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Figure 6 in Variations in trophic niches of generalist predators with plant community composition as indicated by stable isotopes and fatty acids
Figure 6. Principal components analysis of the relative abundance (mol%, logit- transformed) of individual PLFAs of soil microorganisms using flooding index (FI), plant species richness (SR), plant functional group richness (FG), presence of grasses (Gr), legumes (Leg), small herbs (SH) and tall herbs (TH) as supplementary variables.
Figure 2 in Variations in trophic niches of generalist predators with plant community composition as indicated by stable isotopes and fatty acids
Figure 2. Variations in δ15N signatures of Harpalus rufipes (P <0.01, R2 = 0.11) as affected by plant species richness (log-transformed).
Landsat-based Spectral Indices for pan-EU 2000-2022 - Annual predictor P50 (2001): Reflectances bands, NDVI and NDWI
<h2><strong>Data information</strong></h2> <p>This dataset provides the P50 (median) values of corresponding predictors for the year 2001. The median values are calculated from the bimonthly values of six corresponding predictors throughout the year. The predictors in this subset include seven reflectance bands: red, green, blue, NIR, SWIR1, SWIR2, and thermal; as well as two spectral indices: NDVI and NDWI.</p> <h2><strong>As a part of a Data Cube</strong></h2> <p>This data represents a subset of the <a href="../records/10776892">Time-series of Landsat-based Spectral Indices (EU, 30m) data cube</a>. For a comprehensive overview and full dataset information, please visit the landing page of this data cube using the provided link.</p> <ul> <li>To cite this dataset, refer to the DOI available on the landing page.</li> <li>To access other data layers in the data cube, use the navigation catalog on the landing page as well.</li> </ul> <h2><strong>Support</strong></h2> <p>If you discover a bug, artifact, or inconsistency, or if you have a question, please raise a <a href="https://github.com/AI4SoilHealth/SoilHealthDataCube/issues">Github Issue</a>!</p>
ERA5-Land selected indicators daily aggregates for the Latin America region, 2023
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land eight selected indicators, covering the Latin America region, for 2023.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u component of wind, 10m v component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
The data used for "Exploring how differences in dust particle size distribution and complex refractive indices affect dust direct radiative fluxes using the CAS-FGOALS-SPRINTARS global climate model"
<p>These data are used for " Exploring how differences in dust particle size distribution (PSD) and complex refractive indices (CRI) affect direct radiative effect (DRE) using the CAS-FGOALS-SPRINTARS global climate model ". </p> <p>(1) AS83+OPAC: The control experiment, dust PSD is the original AS83, and the generic CRI is from OPAC. </p> <p>(2) BFT22+OPAC: Same as the control experiment, but the PSD is updated to use BFT22.</p> <p>(3) BFT22+DB: Same as the experiment BFT22+OPAC, but the generic OPAC CRI is replaced by nine regionally dependent DB CRIs.</p> <p>(4) BFT22+DB strong abs: Same as the experiment BFT22+DB, but the generic CRI consists of 10% percentile real and 90% percentile imaginary parts and no regional dependencies.</p> <p>(5) BFT22+DB weak abs: Same as the experiment BFT22+DB, but the generic CRI consists of 90% percentile real and 10% percentile imaginary parts and no regional dependencies.</p> <p>All experiments mentioned above are run for 5 years (2010-2014). The annual average simulation results are stored here.</p> <p><strong>Note:</strong> AS83 represents the dust PSD scheme from d'Almeida and Schütz. (1983). BFT22 represents the new dust PSD developed by Meng et al. (2022) based on the improved brittle fragmentation theory. OPAC: the Optical Properties for Aerosols and Clouds dataset, DB: the CRIs from Di Biagio et al. (2017, 2019).