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95 results for “urban trees”
Sap-flux and associated environmental data from ash tree monitoring at four urban parks in St. Paul, Minnesota, USA, from May to November of 2023.
We measured the sap flux density of eighteen ash trees (Fraxinus spp.) of varying health and canopy conditions across four urban parks in the City of St. Paul, MN, USA in summer 2023 with a low-cost, compact data logger system we designed in-house. Although many ash trees in the city have either been killed or removed to control the spread of Emerald Ash Borer, chemical insecticide treatments are available for trees that are in early stages infestation. The trees selected for the research have all been receiving insecticide treatment for a few years, but their health and canopy conditions vary. We also have collocated temperature, soil moisture, and precipitation measurements at the same site for summer 2023.
Minneapolis-St. Paul Urban Tree Inventory
This dataset is a compilation of spatially explicit, species-specific urban tree inventories from across the seven-county Minneapolis-St. Paul (MSP) metropolitan area in Minnesota, U.S.A. The dataset was compiled to examine fine-scale patterns of tree biodiversity across MSP. Existing tree inventories were solicited from all municipalities, counties, park systems, and relevant non-profit organizations in the region for which we were able to find contact information, resulting in inventories from 35 municipalities, one county, one park system, three non-profit organizations and and two prior academic research efforts. The spatial and temporal scope of the inventories varies; for example, the inventories from some municipalities include data from a subset of only street trees at one timepoint, while other municipal inventories were continuously updated datasets with spatially comprehensive data for street trees in addition to some trees in parks and private lands. No inventory was fully comprehensive of all trees in an area. Data are assumed to have been collected between 2012-2022, although the timestamp on each data point is not explicit. Individual inventories were combined into one uniform database.
Environmental Data for Soil, Leaf, and Root samples Boston Street Trees and Massachusetts Rural and Urban Forests in Summer 2021
This dataset provides detailed environmental and tree-level data and metadata for over 850 samples collected from 91 trees across an urban-to-rural gradient in Massachusetts. The dataset captures key variables characterizing urban environmental gradients, including soil moisture, pH, temperature, and nitrogen availability. Tree-level attributes include species identification, diameter at breast height (DBH), and growth rate based on previous tree census data. Geographic coordinates and site-specific context (urban forest, rural forest, street tree, forest edge, forest interior) are included to enable spatial analyses. The microbial sequence data associated with this environmental metadata can be found in the NCBI SRA under BioProject accession number PRJNA1297772.
Residential housing segregation and urban tree canopy in 37 US Cities; data in support of Locke et al 2021 in npj Urban Sustainability
Our goal in this paper is to examine whether there are similar patterns in the distribution of tree canopy by Home Owners’ Loan Corporation (HOLC) graded neighborhoods across 37 cities. A pre-print of the paper can be found here: https://osf.io/preprints/socarxiv/97zcs This data packages contains: 1. City-specific file geodatabases with features classes of the HOLC polygons obtained from the Mapping Inequality Project https://dsl.richmond.edu/panorama/redlining/, and tables summarizing tree canopy, and in some cases other land cover classes. 2. An *.R script that replicates all of the analyses, graphs, and tables in the paper. Other double checks, exploratory, and miscellaneous outputs are created by the script too as a bonus. Everything in the paper can be done with the script; additional work outputs are also created. 3. A *.csv file containing city, the HOLC grade, and the percent tree canopy cover. This can be used to create the main findings of the paper and this flat file is provided as an alternative to running the R script to extract information from the geodatabases, combine, and analyze them. The intention is that this file is more widely accessible; the underlying information is the same. Redlining was a racially discriminatory housing policy established by the federal government’s Home Owners’ Loan Corporation (HOLC) during the 1930s. For decades, redlining limited access to homeownership and wealth creation among racial minorities, contributing to a host of adverse social outcomes, including high unemployment, poverty, and residential vacancy, that persist today. While the multigenerational socioeconomic impacts of redlining are increasingly understood, the impacts on urban environments and ecosystems remains unclear. To begin to address this gap, we investigated how the HOLC policy administered 80 years ago may relate to present-day tree canopy at the neighborhood level. Urban trees provide many ecosystem services, mitigate the urban heat island effect
A spatio-temporal dataset for ecophysiological monitoring of urban trees
