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61 results for “trait database”
AusTraits: a curated plant trait database for the Australian flora
<p>AusTraits is a transformative database, containing measurements on the traits of Australia's plant taxa, standardised from hundreds of disconnected primary sources. So far, data have been assembled from > 300 distinct sources, describing > 500 plant traits and > 34,000 taxa.</p> <p>To handle the harmonising of diverse data sources, we use a reproducible workflow to implement the various changes required for each source to reformat it suitable for incorporation in AusTraits. Such changes include restructuring datasets, renaming variables, changing variable units, changing taxon names. While this repository contains the harmonised data, the raw data and code used to build the resource are also available on the project's GitHub repository, <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Further information on the project is available at the project website <a href="https://austraits.org">austraits.org</a> and in the associated publication (see below).</p> <p><strong>CONTRIBUTORS</strong></p> <p>The project is jointly led by Dr Daniel Falster (UNSW Sydney), Dr Rachael Gallagher (Western Sydney University), Dr Elizabeth Wenk (UNSW Sydney), and Dr Hervé Sauquet (Royal Botanic Gardens and Domain Trust Sydney), with input from > 300 contributors from over > 100 institutions (see full list above). The project was initiated by Dr Rachael Gallagher and Prof Ian Wright while at Macquarie University.</p> <p>We are grateful to the following institutions for contributing data Australian National Botanic Garden, Brisbane Rainforest Action and Information Network, Kew Botanic Gardens, National Herbarium of NSW, Northern Territory Herbarium, Queensland Herbarium, Western Australian Herbarium, South Australian Herbarium, State Herbarium of South Australia, Tasmanian Herbarium, Department of Environment Land Water and Planning Victoria and the Royal Botanic Gardens Victoria.</p> <p>AusTraits has been supported by investment from the Australian Research Data Commons (ARDC), via their "Transformative data collections" (https://doi.org/10.47486/TD044) and "Data Partnerships" (https://doi.org/10.47486/DP720, https://doi.org/10.47486/DP720A) programs; and grants from the Australian Research Council (FT160100113, DE170100208, FT100100910) and Macquarie University, The ARDC is enabled by National Collaborative Research Investment Strategy (NCRIS).</p> <p><strong>ACCESSING AND USE OF DATA</strong></p> <p>The compiled AusTraits database is released under an open source licence (CC-BY), enabling re-use by the community.</p> <p>A requirement of use is that users cite the AusTraits resource paper, which includes all contributors as co-authors:</p> <blockquote> <p>Falster, Gallagher et al (2021) <em>AusTraits, a curated plant trait database for the Australian flora</em>. Scientific Data 8: 254, <a href="https://doi.org/10.1038/s41597-021-01006-6">https://doi.org/10.1038/s41597-021-01006-6</a></p> </blockquote> <p>In addition, we encourage users you to cite the original data sources, wherever possible.</p> <p>Note that under the license data may be redistributed, provided the attribution is maintained.</p> <p>The downloads below provide the data in two formats:</p> <ul> <li>austraits-X.X.X.zip: data in plain text format (.csv, .bib, .yml files). Suitable for anyone, including those using Python.</li> <li>austraits-X.X.X.rds: data as compressed R object. Suitable for users of R (see below).</li> <li> <div>austraits-X.X.X-flattened.rds: contains a flattened version of the dataset for direct loading in R; all data tables are joined into a wider format</div> </li> <li> <div>austraits-X.X.X-flattened.parquet: contains a flattened version of the dataset in parquet format; all data tables are joined into a wider format </div> </li> </ul> <p>For R users, access and manipulation of data is assisted with the <a href="http://github.com/traitecoevo/austraits">austraits R package</a>. The package can both download data and provides examples and functions for running queries.<br><br><strong>STRUCTURE OF AUSTRAITS</strong></p> <p>The compiled AusTraits database contains a series of relational tables and files. These elements include all the data, contextual information submitted with each contributed datasets, database schema, and trait definitions. The file dictionary.html provides the same information in textual format. Similar information is available at <a href="https://traitecoevo.github.io/traits.build-book/">https://traitecoevo.github.io/traits.build-book/</a>.</p> <p><strong>CONTRIBUTING</strong></p> <p>We envision AusTraits as an on-going collaborative community resource that:</p> <ol> <li>Increases our collective understanding the Australian flora;</li> <li>Facilitates accumulation and sharing of trait data;</li> <li>Builds a sense of community among contributors and users; and</li> <li>Aspires to fully transparent and reproducible research of the highest standard.</li> </ol> <p>As a community resource, we are very keen for people to contribute. Assembly of the database is managed on GitHub at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p> <p>Here are some of the ways you can contribute:</p> <p><strong>Reporting Errors</strong>: If you notice a possible error in AusTraits, please <a href="https://github.com/traitecoevo/austraits.build/issues">post an issue on GitHub</a>.</p> <p><strong>Refining documentation:</strong> We welcome additions and edits that make using the existing data or adding new data easier for the community.</p> <p><strong>Contributing new data</strong>: We gladly accept new data contributions to AusTraits. See full instructions on how to contribute at <a href="https://github.com/traitecoevo/austraits.build/">https://github.com/traitecoevo/austraits.build/</a>.</p>
