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<i>k</i>-mer-based diversity scales with population size proxies more than nucleotide diversity in a meta-analysis of 98 plant species
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Data from: Body size correlates with discrete character morphological proxies
Principal coordinates analysis (PCoA) is a statistical ordination technique commonly applied to morphology-based cladistic matrices to study macroevolutionary patterns, morphospace occupation and disparity. However, PCoA-based morphospaces are dissociated from the original data; therefore, whether such morphospaces accurately reflect body plan disparity or extrinsic factors, such as body size, remains uncertain. We collated nine character-taxon matrices of dinosaurs together with body mass estimates for all taxa and tested for relationships between body size and both the principal ordinated axis of variation (PCo1) and the entire set of PCo scores. The possible effects of body size on macroevolutionary hypotheses derived from ordinated matrices were tested by re-evaluating evidence for the accelerated accumulation of avian-type traits indicated by a strong directional shift in PCo1 scores in hypothetical ancestors of modern birds. Body mass significantly accounted for, on average, approximately 50 and 16 per cent of the phylogenetically corrected variance in PCo1 and all PCo scores, respectively. Along the avian stem lineage, approximately 30 per cent of the morphological variation is attributed to the reconstructed body masses of each ancestor. When the effects of body size are adjusted, the period of accelerated trait accumulation is replaced by a more gradual, additive process. Our results indicate that even at low proportions of variance, body size can noticeably effect macroevolutionary hypotheses generated from ordinated morphospaces. Future studies should thoroughly explore the nature of their character data in association with PCoA-based morphospaces and use a residual/covariate approach to account for potential correlations with body size.
Datasets for ``Application of a helicity proxy to edge-on galaxies''
<pre>The run directories contain unformatted data files either as data/proc0 (for 128x128x32_20kc_BDMSST93c_Oml) or as data/proc0-3 (for 128x64_20kc_BDMSST93_wind10a), or as bb.sav just for the magnetic field components in the other directories. They can be read directly with the corresponding idl routines that are in the directory (run_directories/run_idl) together with those delivered with the Pencil Code (https://github.com/pencil-code).</pre>
Data from: Abiotic proxies for predictive mapping of near-shore benthic assemblages: implications for marine spatial planning
Marine spatial planning (MSP) should assist managers in guiding human activities towards sustainable practices and in minimizing user-conflicts in our oceans. A necessary first step is to quantify spatial patterns of marine assemblages in order to understand the ecosystem's structure, function, and services. However, the large spatial scale, high economic value, and density of human activities in near-shore habitats often makes quantifying this component of marine ecosystems especially daunting. To address this challenge, we developed an assessment method that employs abiotic proxies to rapidly characterize marine assemblages in near-shore benthic environments with relatively high resolution. We evaluated this assessment method along 300 km of the State of Maine's coastal shelf (< 100m depth)—a zone where high densities of buoyed lobster traps typically preclude extensive surveys by towed sampling gear (i.e., otter trawls). During the summer months of 2010-2013, we implemented a stratified-random survey using a small remotely operated vehicle that allowed us to work around lobster buoys and to quantify all benthic megafauna to species. Stratifying by substrate, depth, and coastal water masses, we found that abiotic variables explained a significant portion of variance (37- 59%) in benthic species composition, diversity, biomass and economic value. Generally, the density, diversity, and biomass of assemblages significantly increased with the substrate complexity (i.e., from sand-mud to ledge). The diversity, biomass and economic value of assemblages also decreased significantly with increasing depth. Lastly demersal fish densities, sessile invertebrate densities, species diversity, and assemblage biomass increased from east to west, while the abundance of mobile invertebrates and economic value decreased, corresponding mainly to the contrasting water-mass characteristics of the Maine Coastal Current system (i.e., summertime current direction, speed, and temperature). Integrating modeled predictions with existing GIS layers for abiotic conditions allowed us to scale up important assemblage attributes to define key foundational ecological principles of MSP and to find priority regions where some bottom-disturbing activities would have minimal impact to benthic assemblages. We conclude that abiotic proxies can be strong forcing functions for the assembly of marine communities and therefore useful tools for spatial extrapolations of marine assemblages in congested (heavily used) near-shore habitats.