</p> <p><strong>References</strong></p> <p>d'Almeida, G. A., & Schütz, L. (1983). Number, Mass and Volume Distributions of Mineral Aerosol and Soils of the Sahara. <em>Journal of Applied Meteorology and Climatology</em>,<em> 22</em>(2), 233-243. https://doi.org/https://doi.org/10.1175/1520-0450(1983)022<0233:NMAVDO>2.0.CO;2</p> <p>Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2019). Complex refractive indices and single-scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content. <em>Atmospheric Chemistry and Physics</em>,<em> 19</em>(24), 15503-15531. https://doi.org/10.5194/acp-19-15503-2019</p> <p>Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2017). Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing. <em>Atmospheric Chemistry and Physics</em>,<em> 17</em>(3), 1901-1929. https://doi.org/10.5194/acp-17-1901-2017</p> <p>Meng, J., Huang, Y., Leung, D. M., Li, L., Adebiyi, A. A., Ryder, C. L., et al. (2022). Improved Parameterization for the Size Distribution of Emitted Dust Aerosols Reduces Model Underestimation of Super Coarse Dust. Geophysical Research Letters, 49(8), e2021GL097287, https://doi.org/https://doi.org/10.1029/2021GL097287</p>
Figure 2 in Effects of methyl farnesoate injection on spermatozoa number and reproductive indices in the narrow-clawed crayfish Pontastacus leptodactylus
Figure 2. Effect of MF injection on reproductive system weight and GSI in male Pontastacus leptodactylus. Letters indicate significant difference groupings (P <0.05) (mean ± S.D; n = 15).
Fig.5 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.5. Jamesoniella autumnalis distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Fig.3 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.3. Neckera pennata distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzemes, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Fig.1 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.1. Anomodon longifolius distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical reģions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumnkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J-upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N-upland Latgale, O-Austrumlatgale.
Fig.4 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.4. Lejeunea cavifolia distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
Fig.2 in Distribution Of Five Interesting Woodland Key Habitat Bryophyte Indicator Species In Latvia
Fig.2. Homalia trichomanoides distribution in Geobotanical regions of Latvia in 5x5 km square network. (Latvian State Forest Service data (circle), personal database of Anna Mežaka (triangle), personal data base of Sanita Putna (square)). Geobotanical regions (Ramans 1994): A-Piejūra, B-Kursa, C-Ventas land, D - Austrumkursa, E-Rietumzemgale, F-Austrumzemgale, G-Dienvidvidzeme, H-Ziemeļvidzeme, I-Gaujas land, J- upland Vidzeme, K-Austrumvidzeme, L-Aiviekstes land, M-Augšzeme, N- upland Latgale, O-Austrumlatgale.
High rates of nectar depletion in summer grasslands indicate competitive conditions for pollinators
<p>Competition among pollinators for floral resources is a phenomenon of both basic and applied importance. While competition is difficult to measure directly under field conditions, it can be inferred indirectly through the measurement of floral resource depletion. In this study, we conducted a pollinator exclusion experiment to calculate nectar depletion rates in summer across 16 grassland sites in the German regions of Franconia and Saxony-Anhalt. Overall depletion rates were estimated at 95% in Franconia and 79% in Saxony-Anhalt, indicating strong nectar limitation and likely competition among pollinators for nectar. Despite being ubiquitous in our study regions, honey bees were scarce at our sites at the time of nectar sampling. This demonstrates that wild pollinators alone are capable of massive nectar depletion, and the addition of managed honey bees to wild pollinator communities may intensify already competitive conditions. Nevertheless, the manifest diversity of the pollinator communities at our sites indicates that other factors, such as non-trophic constraints or temporal variation in nectar limitation, can mitigate competitive exclusion despite immediate conditions of acute nectar scarcity. </p>