<p>A dataset was produced for 117 urban trees in four monospecific tree rows in the city of Rennes, northwestern France. The trees were measured in nine 2- to 3-day measurement sessions from Apr-Sep 2021. The dataset includes (i) leaf traits (i.e., contents of pigments, water and dry matter) measured <em>in situ</em> and in the laboratory; (ii) plant area density measured <em>in situ</em> under the canopy and (iii) georeferenced data that describe the location, geometry and species of the trees. The dataset provides an original overview of dynamics of the contents of pigments, water and dry matter for four tree species grown under urban conditions. It can be used for several purposes, such as identifying trees’ responses/behaviors in relation to their urban environment or climate conditions.</p> <p>The repository comprised 3 files : </p> <ul> <li><strong>DATASET_PART1.csv</strong> : This file contains leaf trait measurements</li> <li><strong>DATASET_PART2.csv</strong> : This file contains plant area density measurements </li> <li><strong>DATASET_PART3.gpkg</strong> : This file contains two spatial vector layers: (1) <em>CROWN_EXTENT </em>that is<em> </em>a polygon layer describing tree crowns and (2) <em>TRUNK_LOCATION</em> that is a point layer describing tree location.</li> </ul> <p>More details on the study site, protocols and data can be found in the following reference:</p> <p>Théo Le Saint, Jean Nabucet, Cécile Sulmon, Julien Pellen, Karine Adeline, Laurence Hubert-Moy, A spatio-temporal dataset for ecophysiological monitoring of urban trees, Data in Brief, Volume 57, 2024, 111010, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2024.111010.</p>
Data: The Role of Urban Trees in Reducing Land Surface Temperatures in European Cities
<p>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.</p> <p>The description of the column names is provided in the readme file.</p> <p> </p>
Comparison of Urban-Rural Tree Growth Response to Climate in the Eastern U.S. for the years 1990-2014
Climate is an important driver of tree growth in forests of the eastern U.S. Urbanization can augment the growing conditions of trees in ways that could change the sensitivity of radial growth to climate stressors such as excessive heat and water stress. This dataset include tree ring chronologies (1990-2014) in the form of basal area increment for canopy oak and maples trees from paired urban and nearby rural reference forest sites in Baltimore, Maryland, New York City, New York, and Boston, Massachusetts. Also included are metrics of heat stress and water stress from 1990-2014.
Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer
As cities warm and the need for climate adaptation strategies increases, a more detailed understanding of the cooling effects of land-cover across a continuum of spatial scales will be necessary to guide management decisions. We asked how tree canopy cover and impervious surface cover interact to influence daytime and nighttime summer air temperature, and how effects vary with the spatial scale at which land-cover data are analyzed (10, 30, 60 and 90-m radii). A bicycle-mounted measurement system was used to sample air temperature every 5 m along 10 transects (about 7 km length, sampled 3-12 times each) spanning a range of impervious and tree canopy cover (0 to 100%, each) in a mid-sized city in the Upper Midwest, USA. Variability in daytime air temperature within the urban landscape averaged 3.5 degreeC (range 1.1 to 5.7 degreeC). Temperature decreased nonlinearly with increasing canopy cover, with the greatest cooling when canopy cover exceeded 40%. The magnitude of daytime cooling also increased with spatial scale, and was greatest at the size of a typical city block (60-90 m). Daytime air temperature increased linearly with increasing impervious cover, but the magnitude of warming was less than the cooling associated with increased canopy cover. Variation in nighttime air temperature averaged 2.1C (range 1.2 to 3.0 degreeC), and temperature increased with impervious surface. Effects of canopy were limited at night; thus, reduction of impervious surfaces remains critical for reducing nighttime urban heat. Results suggest strategies for managing urban land-cover patterns to enhance resilience of cities to climate warming.
Species, DBH, and height of overstory trees in a chronosequence of reforested urban sites, Lexington, KY, USA
Urban reforested areas located in Lexington, Kentucky were evaluated over the course of summer 2020. This dataset contains information on tree species, height, DBH, and snag class across twenty urban reforestation sites planted as part of the Reforest the Bluegrass program in Lexington, KY. At least three plots (and up to nine plots) were established in each site, with additional plots added if forested patches were sufficiently large. At each plot, we established a 0.008-ha (0.02-ac) circular sampling plot and recorded the species, diameter at breast-height (DBH), and total height of each tree ≥2.5 cm (1 in) DBH. DBH was measured to the nearest 0.25 cm (0.1 in) using a DBH tape, and tree height was measured to the nearest 0.1 m using a laser hypsometer (Nikon Forestry Pro II) or telescoping height pole. These data provide a critical baseline for understanding tree growth on reforested urban sites.
