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Resources from: Disparate patterns of genetic divergence in three widespread corals across a pan-pacific environmental gradient highlights species-specific adaptation trajectories
<p>The following files are contained in this repository:</p> <p><br> README.Hume_et_al_2022.zenodov4.txt - This document.</p> <p>scripts.Hume_et_al_2022.zenodov4.pdf - Contains the scripts, or locations of the scripts, used to conduct the data analyses detailed in the associated manuscript.</p> <p>acknowledgements_local_authorities.Hume_et_al_2022.zenodov1.pdf - Acknowledgements of local authorities for the collection of samples used in the associated study.</p> <p>TaraPacific_SST_timeseries_mean_productsV2mai2021.Hume_et_al_2022.zenodov1.csv - The historical temperature data set used for the RDA, Mantel tests and gradient Forest analysis.</p> <p>Pocillopora_meandrina_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip - The Pocillopora SNPs referred to as 'raw' in the Methods of the associated manuscript. Compressed using genozip (https://genozip.readthedocs.io/index.html).</p> <p>Pocillopora_meandrina_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip.md5 - md5 of the the Pocillopora raw SNPs.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'linked' in the Methods of the associated manuscript.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora linked SNPs.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'unlinked' in the Methods of the associated manuscript.</p> <p>Pocillopora_meandrina_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora unlinked SNPs.</p> <p>Porites_lobata_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip - The Pocillopora SNPs referred to as 'raw' in the Methods of the associated manuscript. Compressed using genozip (https://genozip.readthedocs.io/index.html).</p> <p>Porites_lobata_v3_11Islands.raw.Hume_et_al_2022.zenodov2.vcf.genozip.md5 - md5 of the the Pocillopora raw SNPs.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'linked' in the Methods of the associated manuscript.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss.linked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora linked SNPs.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz - The Pocillopora SNPs referred to as 'unlinked' in the Methods of the associated manuscript.</p> <p>Porites_lobata_v3_11Islands_maf05_minQ30_biallelic_nomiss_LD02.unlinked.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Pocillopora unlinked SNPs.</p> <p>PANAMA2021.raw.Hume_et_al_2022.zenodov2.vcf.gz - The Millepora SNPs referred to as 'raw' in the Methods of the associated manuscript.</p> <p>PANAMA2021.raw.Hume_et_al_2022.zenodov2.vcf.gz.md5 - md5 of the the Millepora raw SNPs.</p> <p>Millepora_REF_orthologue_genes.Hume_et_al_2022.zenodov2.csv - The Millepora gene list referred to as 'target genes' in the Methods of the associated manuscript.</p> <p>Mil_transcriptom.Hume_et_al_2022.zenodov2.fa.gz - The Millepora de novo assembled transcriptome.</p> <p>Mil_transcriptom.Hume_et_al_2022.zenodov2.fa.gz.md5 - md5 of the Millepora de novo assembled transcriptome.</p> <p> </p> <p>mtORF Phylogeny</p> <p>TP-Johnston_mtORF-Pocillo.fa = all sequences</p> <p>TP-Johnston_mtORF-Pocillo.mafft.fa = mafft alignment</p> <p>TP-Johnston_mtORF-Pocillo.mafft.ML.nwk = ML tree newick</p> <p> </p> <p>Hellberg genotype network Porites</p> <p>TP-Hellberg_MM32-Porites.nex = all aligned sequences for this locus with indels encoded</p> <p>TP-Hellberg_MM100-Porites.nex = all aligned sequences for this locus with indels encoded</p> <p>TP-Hellberg_ATPaseB.nex = all aligned sequences for this locus with indels encoded,</p> <p>TP-Hellberg_POFAD.nex = POFAD multilocus genotypic distance,</p> <p>TP-Hellberg_Splitstree.nex= Multilocus genotype network in nexus format</p> <p><br> Gradient Forest Analysis</p> <p>Poc_abund.csv - Pocillopora SSH Occurrences per Site er Island</p> <p>Por_abund.csv - Porites SSH Occurrences per Site er Island</p> <p>mean_depth_por.csv - per site per island mean depth among Porites colonies</p> <p>mean_depth_poc.csv - per site per island mean depth among Pocillopora colonies</p>