Historical baleen plates indicate that once abundant Antarctic blue and fin whales demonstrated distinct migratory and foraging strategies
<p>Southern hemisphere blue (<em>Balaenoptera musculus intermedia</em>) and fin (<em>Balaenoptera physalus</em>) whales are the largest predators in the Southern Ocean, with similarities in morphology and distribution. Yet, understanding of their life history and foraging is limited due to current low abundances and limited ecological data. To address these gaps, historic Antarctic blue (n = 5) and fin (n = 5) whale baleen plates, collected in 1947–1948 and recently rediscovered in the Smithsonian National Museum of Natural History, were analyzed for bulk (δ<sup>13</sup>C and δ<sup>15</sup>N) stable isotopes. Regular oscillations in isotopic ratios, interpreted as annual cycles, revealed that baleen plates contain approximately six years (14.35 ± 1.20 cm yr<sup>–1</sup>) of life history data in blue whales and four years (16.52 ± 1.86 cm yr<sup>–1</sup>) in fin whales. Isotopic results suggest that: 1) in the 1940s, blue and fin whales fed at the same trophic level but in slightly different habitats, 2) fin whales appear to have had more regular annual migrations, and 3) fin whales may have migrated to ecologically distinct sub-Antarctic waters annually while some blue whales may have resided year-round in the Southern Ocean. These results reveal differences in ecological niche and life history strategies between Antarctic blue and fin whales during a period when their populations were more abundant than today, and before major human-driven climatic changes occurred in the Southern Ocean.</p>
→ Fig. 2. Marellomorph arthropod Mimetaster florestaensis sp. nov. from Tremadocian of Mojotoro Mountains, Salta, Argentina. A–C. CNS-I 133/1-1, part. A. Cephalic shield and spines. Detail of the secondary spines on mediolateral spine (A2). B. View of the imprint of the ventral posterior margin of the cephalic shield. C. Explanatory drawing revealing the most important morphological characters. D. CNS-I 133/1-1´, counterpart showing detail of strong secondary spines on anterolateral spine. Arrows indicate the secondary spines. in A new marrellomorph euarthropod from the Early Ordovician of Argentina
→ Fig. 2. Marellomorph arthropod Mimetaster florestaensis sp. nov. from Tremadocian of Mojotoro Mountains, Salta, Argentina. A–C. CNS-I 133/1-1, part. A. Cephalic shield and spines. Detail of the secondary spines on mediolateral spine (A2). B. View of the imprint of the ventral posterior margin of the cephalic shield. C. Explanatory drawing revealing the most important morphological characters. D. CNS-I 133/1-1´, counterpart showing detail of strong secondary spines on anterolateral spine. Arrows indicate the secondary spines.
РИС. 3. Место нахождениЯ Amuranodonta kijaensis в Хабаровском крае: А. Карта-схема краЯ. В. Приустьевый участок р. Амур. С, D. ТопографическаЯ карта и спутниковый снимок с. Чныррах с укаЗанием места сбора. МасШтабные линейки: 200 км (А), 16 км (B), 4 км (C) и 200 м (D). FIG. 3. Locality of Amuranodonta kijaensis in the Khabarovsk Territory: A. Scheme map of the region. B. Amur River mouth area. C, D. Topographic map and satellite image of Chnyrrakh village indicating the collection site. Scale bars: 200 km (А), 16 km (B), 4 km (C), and 200 m (D). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 3. Место нахождениЯ Amuranodonta kijaensis в Хабаровском крае: А. Карта-схема краЯ. В. Приустьевый участок р. Амур. С, D. ТопографическаЯ карта и спутниковый снимок с. Чныррах с укаЗанием места сбора. МасШтабные линейки: 200 км (А), 16 км (B), 4 км (C) и 200 м (D). FIG. 3. Locality of Amuranodonta kijaensis in the Khabarovsk Territory: A. Scheme map of the region. B. Amur River mouth area. C, D. Topographic map and satellite image of Chnyrrakh village indicating the collection site. Scale bars: 200 km (А), 16 km (B), 4 km (C), and 200 m (D).