Species and height of understory trees and shrubs in a chronosequence of reforested urban sites, Lexington, KY USA.
This dataset contains information on soil physical and chemical characteristics across twenty urban reforestation sites planted as part of the Reforest the Bluegrass program in Lexington, KY. Urban reforested areas located in Lexington, Kentucky were evaluated over the course of summer 2020. At least three plots (and up to nine plots) were established in each site, with additional plots added if forested patches were sufficiently large. At each plot, we established a 0.002-ha (0.005-ac) circular sampling plot to survey woody understory species, respectively. Height and species of all woody plants <2.5 cm DBH and ≥30 cm tall were tallied to the nearest 0.5 cm with a telescoping height pole. These data will contribute to understanding of understory stand dynamics in developing urban forests.
Minneapolis-St. Paul, MN Canopy Temperature and Combined Urban Tree Index (CUTI), 2018-2021
This dataset includes a series of raster datasets of canopy temperature across Minneapolis-St. Paul, MN at a resolution of approximately 70 meters. There are data from five different warm, summer afternoons from 2018-2021. The canopy temperature data was derived from ECOSTRESS Land Surface Temperature and Emissivity data and high-resolution land cover data using a downscaling approach. Canopy temperature acts as a proxy of canopy health and function, with higher temperatures indicative of higher stress and lower function in the canopy. For each image, we also compute the combined urban tree index (CUTI), a metric we developed that provides an indication of relative benefits from urban canopy which arise from a combination of the amount and function/health of urban canopy (based on the canopy temperature). CUTI ranges from 0-1, with high values indicating areas likely experiencing greater canopy benefits due to greater canopy cover and better canopy health/function.
Data from: Urban trees reduce nutrient leaching to groundwater
Many urban waterways suffer from excess nitrogen (N) and phosphorus (P) feeding algal blooms, which cause lower water clarity and oxygen levels, bad odor and taste, and the loss of desirable species. Nutrient movement from land to water is likely to be influenced by urban vegetation, but there are few empirical studies addressing this. In this study, we examined whether or not urban trees can reduce nutrient leaching to groundwater, an important nutrient export pathway that has received less attention than stormwater. We characterized leaching beneath thirty-three trees of fourteen species, and seven open turfgrass areas, across three city parks in Saint Paul, Minnesota. We installed lysimeters at 60 cm depth to collect soil water approximately biweekly from July 2011 through October 2013, except during winter and drought periods, measured dissolved organic carbon (C), N, and P in soil water, and modeled water fluxes using the BROOK90 hydrologic model. We also measured soil nutrient pools (bulk C and N, KCl-extractable inorganic N, Brays-P), tree tissue nutrient concentrations (C, N, and P of green leaves, leaf litter, and roots), and canopy size parameters (leaf biomass, leaf area index) to explore correlations with nutrient leaching. Trees had similar or lower N leaching than turfgrass in 2012 but higher N leaching in 2013; trees reduced P leaching compared with turfgrass in both 2012 and 2013, with lower leaching under deciduous than evergreen trees. Scaling up our measurements to an urban subwatershed of the Mississippi River (~17,400 ha, containing roughly 1.5 million trees), we estimated that trees reduced P leaching to groundwater by 533 kg in 2012 (0.031 kg/ha or 3.1 kg/km2) and 1201 kg in 2013 (0.069 kg/ha or 6.9 kg/km2). Removing these same amounts of P using stormwater infrastructure would cost $2.2 million and $5.0 million per year (2012 and 2013 removal amounts, respectively).