Zooplankton functional trait database for Canadian lakes
<p>This dataset contains functional traits of 102 crustacean zooplankton taxa sampled in 624 Canadian lakes (Pelagic sample), as well as 58 sub-fossil cladoceran taxa (Sediments sample) sampled in 101 lakes. Lakes were sampled across Canada as part of the NSERC Canadian LakePulse Network. The traits used are: feeding type (B(<em>Bosmina</em>)-filtration, C(<em>Chydorus</em>)-filtration, D(<em>Daphia</em>)-filtration, S(<em>Sidae</em>)-filtration, stationary suspension or raptorial), habitat (littoral, pelagic or intermediate) and trophic group (carnivore, herbivore, omnivore, or a combination of these). Pelagic species length of up to 10 individuals per taxon per lake were measured by BSA Environmental Services (Ohio, U.S.A.), and averaged for each taxon. Sediments sample lengths were either obtained from the literature (Demott & Kerfoot, 1982; Barnett et al., 2007; Griffiths et al., 2019), or from the Pelagic sample length data. Feeding type, habitat and trophic group functional traits were obtained from literature (Demott & Kerfoot, 1982; Barnett et al., 2007; Hébert et al., 2016; Griffiths et al., 2019).</p> <p>References</p> <p>Barnett, A. J., Finlay, K., & Beisner, B. (2007). Functional diversity of crustacean zooplankton communities: Towards a trait-based classification. <em>Freshwater Biology</em>, <em>52</em>(5), 796–813. https://doi.org/10.1111/j.1365-2427.2007.01733.x</p> <p>Demott, W. R., & Kerfoot, W. C. (1982). Competition among cladocerans: nature of the interaction between Bosmina and Daphnia. <em>Ecology</em>, <em>63</em>(6), 1949–1966. https://doi.org/10.2307/1940132</p> <p>Griffiths, K., Winegardner, A. K., Beisner, B. E., & Gregory-Eaves, I. (2019). Cladoceran assemblage changes across the Eastern United States as recorded in the sediments from the 2007 National Lakes Assessment, USA. <em>Ecological Indicators</em>, <em>96</em>, 368–382. 061</p> <p>Hébert, M.-P., Beisner, B. E., & Maranger, R. (2016). A compilation of quantitative functional traits for marine and freshwater crustacean zooplankton. Ecology. https://doi.org/10.1890/15-1275</p>
Functional traits database for North American birds
<p>Estimation of functional diversity in biological communities requires extensive and complete data on numerous functional traits of species or even individuals. When estimating functional diversity at large scales, this fact possesses an issue that may be hard to overcome: for many species, there might not be sufficient data on their functional traits. In such cases, even if there is missing information on functional trait value for one species in a community, this makes the trait impossible to use for the estimation of the functional diversity of a community. On the other hand, there are available datasets on the functional traits of all extant species within certain lineages across the world, but such datasets are often limited to very few functional traits, missing some dimensions of species' ecological niches. In this dataset, I compiled the available data from various sources that describe 23 functional traits of 703 bird species that occur in Canada, the United States, and Mexico. These functional traits include the following: diet type, diurnal and nocturnal feeding, diet items, feeding methods, feeding substrate, nest type, nest substrates, breeding system, chick development at hatching, nest aggregation, clutch size, first breeding age, number of clutches a year, breeding success, adult annual survival, mean biomass, maximum lifespan, hand-wing index, kleptoparasitism, nest parasitism, and the extent of dependency on other species for building a nest.</p>
Frugivoria: A trait database for birds and mammals exhibiting frugivory across contiguous Neotropical moist forests