РИС. 1. Место нахождениЯ Amuranodonta kijaensis в Зейском районе, АмурскаЯ обл.: А. Карта-схема области. B. Зейское вдхр. С, D. ТопографическаЯ карта и спутниковый снимок Залива в Западной части Зейского вдхр. у пос. Береговой с укаЗанием места сбора. МасШтабные линейки: 300 км (А), 50 км (В), 4 км (С) и 200 м (D). FIG. 1. Locality of Amuranodonta kijaensis in Zeya District, Amur Region: A. Schematic map of the region. B. Zeya Reservoir. C, D. Topographic map and satellite image of the bay in the western part of Zeya Reservoir near Beregovoi village indicating the collection site. Scale bars: 300 km (А), 50 km (В), 4 km (С), and 200 m (D). in Новые данные об охранЯемом пресноводном двустворчатом моллюске Amuranodonta kijaensis Moskvicheva, 1973 (Unionidae, Anodontinae)
РИС. 1. Место нахождениЯ Amuranodonta kijaensis в Зейском районе, АмурскаЯ обл.: А. Карта-схема области. B. Зейское вдхр. С, D. ТопографическаЯ карта и спутниковый снимок Залива в Западной части Зейского вдхр. у пос. Береговой с укаЗанием места сбора. МасШтабные линейки: 300 км (А), 50 км (В), 4 км (С) и 200 м (D). FIG. 1. Locality of Amuranodonta kijaensis in Zeya District, Amur Region: A. Schematic map of the region. B. Zeya Reservoir. C, D. Topographic map and satellite image of the bay in the western part of Zeya Reservoir near Beregovoi village indicating the collection site. Scale bars: 300 km (А), 50 km (В), 4 km (С), and 200 m (D).
РИС. 1. ОЗеро Дальнее (А) и место обнаруЖениЯ Beringiana beringiana в оЗере (B, С), песчаный грунт в месте обитаниЯ беЗЗубок (D). Стрелками обоЗначено место сбора моллюсков. FIG. 1. Lake Dalneye (A) with the sampling site (B, C), sandy bottom in the habitat of mollusks (D). The arrows indicate the site of material sampling. in Первые данные о морфологии глохидиев двустворчатых моллюсков Beringiana beringiana (Bivalvia, Unionidae) оЗера Дальнее, Камчатка
РИС. 1. ОЗеро Дальнее (А) и место обнаруЖениЯ Beringiana beringiana в оЗере (B, С), песчаный грунт в месте обитаниЯ беЗЗубок (D). Стрелками обоЗначено место сбора моллюсков. FIG. 1. Lake Dalneye (A) with the sampling site (B, C), sandy bottom in the habitat of mollusks (D). The arrows indicate the site of material sampling.
РИС. 3. Раковины Зрелых глохидиев Beringiana beringiana в раЗных ракурсах: А–С – Закрытые раковины, вид со стороны створки (А), вентрального угла (В) и лигамента (С); D–F – полностью открытые раковины, вид иЗнутри (D), снаруЖи (E) и боковых краев створок (F); G, H – приоткрытые раковины, стрелка укаЗывает на остатки волокон мускулаЗамыкателЯ. МасштабнаЯ линейка 100 мкм. СканируюЩаЯ ЭлектроннаЯ микроскопиЯ. FIG. 3. Mature glochidial shells of Beringiana beringiana from different angles of view: A–C – closed shells, view from the valve side (A), ventral angle (B), and ligament (C); D–F – open shells, interior view (D), exterior view (E), and from lateral margins of valves (F); G, H – ajar shells, the arrows indicate the remains of the adductor muscle fibers. Scale bar 100 µm. Scanning electron microscopy. in Первые данные о морфологии глохидиев двустворчатых моллюсков Beringiana beringiana (Bivalvia, Unionidae) оЗера Дальнее, Камчатка
РИС. 3. Раковины Зрелых глохидиев Beringiana beringiana в раЗных ракурсах: А–С – Закрытые раковины, вид со стороны створки (А), вентрального угла (В) и лигамента (С); D–F – полностью открытые раковины, вид иЗнутри (D), снаруЖи (E) и боковых краев створок (F); G, H – приоткрытые раковины, стрелка укаЗывает на остатки волокон мускулаЗамыкателЯ. МасштабнаЯ линейка 100 мкм. СканируюЩаЯ ЭлектроннаЯ микроскопиЯ. FIG. 3. Mature glochidial shells of Beringiana beringiana from different angles of view: A–C – closed shells, view from the valve side (A), ventral angle (B), and ligament (C); D–F – open shells, interior view (D), exterior view (E), and from lateral margins of valves (F); G, H – ajar shells, the arrows indicate the remains of the adductor muscle fibers. Scale bar 100 µm. Scanning electron microscopy.