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>
Рис. 1. А — рыжеухий бюΛьбюΛь на боярышнике, с. Δазо, Приморский край, 5.11.2019. Фото В. П. Шохрина; B — рыжеухий бюΛьбюΛь пьет сок кΛена приречного, с. Каймановка, Уссурийский гороΑской округ, Приморский край, 03.04.2022. Фото À. А. БеΛяева; C — рыжеухий бюΛьбюΛь кормится ягоΑами Αевичьего винограΑа пятиΛисточкового, г. ВΛаΑивосток, 02.01.2020. Фото А. П. ХоΑакова; D — рыжеухий бюΛьбюΛь, кΛ. ФореΛевый, окрестности с. ФиΛипповка, Хасанский район, Приморский край, 01.03.2018. Фото Ю. А. Àармана; E — рыжеухий бюΛьбюΛь кормится ягоΑами бархата амурского, г. ВΛа- Αивосток, 06.11.2019. Фото А. В. ВяΛкова; F — рыжеухий бюΛьбюΛь, с. Каймановка, Уссурийский гороΑской округ, Приморский край, 04.05.2022. Фото М. В. МасΛова Fig. 1. A — brown-eared bulbul on a hawthorn tree, Lazo village, Primorsky Region, 5.11.2019. Photo by V. P. Shokhrin; B — brown-eared bulbul drinks the juice of an Amur maple, Kaymanovka Village, Ussuriysky Urban District, Primorsky Region, 03.04.2022. Photo by D. A. Belyaev; C — brown-eared bulbul feeds on the berries of the Virginia creeper, Vladivostok, 02.01.2020. Photo by A. P. Khodakov; D — brown-eared bulbul. Forelevy spring, vicinity of Filippovka Village, Khasansky District, Primorsky Region, 01.03.2018. Photo by Yu. A. Darman; E — brown-eared bulbul feeds on the berries of an Amur cork tree, Vladivostok, 06.11.2019. Photo by A. V. Vyalkov; F — brown-eared bulbul. Kaymanovka Village, Ussuriysky Urban District, Primorsky Region, 04.05.2022. Photo by M. V. Maslov in An increase in the number of records of the brown-eared bulbul Microscelis amaurotis in the Russian Far East in recent years
Рис. 1. А — рыжеухий бюΛьбюΛь на боярышнике, с. Δазо, Приморский край, 5.11.2019. Фото В. П. Шохрина; B — рыжеухий бюΛьбюΛь пьет сок кΛена приречного, с. Каймановка, Уссурийский гороΑской округ, Приморский край, 03.04.2022. Фото À. А. БеΛяева; C — рыжеухий бюΛьбюΛь кормится ягоΑами Αевичьего винограΑа пятиΛисточкового, г. ВΛаΑивосток, 02.01.2020. Фото А. П. ХоΑакова; D — рыжеухий бюΛьбюΛь, кΛ. ФореΛевый, окрестности с. ФиΛипповка, Хасанский район, Приморский край, 01.03.2018. Фото Ю. А. Àармана; E — рыжеухий бюΛьбюΛь кормится ягоΑами бархата амурского, г. ВΛа- Αивосток, 06.11.2019. Фото А. В. ВяΛкова; F — рыжеухий бюΛьбюΛь, с. Каймановка, Уссурийский гороΑской округ, Приморский край, 04.05.2022. Фото М. В. МасΛова Fig. 1. A — brown-eared bulbul on a hawthorn tree, Lazo village, Primorsky Region, 5.11.2019. Photo by V. P. Shokhrin; B — brown-eared bulbul drinks the juice of an Amur maple, Kaymanovka Village, Ussuriysky Urban District, Primorsky Region, 03.04.2022. Photo by D. A. Belyaev; C — brown-eared bulbul feeds on the berries of the Virginia creeper, Vladivostok, 02.01.2020. Photo by A. P. Khodakov; D — brown-eared bulbul. Forelevy spring, vicinity of Filippovka Village, Khasansky District, Primorsky Region, 01.03.2018. Photo by Yu. A. Darman; E — brown-eared bulbul feeds on the berries of an Amur cork tree, Vladivostok, 06.11.2019. Photo by A. V. Vyalkov; F — brown-eared bulbul. Kaymanovka Village, Ussuriysky Urban District, Primorsky Region, 04.05.2022. Photo by M. V. Maslov
Data for "COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees"
<p>In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the vegetated urban canopy model BEPTree and the mesoscale weather and climate model COSMO.</p> <p>The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June-July 2015.</p> <p>The data includes:</p> <p>1. <em>datasets</em><br> Datasets of building geometries (Shapefile, WGS84), trees (GeoTiff, WGS84), Landsat 7 scene (GeoTIFF, WGS84) and imperviousness (GeoTIFF, WGS84).</p> <p>2. <em>model outputs</em><br> The processed model outputs (.npy files, generated with Python v3) are provided for all the simulations, in terms of time series at the observation sites and spatial distributions. The full 3D model outputs, 1 TB) can be provided by request by contacting the author (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>3. <em>model inputs</em><br> Input namelists for the COSMO-BEP-Tree model and initial/static conditions. The full 3D boundary conditions (60 GB) can be provided by request (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>4. <em>observations</em><br> Measurement data (.txt).</p> <p>5. <em>post-processing scripts</em><br> Jupyter (Python 3) Notebook files used to generate the figures and to analyse model results. Tested in Python 3.6.5.</p>
City's Low Hanging Fruit: Exploring Policy Alternatives for Exploiting Urban Fruit Trees
<p>City's Low Hanging fruit:<br><br>Map, Thematic analysis, Three quistionnaires, questionnaire answers<br><br>מפה, ניתוח תמטי, שלושת השאלונים, תשובות לשאלונים<br><br></p> <p>Abstract + methods - below<br>תקציר - למטה</p>