Data from: Delusions of grandeur: seed count is not a good fitness proxy under individual variation in phenology
The concept of fitness is central to evolutionary biology, yet it is difficult to define and to measure. In plant biology, fitness is often measured as seed count. However, under an array of circumstances, seed count may be a biased proxy of fitness; for example, when individuals vary in allocation to sexual vs. asexual reproduction. A more subtle example, but also likely to be important in natural populations, is when interindividual variation in conditions during development results in variation in offspring quality among seed parents. In monocarpic (semelparous) plants, this is expected to result from variation in effective season length experienced among individuals that reach reproductive maturity at different times. Here, we manipulate growing season length to ask whether seed count is an accurate representation of parental fitness in the monocarpic herb Lobelia inflata. Simple seed count suggests a paradoxical fitness advantage under constrained season length. However, we find that the apparent fitness advantage of a constrained season length is overridden by low relative per-seed fitness. Furthermore, the fitness deficit in the constrained environment is associated primarily with an accelerating decrease in viability and seedling survival in seeds derived from fruits produced progressively later in the season. In this study, the overall fitness value of a seed under a constrained season is 0.774 of that observed under a long season.
Amulet of Amun [3D Printable Proxy]
Used as a 3D proxy in a demo Musueum in a Box http://museuminabox.org Original: "Blue glazed composition amuletic figure of a ram; hole in the top of the head for the attachment of a headdress(now lost); pierced suspension ring on the back; damage to the front of the base."~http://www.britishmuseum.org/research/collection_online/collection_object_details.aspx?objectId=130052&partId=1&searchText=amulet+ram&images=true&page=1  "We made a demonstration box that's a blend of 3D and 2D objects about daily life in ancient Egypt. We're finding that a demo box speaks volumes about potential, and it was great to be able to show the box to families at the museum." ~ http://museuminabox.org/boxes Because of a super fast turnaround to produce this box we were unable to get access to 3D scan the objects we wanted to include - so we quickly bashed out some proxy replicas in Blender! Source: Objaverse 1.0 / Sketchfab
Pottery Flask [3D Printable Proxy]
Used as a 3D proxy in a demo Musueum in a Box http://museuminabox.org Original: "Pottery flask of Phoenician (?) Black-on-Red ware; biconvex body, narrow concave neck flaring to a funnel-shaped mouth, two vertical handles from shoulder to neck; red clay and slip, decorated with black paint: vertical triple concentric circles on the sides of body."~http://twoway.st/things/GAA44978  "We made a demonstration box that's a blend of 3D and 2D objects about daily life in ancient Egypt. We're finding that a demo box speaks volumes about potential, and it was great to be able to show the box to families at the museum." ~ http://museuminabox.org/boxes Because of a super fast turnaround to produce this box we were unable to get access to 3D scan the objects we wanted to include - so we quickly bashed out some proxy replicas using Smoothie 3D and Blender! Source: Objaverse 1.0 / Sketchfab
Energy management strategies for ectothermic vertebrates: temperature and heart rate as independent proxies
<p>Filtered data used for analysis of energy management strategies in three different species. R script provides an example of the across- and within-individual analysis. </p>
Cylinder Seal [3D Printable Proxy]
Used as a 3D proxy in a demo Musueum in a Box http://museuminabox.org Reverse engineered from this image:  ...and it works!  "Cylinder seal of lustrous black haematite: perforated through the centre, with a roughly incised inscription. Good condition."  "We made a demonstration box that's a blend of 3D and 2D objects about daily life in ancient Egypt. We're finding that a demo box speaks volumes about potential, and it was great to be able to show the box to families at the museum." ~ http://museuminabox.org/boxes Because of a super fast turnaround to produce this box we were unable to get access to 3D scan the objects we wanted to include - so we quickly bashed out some proxy replicas in Blender! Source: Objaverse 1.0 / Sketchfab
Data from: Are fecal samples an appropriate proxy for amphibian intestinal microbiota?