SBC LTER: Reef: Long-term experiment: biomass of kelp forest species, ongoing since 2008 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sbc/119/7. The abstract below was extracted from the Level 0 data package and is included for context: These data represent values of biomass density for more than 200 species of macroalgae, invertebrates and fish measured in fixed plots at five reefs as part of a long-term experiment designed to evaluate the effects of disturbance to giant kelp on the structure and productivity of the benthic community. Taxon-specific relationships between size and mass were applied to field measurements of species abundance to estimate biomass density of each species. The five reefs (Arroyo Quemado 34°28.048’N, 120°07.031’W; Carpinteria 34°23.474’N, 119°32.510’W; Isla Vista 34°23.275’N, 119°32.792’W; Mohawk 34°23.649’N, 119°43.762’W; and Naples 34° 25.342’N, 119° 57.102’W) ranged in depth from 5.8 m to 8.9 m (MLLW) and were chosen to represent a range of physical and biological characteristics known to influence subtidal macroalgal assemblages in the region. A common (but not always persistent) feature on these reefs was the presence of the giant kelp, which forms a dense canopy at the sea surface that suppresses recruitment and growth of understory algae below it. See Methods for more information. The primary research objective of the Santa Barbara Coastal LTER is to investigate the importance of land and ocean processes in structuring giant kelp (Macrocystis pyrifera ) forest ecosystems. As in many temperate regions, the shallow rocky reefs in the Santa Barbara Channel, California, are dominated by giant kelp forests. Because of their close proximity to shore, kelp forests are influenced by physical and biological processes occurring on land as well as in the open ocean. SBC LTER research foc
SBC LTER: Reef: Annual time series of biomass for kelp forest species, ongoing since 2000 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sbc/50/10. The abstract below was extracted from the Level 0 data package and is included for context: These data are annual estimates of biomass of approximately 225 taxa of reef algae, invertebrates and fish in permanent transects at 11 kelp forest sites in the Santa Barbara Channel (2-8 transects per site). Abundance is measured annually (as percent cover or density, by size) and converted to biomass (i.e., wet mass, dry mass, decalcified dry mass, ash free dry mass) using published taxon-specific algorithms. Data collection began in summer 2000 and continues annually in summer to provide information on community structure, population dynamics and species change. The time period of data collection varied among the 11 kelp forest sites. Sampling at BULL, CARP, and NAPL began in 2000, sampling at the other 6 mainland sites (AHND, AQUE, IVEE, GOLB, ABUR, MOHK) began in 2001 (transects 3, 5, 6, 7, 8 at IVEE were added in 2011). Data collection at the two Santa Cruz Island sites (SCTW and SCDI) began in 2004. See Methods for more information. See Methods for more information. The primary research objective of the Santa Barbara Coastal LTER is to investigate the importance of land and ocean processes in structuring giant kelp (Macrocystis pyrifera ) forest ecosystems. As in many temperate regions, the shallow rocky reefs in the Santa Barbara Channel, California, are dominated by giant kelp forests. Because of their close proximity to shore, kelp forests are influenced by physical and biological processes occurring on land as well as in the open ocean. SBC LTER research focuses on measuring and modeling the patterns, transport, and processing of material constituents (e.g., nutrients
PVC02 Plant species composition on selected watersheds at Konza Prairie (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-knz/69/18. The abstract below was extracted from the Level 0 data package and is included for context: Canopy coverage and frequency were recorded in 20 circular 10 sq m plots. Six treatments were sampled, three ungrazed and three to grazed by native grazers. In each case one of the three watersheds was unburned, another burned annually in April, the third burned every four years in April. In each treatment two soils were sampled: a lower-slope deep fertile nonrocky soil (tully silty clay loam), and a shallow rocky soil (florence cherty silt loam) on level to gently sloping ridges. In 1983 another ungrazed annual burn area '1c' was added 'both tully and florence soils' because original area '1d' appeared aberrant.