Urban Tree Canopy Cover, environmental and socioeconomic data to São Paulo city, Brazil
<p>Urban Tree Canopy Cover from 2017, elevation and roughness from 2011 and socioeconomic data from 2010, to São Paulo city, Brazil. These data are analyzed in: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ufug.2024.128497" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ufug.2024.128497</a></p>
Data from: Using Landsat time-series to investigate nearly 50 years of tree canopy cover change across an urban-rural landscape in southern Ontario
<p><strong>Paper Abstract:</strong></p> <p>Canadian urban and adjacent landscapes have been dynamic over the last 50 years due to land management, land cover alternations, climate change, and disturbances. Remote sensing, particularly the Landsat archive, provides the only means to spatially quantify these long-term dynamics locally. Here, we explore the utility of Landsat, including the often-forgotten MSS sensor, for investigating percent tree canopy cover (TCC) change between 1972 and 2020 in a Canadian urban-rural context. We build a TCC time-series by training random forest models using visually interpreted TCC from high-resolution imagery. Predictors include topographic and yearly LandsatLinkr-harmonized and LandTrendr-fitted tasseled cap indices. Yearly binary TCC maps are built to mask consistently treeless areas and limit noise. To increase confidence in observed TCC change without historical reference imagery, we investigate multiple temporal validation options. Our TCC time-series (R2: 0.89, RMSE: 10.7%), quantifies TCC dynamics while limiting erroneous change and predictor space extrapolation. We explore TCC changes across landscapes, revealing periods of gain and loss associated with agricultural reforestation (1978-1996), housing development (on-going), drought (late 1990s), emerald ash borer (2010s), an ice storm (2013), and other drivers. Results demonstrate how long-term Landsat time-series can be used to better understand historical tree canopy change at local-regional scales. </p> <p> </p> <p><strong>Dataset details:</strong></p> <p>See paper. </p> <ul> <li>cc_72to20.tif: Yearly tree CC predictions (1972-2020)</li> <li>always_nonforest10_nowater.tif: continuous-non-canopy mask</li> <li>water.tif: water mask</li> <li>Yearly.zip: Annual predictors (including CC10) and asc outputs</li> </ul> <p> </p> <p>See code on GitHub: <a href="https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020">ZZMitch/PredictTreeCC_Landsat_1972to2020: Code from the portion of my PhD about using Landsat time-series to predict tree canopy cover from 1972 - 2020. Code will be released as papers are published. (github.com)</a></p>
Urban tree pests can support biological control services in landscape shrubs
<p>Scale insects are common tree pests in urban ecosystems. Although severe scale infestations can worsen tree condition, trees can tolerate moderate scale densities. Scale insects are prey for many arthropod natural enemies that also feed on plant pests throughout urban landscapes. Because scale-infested trees support natural enemy communities, they may support biological control services on nearby plants and function analogously to banker plants in greenhouse production systems. In this study, we tested if sentinel insect prey were more likely to be removed on shrubs below scale-infested trees compared to scale-uninfested trees. We conducted several biological control experiments from 2019–2021 using fruit flies, aphids, and caterpillars in potted and planted holly shrubs below scale-infested and scale-uninfested oak trees. We found that caterpillars in potted shrubs and fruit flies in planted landscape shrubs were more likely to be removed underneath scale-infested trees compared to scale-uninfested trees. Caterpillars were also more likely to be removed from landscape <em>Ilex</em> <em>vomitoria</em> shrubs compared to <em>I</em>. <em>cornuta</em> shrubs. In all other experiments, we found no effect of scale infestation status or shrub species on prey removal. Our results suggest that scale-infested trees can support biological control services in shrubs below them but that this effect can vary depending on prey and shrub species. The natural enemy communities in urban trees and shrubs appear to be linked and tolerating tree pests can favor conservation biological control services in urban landscapes.</p>
Fig. 1 in Nest Entry Shape Change May Cause Nest Abandonment In Urban Cavity-Nesting Species: A Case Study Of The Tree Sparrow Passer Montanus
Fig. 1. Location of the study area (left) and examples of Tree Sparrow nests at the study sites (right). Site 1: Agricultural Practical Training Center, Chonnam National University,
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.