planting sites. Data Source: Durham Trees & Planting Sites Dataset <a href="https://live-durhamnc.opendata.arcgis.com/datasets/DurhamNC::trees-planting-sites/about" rel="nofollow">https://live-durhamnc.opendata.arcgis.com/datasets/DurhamNC::trees-planting-sites/about</a></p> <p> </p>
Global urban tree LAI/SAI dataset for urban climate modeling
<p>This dataset is the first global urban tree LAI/SAI product at a 500-meter resolution, specifically designed for urban climate modeling to simulate the tree's effects in urban environments. It covers the period from 2000 to 2022 and was developed by using a reprocessed MODIS LAI product with a Random Forest model, demonstrating high accuracy.</p> <p>The original product is a netCDF4 file that has been compressed into three tar.gz files: global_15s.tar.gz, global_0.05.tar.gz, and global_0.5.tar.gz, with resolutions of 500 m, 0.05°, and 0.5°, respectively. Each netCDF4 file in the compressed archive is named Global_UrbanTree_LAI_XX_YYYY.nc, where XX represents the resolution and YYYY represents the year. Each file contains monthly LAI/SAI data for that year, with data dimensions of mon x lat x lon.</p> <p>For version 3 of the LAI data, we replaced the meteorological data from WorldClim v2 with WorldClim v2.1 during model training. This version of the dataset has undergone peer review.</p>
SINGA:PURA (SINGApore: Polyphonic URban Audio)
<p><strong>SINGA:PURA Dataset (v1.0a)</strong></p> <p>This repository contains the strongly-labelled subset of recordings of the SINGA:PURA (SINGApore: Polyphonic URban Audio) dataset and corresponding metadata, formatted in a manner compatible with a <a href="https://github.com/soundata/soundata">soundata</a> dataset loader.</p> <p>Please note that this repository does not contain the unlabelled recordings of the SINGA:PURA dataset! If you wish to access the unlabelled recordings, please refer to <a href="https://doi.org/10.21979/N9/Y8UQ6F">https://doi.org/10.21979/N9/Y8UQ6F</a> for the full version (v1.0) of the SINGA:PURA dataset (which contains both the strongly-labelled and unlabelled recordings).</p> <p><strong>Regarding this repository</strong></p> <p>The SINGA:PURA dataset is a polyphonic urban sound dataset with spatiotemporal context that contains 6547 strongly-labelled and 72406 unlabelled recordings from a wireless acoustic sensor network deployed in Singapore to identify and mitigate noise sources in Singapore. However, this repository only contains the subset of 6547 strongly-labelled recordings from the SINGA:PURA dataset and their corresponding labels, formatted in a manner compatible with a soundata dataset loader. The recordings are all 10 seconds in length, and may have 1 or 7 channels, depending on the recording device used to record them.</p> <p>The readme file in this repository ("Readme.md") contains the same information as this description: a short description on the organisation of this repository, as well our label taxonomy and the dataset itself. For full details regarding the sensor units used, the recording conditions, and annotation methodology, please refer to our conference paper below:</p> <blockquote> <p>K. Ooi, K. N. Watcharasupat, S. Peksi, F. A. Karnapi, Z.-T. Ong, D. Chua, H.-W. Leow, L.-L. Kwok, X.-L. Ng, Z.-A. Loh, W.-S. Gan, "A Strongly-Labelled Polyphonic Dataset of Urban Sounds with Spatiotemporal Context," in 13th Asia Pacific Signal and Information Processing Association Annual Summit and Conference, 2021.</p> </blockquote> <p>The conference paper has also been included in this repository as "APSIPA.pdf".</p> <p><strong>Directory structure</strong></p> <p>This repository contains a total of 9 files. 5 of the files ("labelled.zip", "labelled.z01", "labelled.z02", "labelled.z03", "labelled.z04") form a multi-part ZIP archive that, when extracted, contain the subset of 6547 strongly-labelled recordings (in FLAC format) in the SINGA:PURA dataset organised in folders by date of recording. The other 4 files are:</p> <ul> <li>"APSIPA.pdf": A PDF copy of the conference paper describing the dataset, recording and annotation methodology in detail.</li> <li>"labelled_metadata_public.csv": A CSV file containing the metadata for the 6547 strongly-labelled recordings. Each row corresponds to a single recording. See the section titled "<strong>Metadata CSV file</strong>" for more information.