[DEPRECATED] Vegetation Plots of the Bonanza Creek LTER Control Plots: Species Count (1975 - 2004) (Reformatted to ecocomDP Design Pattern)
This ecocomDP data package has been deprecated due to issues in the L0 source dataset that prohibits the creation of an L1 ecocomDP dataset. This data package is formatted according to the "ecocomDP", a data package design pattern for ecological community surveys, and data from studies of composition and biodiversity. For more information on the ecocomDP project see https://github.com/EDIorg/ecocomDP/tree/master, or contact EDI https://environmentaldatainitiative.org. This Level 1 data package was derived from the Level 0 data package found here: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-bnz&identifier=175&revision=20 The abstract below was extracted from the Level 0 data package and is included for context: These data are the vegetation datasets for 27 LTER sites in Bonanza Creek Experimental Forest. The 27 sites are divided into three replicates for six primary successional stages on the floodplains (3 replicates X 6 successional stages = 18 sites) and three replicates for three secondary successional stages in the uplands (3 replicates X 3 successional stages = 9 sites). Data include: 1) Visual estimates of percent cover, 2) Stem counts (the number of individuals/species), and 3) Heights (cm) for "tall shrub species" in twenty 4 m2 plots. Shrubs are considered "Tall shrubs" if they are Salix sp., Alnus sp., Rosa acicularis, Viburnum edule, Betula nana, Betula glandulosa, or Rubus idaeus. Initial colonziations plots (FP0s, SL1s, HR1A) were remeasured every year. Early successional plots were remeasured every 2-4 years. Later succesional plots were remeasured approximately every five years. For a detail schedule of plot measurements please see the file: Vegetation Monitoring Schedule.xls Although most sites were established in 1988 some sites have vegetation plots that have been sampled periodically since 1965. In 2006 shrub data collection was changed to a transect method of sampling. These data can be found in the file: <a href="http://www.lte
Data and code from: Insect biomass decline scaled to species diversity: General patterns derived from a hoverfly community
<p>To study changes in flying insect communities, and hoverflies in particular, malaise trap samples from a German site were compared between two years (Hallmann et al. 2020). The data files deposited here contain data obtained from six malaise traps in the Wahnbachtal (North Rhine-Westphalia, Germany, 50.851944N, 7.320833E) that were deployed in 1989 and again in 2014, at the exact same locations. Traps were situated in wet meadows as well as tall perennial meadows, in close proximity to shrub corridors, to forest–grassland borders, and to the Wahnbach River and surrounded by agricultural land, essentially a rather heterogeneous habitat. The Wahnbach River and the greater part of the valley are protected for watershed purposes and are subject to nature conservation management by the Wahnbach Talperrenverband. Hence, several restrictions apply to safeguard against water contamination.</p> <p>Total insect biomass collected with these traps was already included in Hallmann et al. (2017), but here we focus on additional information: the abundance and richness of hoverflies (Syrphidae) in each of the collected samples (pots). Methodologies of collection are described in Sorg (1990), Schwan et al. (1993), Sorg et al. (2013), Hallmann et al. (2017), and Ssymank et al. (2018). In brief, malaise traps were deployed throughout the growing season and operated continuously (day and night). Malaise trap construction (e.g., size, material, colouring, and ground sealing) and placing (e.g., positioning, orientation, and slope of the locations) were standardised in all aspects. Insect samples were preserved in 80% ethanol solution. Catches of the six traps investigated in the present study were emptied regularly: On average exposure intervals were 7.0 d (SD = 0.5) in 1989 and 16.7 d (SD = 5.6) in 2014. Across the six traps in 2014 the total exposure time (in number of days) was 42% higher compared to 1989. All collected samples (n = 196) were used in the present analysis with in total 19,604 individual hoverflies counted, distributed over 162 species and 59 genera.</p> <p>To assess how environmental conditions have changed over the 25 year, several additional datasets were assembled. Climatic<br> data were obtained from 169 climatic stations and were used to interpolate daily weather variables to each trap location, using spatiotemporal kriging. These steps are described in detail in Hallmann et al. (2017).</p> <p>Our analysis (see R code) consists of three components. First, we considered total abundance, species richness, and species diversity, at two temporal scales: pooled per year, i.e., across the sampling season, and seasonally (i.e., per day), and we compared these metrics between 1989 and 2014. Second, we examined how total flying biomass (i.e., the weight of all trapped insects, of which hoverflies are only a small proportion) related to total abundance as well as species richness of hoverflies. Third, we derived persistence probabilities and population growth rate trends per species, to examine interspecific variation in these parameters.</p> <p>Descriptions of the deposited files:</p> <p><strong>Groups.