Biodiversity in many areas is rapidly shifting and declining as a consequence of global change. As such, there is an urgent need for new tools and strategies to help identify, monitor, and conserve biodiversity hotspots. One way to identify these areas is by quantifying functional diversity, which measures the unique roles of species within a community and is valuable for conservation because of its relationship with ecosystem functioning. Unfortunately, the trait information required to evaluate functional diversity is often lacking and is difficult to harmonize across disparate data sources. Biodiversity hotspots are particularly lacking in this information. To address this knowledge gap, we compiled Frugivoria, a trait database containing dietary, life-history, morphological, and geographic traits, for mammals and birds exhibiting frugivory, which are important for seed dispersal, an essential ecosystem service. Accompanying Frugivoria is an open workflow that harmonizes trait and taxonomic data from disparate sources and enables users to analyze traits in space. This version of Frugivoria contains mammal and bird species found in contiguous moist montane forests and adjacent moist lowland forests of Central and South America– the latter specifically focusing on the Andean states. In total, Frugivoria includes 45,216 unique trait values, including new values and harmonized values from existing databases. Frugivoria adds 23,707 new trait values (8,709 for mammals and 14,999 for birds) for a total of 1,733 bird and mammal species. These traits include diet breadth, habitat breadth, habitat specialization, body size, sexual dimorphism, and range-based geographic traits including range size, average annual mean temperature and precipitation, and metrics of human impact calculated over the range. Frugivoria fills gaps in trait categories from other databases such as diet category, home range size, generation time, and longevity, and extends certain traits, once only a
A global database of bird nest traits
<p>The reproductive success of birds is closely tied to the characteristics of their nests. It is crucial to understand the distribution of nest traits across phylogenetic and geographic dimensions to gain insight into bird evolution and adaptation. Despite the extensive historical documentation on breeding behavior, a structured dataset describing bird nest characteristics has been lacking. To address this gap, we have compiled a comprehensive dataset that characterizes three ecologically and evolutionarily significant nest traits—site, structure, and attachment—for 9,248 bird species, representing all 36 orders and 241 out of the 244 families. By defining seven sites, seven structures, and four attachment types, we have systematically classified the nests of each species using information from text descriptions, photos, and videos sourced from online databases and literature. This nest traits dataset serves as a valuable addition to the existing body of morphological and ecological trait data for bird species, providing a useful resource for a wide range of avian macroecological and macroevolutionary research.</p>
Ancient Reef Traits Database
<p>This is the first official release of the Ancient Reef Traits Database. </p> <p>Data descriptor can be found [Here].</p> <p>The complete list of data sources can be accessed <a href="https://osf.io/f3u9k/">here</a>.</p>
Sharkipedia: A Curated Open Access Database of Shark and Ray Life History Traits and Abundance Time-series
<p>This dataset represent the intial launch of Sharkipedia: a curated open access database of shark and ray life history traits and abundance time-series. A curated database of shark and ray biological data is increasingly necessary both to support fisheries management and conservation efforts, and to test the generality of hypotheses of vertebrate macroecology and macroevolution. Sharks and rays are one of the most charismatic, evolutionary distinct, and threatened lineages of vertebrates, comprising around 1,250 species. To accelerate shark and ray conservation and science, we developed Sharkipedia as a curated open-source database and research initiative to make all published biological traits and population trends accessible to everyone. Sharkipedia hosts information on 58 life history traits from 264 sources, for 170 species, from 39 families, and 12 orders related to length (n=9 traits), age (8), growth (12), reproduction (19), demography (5), and allometric relationships (5), as well as 871 population time-series from 202 species. Sharkipedia relies on the backbone taxonomy of the IUCN Red List and the bibliography of Shark-References. Sharkipedia has profound potential to support the rapidly growing data demands of fisheries management, international trade regulation as well as anchoring vertebrate macroecology and macroevolution.</p>
Frugivoria: an open trait database of birds and mammals exhibiting frugivory in moist montane Neotropical forest