</li> <li>"labels_public.zip": A ZIP archive that, when extracted, contains 6547 CSV files that each contain the strong labels for their corresponding strongly-labelled recording. The names of the CSV files are identical to the names of the corresponding FLAC files containing the recordings, save for the file extension. Each row corresponds to a single acoustic event. See "<strong>Labels CSV files</strong>" for more information.</li> <li>"Readme.md": The readme file for this repository.</li> </ul> <p>Each numbered part of the multi-part ZIP archive is 1000 MB in size, which makes the dataset in its entirety about 5 GB in size. Please ensure that your connection has sufficient bandwidth to support the download, and it may also be useful to use a download manager for downloading the individual files of the dataset. To extract the multi-part ZIP archive, it may be helpful to use either <a href="https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwiZ3_e5iOPyAhXhFLcAHVyADDkQFnoECAYQAw&url=https%3A%2F%2Fwww.win-rar.com%2F&usg=AOvVaw2dEhBSHC7SNBzJdfDN-Ys2">WinRAR</a> or <a href="https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwj24YnBiOPyAhUvgtgFHR3bCW0QFnoECAcQAw&url=https%3A%2F%2Fwww.winzip.com%2Fen%2F&usg=AOvVaw2F_wLlbmAFFBFt1dPpSoSH">WinZip</a>.</p> <p>After extraction, the directory structure of this repository should be as follows:</p> <pre><code class="language-markdown">. ├─ labelled │ ├─ 2020-08-03 │ │ └─ [b827eb7d576e][2020-08-03T23-32-11Z][manual][---][565a40f866f3d2804332ca7896a4c77d][93.29-86.29 66.65]!-90.flac │ │ │ ├─ 2020-08-17 │ │ └─ <.flac files> │ │ │ ├─ ... │ │ │ └─ 2020-10-31 │ └─ <.flac files> │ ├─ labels_public │ ├─ [b827eb0a63c9][2020-08-20T11-29-04Z][manual][---][de313d12d7f31937615be80cc47a1ad9][]-53.csv │ ├─ [b827eb0a63c9][2020-08-20T11-30-04Z][manual][---][de313d12d7f31937615be80cc47a1ad9][]-54.csv │ ├─ ... │ └─ [b827ebf3744c][2020-09-02T06-53-04Z][manual][---][4edbade2d41d5f80e324ee4f10d401c0][]-1647.csv │ ├─ APSIPA.pdf ├─ labelled_metadata_public.csv └─ Readme.md</code></pre> <p><strong>Label taxonomy</strong></p> <p>Our label taxonomy is derived from the taxonomy used in the <a href="https://zenodo.org/record/3966543#.YTJBYN8RXZQ">SONYC-UST datasets</a>, but has been adapted to fit the local (Singapore) context while retaining compatibility with the SONYC-UST ontonology. We chose this taxonomy to allow the SINGA:PURA dataset to be used in conjunction with the SONYC-UST datasets when training urban sound tagging models by simply omitting the labels that are absent in the SONYC-UST taxonomy from the recordings in the SINGA:PURA dataset. For more information regarding the SONYC-UST datasets, please refer to the following paper published by the SONYC team:</p> <blockquote> <p>M. Cartwright, J. Cramer, A. E. M. Mendez, Y. Wang, H. Wu, V. Lostanlen, M. Fuentes, G. Dove, C. Mydlarz, J. Salamon, O. Nov, J. P. Bello, "SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context," in Proceedings of the Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020.</p> </blockquote> <p>Specifically, our label taxonomy consists of 14 coarse-grained classes and 40 fine-grained classes. Their organisation is as follows:</p> <pre><code class="language-markdown">─┬─ 1. Engine ───────────────┬─ 1. Small engine │ ├─ 2. Medium engine │ └─ 3. Large engine ├─ 2. Machinery impact ─────┬─ 1. Rock drill │ ├─ 2. Jackhammer │ ├─ 3. Hoe ram │ └─ 4. Pile driver ├─ 3. Non-machinery impact ─┬─ 1. Glass breaking* │ ├─ 2. Car crash* │ └─ 3. Explosion* ├─ 4. Powered saw ──────────┬─ 1. Chainsaw │ ├─ 2. Small/medium rotating saw │ └─ 3. Large rotating saw ├─ 5. Alert signal ─────────┬─ 1. Car horn │ ├─ 2. Car alarm │ ├─ 3. Siren │ └─ 4. Reverse beeper ├─ 6. Music ────────────────┬─ 1. Stationary music │ └─ 2. Mobile music ├─ 7. Human voice ──────────┬─ 1. Talking │ ├─ 2. Shouting │ ├─ 3. Large crowd │ ├─ 4. Amplified speech │ └─ 5. Singing* ├─ 8. Human movement* ──────┬─ 1. Footsteps* │ └─ 2. Clapping* ├─ 9. Animal* ──────────────┬─ 1. Dog barking │ ├─ 2. Bird chirping* │ └─ 3. Insect chirping* ├─ 10. Water* ──────────────── 1. Hose pump* ├─ 11. Weather* ────────────┬─ 1. Rain* │ ├─ 2. Thunder* │ └─ 3. Wind* ├─ 12. Brake* ──────────────┬─ 1. Friction brake* │ └─ 2. Exhaust brake* ├─ 13. Train* ──────────────── 1. Electric train* └─ 0. Others* ──────────────┬─ 1. Screeching* ├─ 2. Plastic crinkling* ├─ 3. Cleaning* └─ 4. Gear*</code></pre> <p>Classes marked with an asterisk (*) are present in the SINGA:PURA taxonomy but not the SONYC taxonomy. The "Ice cream truck" class from the SONYC taxonomy has been excluded from the SINGA:PURA taxonomy because this class does not exist in the local context.