<p>The intestinal microbiota, an invisible organ supporting a host's survival, has essential roles in metabolism, immunity, growth, and development. Since intestinal microbiota influences a host's biology, application of such data to wildlife conservation has gained interest. There are standard protocols for studying the human intestinal microbiota, but no equivalent for wildlife. A major challenge is sampling the intestinal microbiota in an effective, unbiased way. Fecal samples are a popular proxy for intestinal microbiota because collection is non-invasive, convenient, and allows for longitudinal sampling. Yet, it is unclear whether the fecal microbiota is representative of the intestinal microbiota. In amphibians, research on sampling methodology is limited. In this study, we characterize and compare microbiota (small intestine, large intestine, feces) of two Hong Kong stream-dwelling frog species: Lesser Spiny Frog (<em>Quasipaa exilispinosa</em>), and Hong Kong Cascade Frog (<em>Amolops hongkongensis</em>). We found that both species have similar dominant phyla and families, but diverge in terms of the dominant genera. Next, we assess the performance of fecal microbiota in representing the intestinal microbiota in these two species. We found that (1) microbiota of small and large intestine differs significantly, (2) feces are not an appropriate proxy of both intestinal sections, and (3) a set of microbial taxa significantly differs between sample types. Our cautions equating fecal and intestinal microbiota. Sampling feces can avoid sacrifice of an animal, but researchers should avoid over-extrapolation and interpret results carefully.</p>
Proxy-based hail-prone-day climatology and trends for Australia 1979-2021
<p><strong>Climatology of and trends on hail-prone days over Australia from 1979-2021</strong></p> <p>Hail-prone day climatology is calculated using the proxy of Raupach et al., 2023a (DOI: 10.1175/MWR-D-22-0127.1) applied to ERA5 pressure-level reanalysis data (DOI: 10.24381/cds.bd0915c6) at gridded 0.25 degree resolution for 1979-2021 (daily) and March 1979-May 2022 (seasonally). A day was considered hail-prone if a hail-prone atmosphere was detected at 03, 06, or 09 UTC. Trends in hail-prone days are calculated as in Raupach et al., 2023b (DOI: 10.1038/s41612-023-00454-8).</p> <p>The climatology shows the mean annual hail-prone days per grid point over 1979-2021, while seasonal climatologies are over March 1979-May 2022. The trends information shows changes in annual hail-prone days over the same period.</p> <p>NB: The seasonal climatology values differ slightly from those in Supp. Figure 3 in Raupach et al 2023, owing to an improved calculation of the climatology. The differences for each season are less than hail-prone 0.5 days. For details of the change see https://github.com/traupach/era5_hail_climatology/blob/main/analysis/climatology_for_zenodo.ipynb.</p> <p>These data are licensed with a Creative Commons Attribution 4.0 International license (CC-BY-4.0, https://creativecommons.org/licenses/by/4.0/legalcode). These results contain modified Copernicus Climate Change Service information 2022. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains.</p> <p>If using these data, please inform T. Raupach at t.raupach@unsw.edu.au, and please cite the following paper for which these data were calculated: </p> <p>Raupach, T.H., Soderholm, J.S., Warren, R.A. <em>et al.</em> Changes in hail hazard across Australia: 1979–2021. <em>npj Clim Atmos Sci</em> <strong>6</strong>, 143 (2023). https://doi.org/10.1038/s41612-023-00454-8</p> <p>Acknowledgements: This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government.</p>
FIGURE 7 in Modern vegetation proxies reflect Palaeogene and Neogene vegetation evolution and climate change in Europe, Turkey, and Armenia
FIGURE 7 (caption on next page).
Figure 6 in Description of the skeleton of the fossil beaked whale Messapicetus gregarius: searching potential proxies for deep-diving abilities
Figure 6. Right scapula of the specimen MUSM 2542, Messapicetus gregarius, in dorsal view.