Biodiversity in many areas is rapidly shifting and declining as a consequence of global change. As such, there is an urgent need for new tools and strategies to help identify, monitor, and conserve biodiversity hotspots. One way to identify these areas is by quantifying functional diversity, which measures the unique roles of species within a community and is valuable for conservation because of its relationship with ecosystem functioning. Unfortunately, the functional trait information required to evaluate functional diversity is often lacking and is difficult to harmonize across disparate data sources. Biodiversity hotspots are particularly lacking in this information. To address this knowledge gap, we compiled Frugivoria, a trait database containing dietary, life-history, and morphological traits as well as IUCN conservation status, for mammals and birds exhibiting frugivory, which are important for seed dispersal, an essential ecosystem service. Accompanying Frugivoria is an open workflow that harmonizes trait and taxonomic data from disparate sources and enables users to analyze traits in space. This version of Frugivoria encompasses species in moist montane forests of Central and South America. Compared with existing trait databases, Frugivoria adds 25 species (reclassifying 199 to align with the most recent taxonomic changes), adds new traits such as observed and inferred range size, habitat specialization, and body size, and also fills gaps in trait categories from other databases such as diet category, home range size, generation time, and longevity. Overall, Frugivoria adds 2,045 new trait values for mammals and 4,022 for birds, and includes a total of 17,454 trait entries with minimums and maximums reported for certain traits. Frugivoria and its workflow enables researchers to quantify relationships between traits and the environment, as well as spatial trends in functional diversity, contributing to basic knowledge and applied conservation of frugivores
SEV-LTER Plant Traits Database
This dataset contains measurements of morphological (leaf, stem, root, and seed), nutrient, and isotopic traits for plant species growing in the Sevilleta National Wildlife Refuge. Approximately 104 species were sampled in or near four core sites of the SEV-LTER (core_blue, core_black, core_creosote, and core_PJ) plus the Sevilleta Field Station between 2017 and 2021. In addition, seed masses were measured from a 2016-era seed collection provided by Jenny Noble and added to the dataset; for these, site = NA.
The FloRes Database: A floral resources trait database for pollinator habitat-assessment generated by a multistep workflow
<p><strong>Background</strong></p> <p>The decline of pollinating insects in agricultural landscapes proceeds due to intensive land use and the associated loss of habitat and food sources. The feeding of those insects depends on the spatial and temporal distribution of nectar and pollen as food resources. Hence, to protect insect biodiversity a spatio-temporal assessment of food quantity of their habitats is necessary. Therefore, sufficient data on traits of floral resources are required.</p> <p><strong>New information</strong></p> <p>Because floral resources' traits of plants are important to quantify food availability, we present two databases, the FloRes Database (Floral Resources Database) and the raw database, where FloRes was derived from. Both databases contain the plant traits (1) flowering period, (2) floral-unit density per day, (3) nectar volume per floral unit per day, (4) sugar content per floral unit, (5) sugar concentration in nectar, (6) pollen mass or volume per floral unit and per day, (7) protein content of pollen and (8) corolla depth. All traits are sampled from literature and online databases. The raw database consists of 702 specified plant species, 138 unspecified species 37 species (spec., sp), 22 species <em>pluralis </em>(spp) and for 79 only the genus was identified) and two species complexes (agg.). Those 842 taxa belong to 488 genera and 102 families. Finally, only 27 taxa have a complete set of traits, too less for a sufficient assessment of spatio-temporal availability of floral food resources.</p> <p>Because information of floral resources is scattered throughout many publications with different units, we also present our multistep workflow implemented in five consecutive R-scripts. The multistep workflow standardizes the trait units of the raw database to comparable entities with identical units and aggregates them on a reasonable taxonomic level into the second application database, the FloRes Database. Finally, the FloRes Database contains aggregated information of traits for 42 taxa and, when corolla depth is excluded, for 70 taxa.</p> <p>This is the first attempt to gather these eight traits from different literature sources in one database with a multistep workflow. The publication of the multistep workflow enables the users to extend the FloRes Database on their own demands with other literature data or newly gathered data to improve the quantification of food resources. Especially, the combination of pollen, nectar, and open flowers per square meter is, as far as we know, a novelty.</p> <p>The FloRes Database can be used to evaluate the quantity of food-resource habitats available for pollinators, e.g., to compare seed mixtures of agri-environmental measures, such as flower strips, considering flower phenology on a daily basis.</p>
Figure A2 in A student-based expansion of the strategies of reproduction in fish (STOREFISH) database to 288 North American freshwater and anadromous species for 14 egg and larval traits
Figure A2. – Summary of the 162 answers for survey questions 5-9 (see Tab. A1 for details). Letter refer to the difficulties associated with (A) finding information (B) reading articles in English, (C) accessing documents, and (D) other reasons.