A dataset of three vine water status indicators, weather records and soil water capacity components collected from a rain-fed Mediterranean vineyard
<p>This dataset contains three key indicators of vine water status: vine shoot growth index (iG-Apex), predawn leaf water potential (Ψpd), and carbon isotope ratio (δ13C). Additionally, it includes weather data and soil measurements. Spatial data files of studied fields, plots, and some vines’ locations are also provided. The data were collected from a rain-fed vineyard in Southern France, 4 km north of the Mediterranean Sea. Measurements were made at the plot level, with each plot consisting of 10 adjacent grapevines (Vitis Vinifera). The iG-Apex was recorded weekly from June 10 to August 22, 2022, across 70 vine plots. Ψpd was measured weekly between 3 a.m. and 5 a.m. in 12 of these plots using a pressure chamber. On August 23, 2022, 100 berries were sampled from each plot, and 1.5 mL of grape juice was extracted for δ13C analysis using a carbon analyzer and mass spectrometer. The dataset contains 761 iG-Apex measurements (70 time series), 720 Ψpd measurements (60 time series), and 70 single-date δ13C measurements. Weather data were recorded daily in 2022 from a weather station located at the vineyard's center, providing five parameters: cumulative rainfall, relative humidity, and three air temperatures (mean, minimum, and maximum). Soil available water capacity components (horizon thickness, field capacity, permanent wilting point, bulk density, and rock fragment content) were measured in 5 of the 70 plots after prior soil profile wall analyses. Monitoring vine water status is essential for optimizing grape yield and wine quality. While Ψpd and δ13C are considered reference methods, they are expensive and prone to logistical constrains. In contrast, iG-Apex can be collected with minimal time and financial investment. This dataset enables not only the exploration of statistical relationships between the plant-based and soil-based indicators, but also the modeling of Ψpd and/or δ13C using iG-Apex, while accounting for weather and soil influences. All data were georeferenced, allowing future integration of ancillary spatial data sources, like multi-spectral remote sensing images or yield data.</p>
Data repository for "Genesis and timing of KREEP-free lunar Mg-suite magmatism indicated by the first norite meteorite Arguin 002"
<p>This is the data repository for paper entitled "Genesis and timing of KREEP-free lunar Mg-suite magmatism indicated by the first norite meteorite Arguin 002". This data repository includes two EXCEL (.xlsx) files representing the dataset necessary to interpret, replicate and build upon the methods or findings reported in the article.</p> <p>Regarding the EXCEL file named "Supplementary Data 1", it incorporates the mineral EPMA compositions (Table S1), mineral trace-element compositions (Table S2), bulk chemistry (Table S3), SIMS U-Pb results (Table S4), and mineral modal abundances and the launch-region identification results (Table S5) in the comprehensive study of the lunar norite meteorite, Arguin 002.</p> <p>Regarding the EXCEL file named "Supplementary Data 2", it incorporates analyses of reference materials in EPMA (Table S1), LA-ICP-MS (Table S2), ICP-MS (Table S3), and SIMS (Table S4).</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.