APPENDIX 1 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies
<p>APPENDIX 1.— Supplementary material including the fossil specimens from the studied sites and the morphological character scores for CLAMP analysis</p><table><thead><tr><th><b>Lower Río Turbio Fm</b></th><th><b>LMA</b></th><th><b>LAA</b></th><th><b>Source</b></th></tr></thead><tbody><tr><th><i>Acrodiclidium flavianum</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Allophylus graciliformis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Anacardites pichileufensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Annona</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Casearia</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cissus pichileufensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cinnamomum neogaea</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cupania grosse-serrata</i></th><td>*</td><td>-</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cupania latifolioides</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cupania patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cupania santacrucensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Drimys patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Drimys</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Eucalyptus</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Escallonia</i> sp.</th><td>*</td><td><b>–</b></td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Myrcia</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Myricaceae</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Myrica hunzikerii</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nectandra prolifica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nothofagus subferruginea</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nothofagus variabilis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nothofagus serrulata</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nothofagus elongata</i></th><td>*</td><td><b>–</b></td><td>Hünicken 1967</td></tr><tr><th><i>Ocotea menendezi</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Ocotea</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Persea</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Psidium liociardensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Qualea patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Schinopsis patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Styrax</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Styrax glandulifera</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Tetracera</i> cf. <i>patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Zyziphus chubutensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Malvaceae</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Urticaceae</i></th><td>*</td><td><b>–</b></td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th>cf. <i>Pouterlabatia clark</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Paullinia</i> sp.</th><td>*</td><td><b>–</b></td><td>Panti 2018</td></tr><tr><th><i>Banisteriophyllum</i></th><td>*</td><td><b>–</b></td><td>Panti 2018</td></tr></tbody></table>
TABLE 4 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies
<p>TABLE 4 — Estimated values for the climate variables in the lower Río Turbio Formation member from CLAMP analysis using the CLAMP3B SA and CLAMPSH90 data set.</p><table><thead><tr><th><b>CLAMP Climate parameters</b></th><th><b>Estimates</b></th><th><b>Error</b></th><th><b>Dataset</b></th><th><b>Estimates</b></th><th><b>Error</b></th><th><b>Data set</b></th></tr></thead><tbody><tr><th>Mean annual temperature (°C)</th><td>12.7</td><td>2.1</td><td>CLAMP3BSA</td><td>12.2</td><td>4.83</td><td>CLAMPSH90</td></tr><tr><th>Warm month mean temperature (°C)</th><td>23.2</td><td>3.3</td><td>CLAMP3BSA</td><td>21.5</td><td>7.5</td><td>CLAMPSH90</td></tr><tr><th>Cold month mean temperature (°C)</th><td>3.4</td><td>3.8</td><td>CLAMP3BSA</td><td>4</td><td>4.6</td><td>CLAMPSH90</td></tr><tr><th>Mean Growing Season Precipitation (mm)</th><td>1741</td><td>42.6</td><td>CLAMP3BSA</td><td>1394</td><td>68</td><td>CLAMPSH90</td></tr><tr><th>3-Wettest Months Precipitation (mm)</th><td>784</td><td>19.8</td><td>CLAMP3BSA</td><td>460</td><td>35</td><td>CLAMPSH90</td></tr><tr><th>3-Driest Months Precipitation (mm)</th><td>334</td><td>15.3</td><td>CLAMP3BSA</td><td>160</td><td>22.8</td><td>CLAMPSH90</td></tr><tr><th>Length Growing Season</th><td>7.7</td><td>1.2</td><td>CLAMP3BSA</td><td>7.4</td><td>0.8</td><td>CLAMPSH90</td></tr></tbody></table>
Validation of the UK'37 paleotemperature proxy in the South Brazilian Bight from core-top sediments
<p>Alkenone unsaturation index, water depth temperature, and nutrient concentration from the South Brazilian Bight (SBB). Temperature and nutrient content were retrieved from the World Ocean Atlas 2018. Residual analyses were performed using six paleoequations against observed temperature from WOA18. The PCA analysis was performed using alkenone unsaturation index, temperature, and nutrient data from each data point.</p>
Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with calibrated AVHRR surface reflectance (LCSPP-AVHRR), 2001-2023
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), generated using the LCREF-AVHRR record from 1982–2023. Due to Zenodo’s size constraints, LCSPP-AVHRR is divided into two separate repositories. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals.</p> <p>Key updates in version 3.2 include:</p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>Other LCSPP repositories can be accessed via the following links:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-MODIS v3.2(2001-2023): <a href="https://doi.org/10.5281/zenodo.11658088" target="_blank" rel="noopener">10.5281/zenodo.11658088</a></li> </ul> <p>The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test. </p> <p>In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:</p> <ul> <li>LCREF-AVHRR v3.2 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that <strong>LCSPP</strong> <strong>is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements</strong>. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.</p> <p>All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p>
Long-term Continuous SIF-informed Photosynthesis Proxy reconstructed with MODIS surface reflectance (LCSPP-MODIS), 2001-2023
<p><strong>Usage Notes</strong>:<br>This is the updated LCSPP dataset (v3.2), reconstructed using the MODIS record from 2001–2023. Previously referred to as "LCSIF," the dataset was renamed to emphasize its role as a SIF-informed long-term photosynthesis proxy derived from surface reflectance and to avoid confusion with directly measured SIF signals. The MODIS-based LCSPP is generated as an ancillary product to complement and benchmark the LCSPP-AVHRR product from 1982-2023.</p> <p>Key updates in version 3.2 include:</p> <ul> <li><strong>Improved Calibration</strong>: Enhanced consistency in calibration methods, addressing technical limitations in version 3.1 including applying more stringent quality filtering and snow masks.</li> <li><strong>Quality Flags</strong>: New quality flag layer enables users to identify whether a pixel is derived from observed surface reflectance (QA=0), high-quality gap-filled values (QA=1), lower-quality gap-filled based on the mean seasonal cycle (QA=2), or missing entirely (QA=3). We advice the user to rely only on observed and high-quality gap-filled values for their analyses.</li> <li><strong>Extension</strong> to include observations from the year of 2023.</li> </ul> <p>LCSPP-AVHRR repositories can be accessed via the following links:</p> <ul> <li>LCSPP-AVHRR v3.2 (1982-2000): <a href="https://doi.org/10.5281/zenodo.7916850" target="_blank" rel="noopener">10.5281/zenodo.7916850</a></li> <li>LCSPP-AVHRR v3.2 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11906675" target="_blank" rel="noopener">10.5281/zenodo.11906675</a></li> </ul> <p>The user can choose between LCSPP-AVHRR and LCSPP-MODIS for the overlapping period from 2001-2023. The two datasets are generally consistent during this overlapping period, although LCSPP-MODIS shows a stronger greening trend between 2001-2023. For studies exploring the long-term vegetation dynamics, the user can either use only LCSPP-AVHRR or use a blend dataset of LCSPP-AVHRR and LCSPP-MODIS as a sensitivity test. </p> <p>In addition, the updated long-term continuous reflectance datasets (LCREF), used for the production of LCSPP, can be accessed using the following links:</p> <ul> <li>LCREF-AVHRR v3.1 (1982-2023): <a href="https://doi.org/10.5281/zenodo.11905959" target="_blank" rel="noopener">10.5281/zenodo.11905959</a></li> <li>LCREF-MODIS v3.1 (2001-2023): <a href="https://doi.org/10.5281/zenodo.11657458" target="_blank" rel="noopener">10.5281/zenodo.11657458</a></li> </ul> <p>A manuscript describing the technical details is available at <a href="https://arxiv.org/abs/2311.14987" target="_blank" rel="noopener">https://arxiv.org/abs/2311.14987</a>, while detailed the uses and limitations of the dataset. In particular, we note that <strong>LCSPP</strong> <strong>is a reconstruction of SIF-informed photosynthesis proxy and should not be treated as SIF measurements</strong>. Although LCSPP has demonstrated skill in tracking the dynamics of GPP and PAR absorbed by canopy chlorophyll (APARchl), it is not suitable for estimating fluorescence quantum yield.</p> <p>All data outputs from this study are available at 0.05° spatial resolution and biweekly temporal resolution in NetCDF format. Each month is divided into two files, with the first file “a” representative of the 1<sup>st</sup> day to the 15<sup>th</sup> day of a month, and the second file “b” representative of the 16<sup>th</sup> day to the last day of a month.