Figure 2 in A student-based expansion of the strategies of reproduction in fish (STOREFISH) database to 288 North American freshwater and anadromous species for 14 egg and larval traits
Figure 2. – The number of species (A) and records (B) in the original (black bars) and new (white bars) data sets for egg (left of the vertical bar) and larval (right of the bar) traits. Numbers in the x-axis correspond to trait numbers in Table I. The maximum possible number of species in (A) was 80 and 288 for the original and new data, respectively. See Table I for trait units and description.
Figure A3 in A student-based expansion of the strategies of reproduction in fish (STOREFISH) database to 288 North American freshwater and anadromous species for 14 egg and larval traits
Figure A3. – Boxplot summaries of the number of references (Q11) and traits (Q12) that the students found. See Table A1 for details.
Open database on traits and phylogenetic data on European pollinators
<p>(subtitle) data on wild bees, hoverflies and butterflies</p> <p>(abstract) This dataset was produced in the framework of the work package 1 (task 2) of the Horizon EU project Safeguard. The dataset encompassess trait data for three groups of pollinators: wild bees, hoverflies and butterflies, as well as phylogenetic data extracted from the GenBank. Information on traits comes from different literature sources, as well as direct measurements in collections and published databases. Some of the traits are common accross different species groups, while some traits are unique for particular group.</p> <p>(method) Litherature search and direct measurments on specimens.</p> <p>(dataset) This dataset compiles trait data for European wild bee, hoveverfly and butterfly species, and includes different categorical or numerical values for particular traits. It also contains phylogenetic information for species.</p>
LT-Brazil: A database of leaf traits across biomes and vegetation types in Brazil
<p><span>Motivation: Leaf traits represent an important component of plant functional strategies, and those related to carbon fixation and nutrient acquisition form the leaf economics spectrum. However, observations of functional leaf traits are underrepresented in tropical regions in comparison with those in temperate areas. Brazil, a country with continental scale and vast biodiversity is a timely example, where many biomes are impacted by human activities and climate change. However, leaf traits relevant to understand vegetation responses to these impacts remain poorly quantified for many species found in the country. We compiled an extensive data set of four functional leaf traits for native woody species occurring in the Brazilian territory. In addition to trait observations, sampling dates and geo-references were compiled and climatic parameters and soil properties of each sampling site were extracted from several databases.</span></p> <p><span>Main types of variables contained: The LT-Brazil data set contains 3479, 1216, 775, and 775 clean observations of leaf mass per area, leaf nitrogen (N) concentration per unit mass, leaf phosphorus (P) concentration per unit mass, and leaf N : P ratio, respectively, from native woody species, encompassing information of biome, vegetation, taxonomic data, geographical coordinates, climatic parameters, as well as soil properties.</span></p> <p><span>Spatial location and grain: We compiled trait observations from 223 sites under native vegetation distributed in all main biomes (i.e., Amazônia, Caatinga, Cerrado, Mata Atlântica, Pampa, and Pantanal) across the Brazilian territory.</span></p> <p><span>Time period and grain: The data represent information published and/or sampled during the last 25 years.</span></p> <p><span>Major taxa and level of measurement: Our compilation was focused on trait data observed for native woody species, excluding monocots, palm trees, herbs, and hemiparasitic plants. Thus, 108, 478, and 1321 botanical families, genera, and species were included, covering <em>c.</em> 9% of the woody angiosperm flora of Brazil.</span></p> <p>Software format: Data are provided as comma-separated value (.csv) files.</p>
The FloRes Database: A floral resources trait database for pollinator habitat-assessment generated by a multistep workflow
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LT-Brazil: A database of leaf traits across biomes and vegetation types in Brazil
Open the record for dataset details and reuse information.