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> Nspec = number of different hoverfly species found in a pot<br> Nind = number of hoverfly individuals found in a pot</p> <p><strong>Counts.csv</strong><br> A matrix of counts of individual hoverflies per pot per species. The 196 rows represent the pots in the same order as in the file 'Groups.csv'. The columns represent the 162 different hoverfly species found. The scientific species names are indicated in the column headers.</p> <p><strong>PairedData.csv</strong><br> pot = sample identifier<br> JAHR = year of sampling<br> MF_NR = identifier of each of the six malaise trap locations<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> NI = number of hoverfly individuals found in a potbiomass.daily<br> NSP = number of different hoverfly species found in a pot<br> biomass.daily = daily fresh weight [gram] of flying insects: total fresh weight in a pot divided by the number of sampling days.</p> <p><strong>ModelFrame.csv</strong><br> MF_NR = identifier of each of the six malaise trap locations<br> yrf = year of sampling<br> pot = sample identifier<br> dt = number of sampling days<br> from.dnr = day-of-the-year on which a pot was attached to a malaise trap<br> to.dnr = day-of-the-year on which a pot was collected from a malaise trap<br> mean.daynr = mean day-of-the-year of the sampling period<br> plot = identifier of each of the six malaise trap locations<br> date = date for which the weather variables are interpolated<br> daynr = day-of-the-year for which the weather variables are interpolated<br> altitude = altitude [m] of the malaise trap locations<br> year = year of sampling<br> temperature = interpolated temperature [degrees Celsius]<br> precipitation = interpolated precipitation [mm per day]<br> wind.speed = interpolated wind speed [m/s]</p> <p><strong>Data_Rcode.pdf</strong><br> This pdf provides the R-code behind the analysis of the Hoverfly data. Three datasets are provided along with this R-code document, namely "Counts.csv", "Groups.csv", "PairedData.csv" and "ModelFrame.csv". Additionally, the BUGS-code ""syrphidModel.jag" is required for running the daily-activity model in JAGS.</p> <p><strong>syrphidModel.jag</strong><br> This BUGS-code is required for running the daily-activity model in JAGS.</p>
Patterns and drivers of species diversity in the Indo-Pacific red seaweed Portieria: phylogenetic data
<p>Alignments, trees and Biogeobears analyses related to the study: Leliaert F, Payo DA, Gurgel CFD, Schils T, Draisma SGA, Saunders GW, Kamiya M, Sherwood AR, Lin S-M, Huisman John M, Le Gall L, Anderson RJ, Bolton John J, Mattio L, Zubia M, Spokes T, Vieira C, Payri CE, Coppejans E, D'hondt S, Verbruggen H, De Clerck O. Patterns and drivers of species diversity in the Indo-Pacific red seaweed Portieria. Journal of Biogeography. 2018;45(10):2299-313. doi:10.1111/jbi.13410</p> <p>Abstract: Biogeographical processes underlying Indo-Pacific biodiversity patterns have been relatively well studied in marine shallow water invertebrates and fishes, but have been explored much less extensively in seaweeds, despite these organisms often displaying markedly different patterns. Using the marine red alga Portieria as a model, we aim to gain understanding of the evolutionary processes generating seaweed biogeographical patterns. Our results will be evaluated and compared with known patterns and processes in animals. Species diversity estimates were inferred using DNA-based species delimitation methods. Historical biogeographical patterns were inferred based on a six-gene time-calibrated phylogeny, distribution data of 802 specimens, and probabilistic modelling of geographic range evolution. The importance of geographic isolation for speciation was further evaluated by population genetic analyses at the intraspecific level. We delimited 92 candidate species, most with restricted distributions, suggesting low dispersal capacity. Highest species diversity was found in the Indo-Malay Archipelago (IMA). Our phylogeny indicates that Portieria originated during the late Cretaceous in the area that is now the Central Indo-Pacific. The biogeographical history of Portieria includes repeated dispersal events to peripheral regions, followed by long-term persistence and diversification of lineages within those regions, and limited dispersal back to the IMA. Our results suggest that the long geological history of the IMA played an important role in shaping Portieria diversity. High species richness in the IMA resulted from a combination of speciation at small spatial scales, possibly as a result of increased regional habitat diversity from the Eocene onwards, and species accumulation via dispersal and/or island integration through tectonic movement. Our results are consistent with the biodiversity feedback model, in which biodiversity hotspots act as both ‘centres of origin’ and ‘centres of accumulation’, and corroborate previous findings for invertebrates and fish that there is no single unifying model explaining the biological diversity within the IMA.</p>
Fig. 17. Begonia burkillii Dunn. A. Leaf pattern. B. Male flowers. Photograph A in A revision and one new species of Begonia L. (Begoniaceae, Cucurbitales) in Northeast India
Fig. 17. Begonia burkillii Dunn. A. Leaf pattern. B. Male flowers. Photograph A courtesy of Aaron Matsumoto and photograph B courtesy of Earl I-Lan of plants in cultivation in private collections.
Fig. 18 in Annonaceae in the Western Pacific: geographic patterns and four new species
Fig. 18. Regional distribution of the genus Uvaria (Annonaceae) in the Pacific. Base map sourced from CartoGIS, College of Asia and the Pacific, The Australian National University, Australia.