</p> <p><strong>Abstract:</strong></p> <p>Satellite-observed solar-induced chlorophyll fluorescence (SIF) is a powerful proxy for the photosynthetic characteristics of terrestrial ecosystems. Direct SIF observations are primarily limited to the recent decade, impeding their application in detecting long-term dynamics of ecosystem function. In this study, we leverage two surface reflectance bands available both from Advanced Very High-Resolution Radiometer (AVHRR, 1982-2023) and MODerate-resolution Imaging Spectroradiometer (MODIS, 2001-2023). Importantly, we calibrate and orbit-correct the AVHRR bands against their MODIS counterparts during their overlapping period. Using the long-term bias-corrected reflectance data from AVHRR and MODIS, a neural network is trained to produce a Long-term Continuous SIF-informed Photosynthesis Proxy (LCSPP) by emulating Orbiting Carbon Observatory-2 SIF, mapping it globally over the 1982-2023 period. Compared with previous SIF-informed photosynthesis proxies, LCSPP has similar skill but can be advantageously extended to the AVHRR period. Further comparison with three widely used vegetation indices (NDVI, kNDVI, NIRv) shows a higher or comparable correlation of LCSPP with satellite SIF and site-level GPP estimates across vegetation types, ensuring a greater capacity for representing long-term photosynthetic activity.</p> <p> </p>
The more the merrier - Multi-frequency magnetic susceptibility of loess and palaeosols as a sensitive climate proxy
<p>This data set (updated 2025) contains: </p> <p><strong>A) Data</strong></p> <p>1) Summary files of the central data sets as Microsoft Excel worksheets</p> <ul> <li>Multi-frequency DynoMag data for section Khonako-II (<em>KH-II_chiBFD.xlsx)</em></li> <li>Dual-frequency Kappabridge data for section Khonako-II (<em>KH-II_full_profile_Kappa.xlsx</em>)</li> </ul> <p>2) Temperature-dependent magnetic susceptibility data in folder<em> k(T).zip</em></p> <p>3) Remanence measurement data in folder<em> remanence.zip</em></p> <p>4) Temperature-dependent magnetic susceptibility data measured at three frequencies using a Quantum Design Magnetic Properties Measurement System (<em>SQUID.zip</em>)</p> <p>5) Multi-frequncy magnetic susceptibilty data for different sites (<em>MFMS_data.zip</em>)</p> <p>6) Raw data files of dual-frequency DynoMag data for section Khonako-II (<em>KH-II_section.zip)</em></p> <p><strong>B) R code files</strong></p> <p>1) R code to correct hysteresis loops from MPMS data for paramagentic contributions and to extract values of coercivity and exchange bias (<em>magnetic_hysteresis_suppl_mat.R</em>). The data files used in this code are stored in folder <em>mass-corrected_MPMS.zip.</em></p> <p>2) R code to calculate magnetic particle size distributions from multi-frequency magnetic susceptibility data measured on the Dynomag instument and temperature-dependent magnetic susceptibility data measured at three frequencies using a Quantum Design Magnetic Properties Measurement System (<em>particle_size_calculation.R</em>). The data files used in this code are stored in folder <em>particle_size_distribution.zip</em>.</p>
Paleoceanographic proxies obtained from core M77/2-024-5 off Peru
<p>This dataset includes paleoceanographic proxies developed in sediment core M77/2-024-5 (11°05.01'S; 78°00.91'W) collected at 210 meters depth off Peru during Meteor expedition M77/2 in 2008 (Meteor station No. M772/677). This dataset includes δ<sup>18</sup>O measurements, depth-age relationship, Uk’37 values, two records of ocean temperatures (alkenone and TEXH86), the difference between these two temperatures, δ<sup> 15</sup>N values (proxy for water column denitrification), diatom assemblages, fish vertebrae abundances and fluxes, the average and standard deviation of the width of fish vertebrae, the number of observations per sample and a preservation index of fish vertebrae. The δ<sup>18</sup>O record was obtained from at least 10 specimens of <em>Bolivina seminuda </em>per sampling interval<em>,</em> that did not show signs of degradation or alteration. According to the age model developed, core M77/2-024-5 covers the last 142 kyr BP including the last interglacial (MIS5e). The temperature difference between the UKˈ37 and the TEX86 can be attributed to changes in water column stratification, with higher offsets interpreted to indicate a deeper thermocline. The fish vertebrae identification includes the following species/groups: anchovy (<em>Engraulis ringens</em>), goby-like fishes, juvenile fishes, <em>Vinciguerria lucetia</em>, hake (<em>Merluccius gayi peruanus</em>), blue lanternfish (<em>Tarletonbeania crenularis</em>), eel-shaped fish and other unidentified species. The fish debris fluxes (#debris × cm−2 × y-1) were calculated by multiplying the fish debris abundance (number of fish debris x g-1) by the sedimentation rates (cm x y-1) by the dry bulk density (g x cm-3). Finally, the fluxes were multiplied by 1000. The widths of the complete vertebrae per sample were measured and the average per sample is presented in this dataset. The preservation index of fish vertebrae estimates the surface of the vertebrae that is not affected by bio-erosion. This index was calculated per each vertebra and an average per sample interval was done, where higher values indicate better preservation of the fish vertebrae remains.</p>
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.