Data from: PalmTraits 1.0, a species-level functional trait database for palms worldwide
Plant traits are critical to plant form and function —including growth, survival and reproduction— and therefore shape fundamental aspects of population and ecosystem dynamics as well as ecosystem services. Here, we present a global species-level compilation of key functional traits for palms (Arecaceae), a plant family with keystone importance in tropical and subtropical ecosystems. We derived measurements of essential functional traits for all (>2500) palm species from key sources such as monographs, books, other scientific publications, as well as herbarium collections. This includes traits related to growth form, stems, armature, leaves and fruits. Although many species are still lacking trait information, the standardized and global coverage of the data set will be important for supporting future studies in tropical ecology, rainforest evolution, paleoecology, biogeography, macroecology, macroevolution, global change biology and conservation. Potential uses are comparative eco-evolutionary studies, ecological research on community dynamics, plant-animal interactions and ecosystem functioning, studies on plant-based ecosystem services, as well as conservation science concerned with the loss and restoration of functional diversity in a changing world.
The Canopy Trait Plasticity (CANTRIP) database v1.0.0
<p>This release aligns with the version of the database published in Nature Plants.</p>
Genome Database: Turnover of strain-level diversity modulates functional traits in the honeybee gut microbiome between nurses and foragers
<p>This repository contains the dataset used in the publication "Turnover of strain-level diversity modulates functional traits in the honeybee gut microbiome between nurses and foragers," which is currently under revision. A pre-print can be found <a href="https://doi.org/10.1101/2022.12.29.522137">here</a>. The database is based on previously published work to create a genomic database of honeybee gut microbes by Kirsten Ellegaard (2021), found <a href="https://zenodo.org/records/4661061">here.</a></p><p>The zipped folder deposited here after unzipping, should contain the following files and directories:</p><ul><li>honeybee_genome.fasta : fasta file containing the host (<i>Apis mellifera</i>) genome sequence</li><li>beebiome_db : fasta file of 198 concatenated genomes with one genome per entry (multi-line fasta) where the headers represent the genome identifier</li><li>beebiome_red_db : fasta file of 39 species representative genomes with one genome per entry (multi-line fasta) where the headers represent the genome identifier to be used for the analysis of intra-specific variation</li><li>fna_files : directory containing genome sequence files and concatenated files where the concatenated files contain one fasta entry renamed to the genome identifier and all contigs concatenated into one entry</li><li>ffn_files : directory containing one file per genome listing the nucleotide sequence of all the predicted genes</li><li>faa_files : directory containing one file per genome listing the amino acid sequence of all the predicted genes</li><li>bed_files : directory containing bed files where the location of each of the predicted genes are indicated based on their position in the concatenated genome file</li><li>single_ortho : directory containing one file per phylotype listing all the single-copy orthogroups (OGs) identified by orthofinder where each line represents an OG id followed by a list of genes from each of the genomes of that phylotype that belong to that OG and the corresponding sequences of these genes can be found in the ffn file belonging to the respective genome</li><li>red_bed_files : directory containing bed files for species representative genomes that only list the positions genes that belong to the core orthogroups of their phylotype</li></ul><p>Further information about how this genome database was used to analyze strain-level diversity can be found in the publication and accompanying code repository.</p>
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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.