</p> <p>In addition, note that the label for the coarse-grained class "Others" in this repository is "0", which is different from the label "X" that is used in the full version of the SINGA:PURA dataset.</p> <p><strong>Metadata CSV file</strong></p> <p>Each row of "labelled_metadata_public.csv" corresponds to a single recording and contains the following fields:</p> <ul> <li> "sensor_id": A string representing the identity of the sensor that the recording was taken from. Each sensor node has a unique identity. In other words, if and only if the "sensor_id" strings for two files are different, then the recordings were taken from different sensors.</li> <li> "filename": The name of the raw audio file corresponding to this row of metadata. Note that there is actually a timestamp on the filename already --- this timestamp corresponds to the UTC+0 time zone and <em>not</em> the SGT (equivalent to UTC+8) time zone. However, the other metadata fields ("day", "hour", etc.) will correspond to the SGT time zone (specifically, the time zone in the "timezone" column), because the sensor nodes were physically located in that time zone.</li> <li>"year": The year that the recording was made.</li> <li>"month": The month of the year that the recording was made.</li> <li>"date": The date of the month that the recording was made.</li> <li>"day": The day of the week that the recording was made (0 = Sunday, 1 = Monday, ..., 6 = Saturday).</li> <li>"hour": The hour of the day that the recording was made, in 24-hour format.</li> <li>"minute": The minute of the hour that the recording was made.</li> <li>"second": The second of the minute that the recording was made.</li> <li>"timezone": The timezone corresponding to the temporal data in the fields of the CSV file. As of the current version, this value should be "SGT" (Singapore time zone, corresponding to UTC+8) for all recordings.</li> <li>"town": The town in which the sensor is located in (either "East 1", "East 2", "West 1", or "West 2").</li> </ul> <p><strong>Labels CSV files</strong></p> <p>Each row of every CSV file in the "labels_public" folder corresponds to a single acoustic event and contains the following fields:</p> <ul> <li>"annotator": A number in the set {1,2,3,4,5} denoting the annotator index. Each index corresponds to a unique annotator.</li> <li>"filename": The name of the raw audio file (i.e. recording) that the annotator heard the event in. This is identical to the name of the CSV file, save for the file extension.</li> <li>"event_label": The label of the event according to the taxonomy described in the "<strong>Label taxonomy</strong>" section, given in the format "<coarse label>-<fine label>". For example, if the event was a siren, then this would be "5-3". In addition, if (A) none of the fine-grained classes applied to the event, but a numbered coarse-grained class (i.e. all coarse-grained classes except "0. Others") did, OR (B) the annotator was not sure which fine-grained class the event belonged to, although they were sure of which coarse-grained class it belonged to, then it was assigned the fine-grained label "0". For example, a non-machinery impact that was not glass breaking, a car crash, or an explosion would be given the label "3-0". In addition, if <em>no</em> events corresponding to coarse- or fine-grained classes in the label taxonomy were heard for a given recording, then there would be a single row in that recording with the value "0-0" in this field.</li> <li>"proximity": One of "near", "far", or "moving", corresponding to what the annotator believed was the proximity of the sound event to the sensor based on the recording. If "near" or "far", then the source is assumed to be stationary. Hence, any instances of class "6-2" are always labelled as "moving" for proximity and any instances of class "6-1" are always labelled as "near" or "far" for proximity. For events with event label "0-0", the value in this field is "NIL".