Fig. 14 in Annonaceae in the Western Pacific: geographic patterns and four new species
Fig. 14. Regional distribution of the genus Monoon (Annonaceae) in the Pacific. Base map sourced from CartoGIS, College of Asia and the Pacific, The Australian National University, Australia.
Fig. 15 in Annonaceae in the Western Pacific: geographic patterns and four new species
Fig. 15. Lectotype of Polyalthia merrillii Kaneh. Right-hand image has the label folded back to reveal the obscured parts of the specimen. Images provided by the Herbarium of Kyushu University (FU), Japan.
Fig. 11 in Annonaceae in the Western Pacific: geographic patterns and four new species
Fig. 11. Lectotype of Goniothalamus carolinensis Kaneh. Image provided by the Herbarium of Kyushu University (FU), Japan.
Fig. 8 in Annonaceae in the Western Pacific: geographic patterns and four new species
Fig. 8. Map of Samoa showing the collecting localities of Huberantha whistleri I.M.Turner & Utteridge sp. nov.
Fig. 7. Huberantha whistleri I.M in Annonaceae in the Western Pacific: geographic patterns and four new species
Fig. 7. Huberantha whistleri I.M.Turner & Utteridge sp. nov. A. Flowering shoot (lower right leaf showing abaxial surface, rest adaxial). B. Flower lateral view (one petal missing). C. Remnant flower with carpels and persistent calyx after loss of corolla and stamens. D, E. Two views of stamen. F. Ovary and stigma. G. Fruit (same scale as A). H. Monocarp (immature). Scale bars: graduated single bar = 2 mm; double bar = 1 cm; graduated double bar = 5 cm. Drawn from Whistler 576. Drawn by Andrew Brown.
Fig. 17 in Annonaceae in the Western Pacific: geographic patterns and four new species
Fig. 17. Regional distribution of the genus Popowia (Annonaceae) in the Pacific. Base map sourced from CartoGIS, College of Asia and the Pacific, The Australian National University, Australia.
Fig. 5. Huberantha asymmetrica I.M in Annonaceae in the Western Pacific: geographic patterns and four new species
Fig. 5. Huberantha asymmetrica I.M.Turner & Utteridge sp. nov. A. Leafy shoot bearing fruit (leaf marked with an asterisk showing adaxial surface, rest with abaxial view). B. Leaf (adaxial view) showing distinct asymmetry. C. Flowering shoot. D. Leaf lamina abaxial midrib region showing indumentum. E. Leaf lamina adaxial midrib region showing indumentum. F. Flower viewed from below. G. Monocarp sectioned longitudinally. Scale bars: graduated single bar = 2 mm; double bar = 1 cm; graduated double bar = 5 cm. Drawn from BSIP 12085 (A in part, D, E); BSIP 9859 (A in part, G); BSIP 12719 (C); BSIP 12261 (B). Drawn by Andrew Brown.
Fig. 4 in Annonaceae in the Western Pacific: geographic patterns and four new species
Fig. 4. Map of the Solomon Archipelago showing the collecting localities of Monoon salomonicum I.M.Turner & Utteridge sp. nov.
Fig. 16 in Annonaceae in the Western Pacific: geographic patterns and four new species
Fig. 16. Regional distribution of the genus Polyalthia (Annonaceae) in the Pacific. Base map sourced from CartoGIS, College of Asia and the Pacific, The Australian National University, Australia.
Fig. 2 in Annonaceae in the Western Pacific: geographic patterns and four new species
Fig. 2. Map of New Guinea showing the collecting localities of Monoon pachypetalum I.M.Turner & Utteridge sp. nov.
Fig. 3. Monoon salomonicum I.M in Annonaceae in the Western Pacific: geographic patterns and four new species
Fig. 3. Monoon salomonicum I.M. Turner & Utteridge sp. nov. A. Leafy twig. B. Domatia in axils of secondary nerves on leaf abaxial surface. C. Indumentum on adaxial surface of midrib. D. Example of more distinctly acuminate leaf apex. E. Branchlet bearing flower. F, G. Two views of flower, one attached, one detached. H. Fruiting pedicel bearing two monocarps. I. Monocarp with part of pericarp removed to expose seed. J. Transverse section of monocarp. Scale bars: graduated single bar = 2 mm; double bar = 1 cm; graduated double bar = 5 cm. Drawn from BSIP 3661 (A, C, E–J); RSS 2530 (B, D). Drawn by Andrew Brown.
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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.