</li> <li> "onset": The starting time of the event in the audio file, given in seconds and to a precision of 3 decimal places. If the event starts from the very beginning of the track, the onset is "0.000". For events with event label "0-0", the value in this field is "0.000".</li> <li>"offset": The ending time of the event in the audio file, given in seconds and to a precision of 3 decimal places. If the event lasts to the very end of the track, the offset is "10.000". For events with event label "0-0", the value in this field is "10.000".</li> <li>"remarks": Any remarks made by the annotator regarding the track of the particular label for the event. This can be left blank (i.e. as an empty string) if there are no remarks for that particular label.</li> </ul> <p>Note that since there can be any number of sound events (including zero) in a given recording, it is possible that there may be <em>multiple</em> rows in a single CSV file. In addition, every recording has labels provided by at least one annotator, and some have labels provided by more than one annotator. Labels for the same recording provided by different annotators are found in the same CSV file. Lastly, some events in the taxonomy are rare enough that they do not occur in the strongly-labelled portion of the dataset, so not all possible event labels are represented in the CSV files in the "labels_public" folder.</p> <p><strong>License and attribution</strong></p> <p>This dataset is licensed under the Creative Commons Attribution-ShareAlike 4.0 International license (a human-readable summary is available at <a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a> and the legal document for the license is available at <a href="https://creativecommons.org/licenses/by-sa/4.0/legalcode">https://creativecommons.org/licenses/by-sa/4.0/legalcode</a>).</p> <p>When attributing the dataset, please acknowledge Kenneth Ooi, Karn Watcharasupat, Santi Peksi, Furi Andi Karnapi, Zhen-Ting Ong, Danny Chua, Hui-Wen Leow, Li-Long Kwok, Xin-Lei Ng, Zhen-Ann Loh, and Woon-Seng Gan. Alternatively, if you are using the dataset in an academic publication, you may want to cite our conference paper instead:</p> <blockquote> <p>K. Ooi, K. N. Watcharasupat, S. Peksi, F. A. Karnapi, Z.-T. Ong, D. Chua, H.-W. Leow, L.-L. Kwok, X.-L. Ng, Z.-A. Loh, W.-S. Gan, "A Strongly-Labelled Polyphonic Dataset of Urban Sounds with Spatiotemporal Context," in 13th Asia Pacific Signal and Information Processing Association Annual Summit and Conference, 2021.</p> </blockquote> <p><strong>Contact</strong></p> <p>Please feel free to drop an email to Kenneth Ooi at <a href="mailto:wooi002@e.ntu.edu.sg">wooi002@e.ntu.edu.sg</a> for questions and issues regarding the dataset.</p> <p><strong>Version history</strong></p> <p>v1.0a: Initial upload of dataset (6547 labelled)</p>
Formalizing Objectives and Criteria for Urban Agriculture Sustainability with a Participatory Approach
<p>The last few years have seen an exponential development of urban agriculture projects within global North countries, especially professional intra-urban farms which are professional forms of agriculture located within densely settled areas of city. Such projects aim to cope with the challenge of sustainable urban development and today the sustainability of the projects is questioned. To date, no set of criteria has been designed to specifically assess the environmental, social and economic sustainability of these farms at the farm scale. Our study aims to identify sustainability objectives and criteria applicable to professional intra-urban farms. It relies on a participatory approach involving various stakeholders of the French urban agriculture sector comprising an initial focus group, online surveys and interviews. We obtained a set of six objectives related to environmental impacts, link to the city, economic and ethical meaning, food and environmental education, consumer/producer connection and socio-territorial services. In addition, 21 criteria split between agro-environmental, socio-territorial and economic dimensions were identified to reach these objectives. Overall, agro-environmental and socio-territorial criteria were assessed as more important than economic criteria, whereas food production was not mentioned. Differences were identified between urban farmers and decision makers, highlighting that decision makers were more focused on projects' external sustainability. They also pay attention to the urban farmer agricultural background, suggesting that they rely on urban farmers to ensure the internal sustainability of the farm. Based on our results, indicators could be designed to measure the sustainability criteria identified, and to allow the sustainability assessment of intra-urban farms.</p>
Data for "Do electric vehicles mitigate urban heat? The case of a tropical city"
<p>This dataset contains the underlying data used in the publication "Do electric vehicles mitigate urban heat? The case of a tropical city", which is under review in <em>Front. Environ. Sci. .</em></p> <p>The dataset includes two folders:</p> <p>1. <strong>data</strong> <br> Include COSMO-DCEP-BEP model inputs and output needed to reproduce the results in the manuscript (NetCDF). </p> <p>2. <strong>script</strong><br> Include post-processing scripts used to generate the figures in the manuscript (Jupiter Python 3 Notebook).</p> <p><em> </em></p>
Dataset containing DTS-data used in Karttunen et al. "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland"
<p>This record contains DTS-data used in the following study:</p> <p>Karttunen et al. (2021): Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland, submitted to AMTD</p> <p> </p> <p>DTS_highfreq_SMEARIII_Karttunen_et_al.zip contains continuous high frequency potential temperature profiles measured along the SMEAR III 31-metre tall mast. See more information on the data in the netCDF-file attributes and on the measurement setup in the related manuscript.</p> <p>DTS_statistics_SMEARIII_Karttunen_et_al.nc contains profiles for the turbulence temperature statistics calculated from the continuous DTS potential temperature profiles.See more information in the netCDF-file attributes and the related manuscript.</p> <p> </p>
Turner et al., 2021, urban water supply contributions and GAMUT output data
<p>Output from gamut (Geospatial Analytics for Multisectoral Urban Teleconnections) model supporting Turner et al. (2021) - https://www.nature.com/articles/s41467-021-27509-9</p> <p> </p> <p> </p>
Correlation of urban avian species diversity present in heterogenous habitat types of the Silk city, Odisha, Eastern India
<p>This is the complete metadata and the R code required to do the analysis of the paper regarding birds of Berhampur city.</p>
Saudi Arabian Capitals Urban Land Cover Maps: 1985-2019
<p>A CCDC algorithm was used to produce 13 sets of geographical maps. Each set represents one capital city of Saudi Arabia: Riyadh (which also serves as the country’s capital), Buridah, Ha’il, Dammam, Makkah, Madinah, Arar, Skakah Tabuk, Albaha, Abha, Jazan, and Najran. Each of the 13 datasets contains 35 annual maps from 1985 to 2019. The file format of these datasets is the .hdr file. The dataset is free to download. </p> <p>In the case of using the urban land cover maps, please cites the website and the paper:</p> <ol> <li>Aljaddani AH, Song X-P, Zhu Z. Characterizing the Patterns and Trends of Urban Growth in Saudi Arabia’s 13 Capital Cities Using a Landsat Time Series. <em>Remote Sensing</em>. 2022; 14(10):2382. https://doi.org/10.3390/rs14102382</li> <li>Aljaddani, Amal H, Song, Xiao-Peng, & Zhu, Zhe. (2022). Saudi Arabian Capitals Urban Land Cover Maps: 1985-2019 (Version: 1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6210073</li> </ol> <p> </p> <p> </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>
The urban boundaries of 196 cities in China for deriving urban precipitation intensity-duration-frequency curves
<p>The shapefiles of urban boundaries of 196 cities in China are generated by processing and filtering the outputs of Li et al., (2018). Mapping global urban boundaries from the global artificial impervious area (GAIA) data. </p>
ScienceDex guides
Understand access before you commit
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.