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342 results for “Proxies”
Four organic sea surface temperature proxies (UK'37, TEXH86, RI-OH' and LDI) in the westernmost Mediterranean for the last 35 kyr
<p>We present a high-resolution paleotemperature reconstruction from a marine sediment core (GP04PC) recovered in the westernmost Mediterranean, the Alboran Sea basin, over the last 35 kyr using for the first time in pararell four independent organic sea surface temperature (SST) proxies (U<sup>K'</sup><sub>37</sub>,<sup> </sup>TEX<sup>H</sup><sub>86</sub>,RI-OH' and LDI). We also present the δ<sup>18</sup>O of planktonic foraminifera <em>G. bulloides</em> record together with records of bulk parameters (total organic carbon content, δ<sup>13</sup>C<sub>org</sub>) and the accumulation rates of different biomarkers, providing insights in terrestrial input and primary productivity variations. </p> <p>We have also examined the Bayesian calibrations BAYSPLINE for U<sup>K'</sup><sub>37 </sub>and BAYSPAR for TEX<sub>86</sub>, although the non-Bayesian calibrations are used in this study.</p> <p> </p> <p> </p>
Contrasting sensitivity of weathering proxies to Quaternary climate and sea-level fluctuations on the southern slope of the South China Sea
<p>Tropical marginal seas host important sedimentary archives that may be exploited to reveal past changes in continental erosion, chemical weathering, and ocean dynamics. However, these records can be challenging to interpret due to the complex interactions between climate and particulate transport across ocean margins. For the southern South China Sea over the last 90 kyr, we observe a contrasting temporal relationship between the deposition of clay minerals and magnetic minerals, which were associated with two different hydrodynamic modes. Fine-grained clay minerals can be carried in suspension by ocean currents, leading to a rapid response to regional climate-driven inputs. In contrast, changes in magnetic mineralogy were linked to glacial-interglacial sea-level variability, from which we infer a control by bedload transport and resuspension. Overall, this study indicates that the transfer pathways and mechanisms imparted by varying hydrodynamic conditions exert a substantial influence on the distribution of terrigenous material in continental margin sediments.</p>
Data supporting the study "The evolution of surface structure during atmospheric ageing of nano-scale coatings of an organic surfactant aerosol proxy" by Milsom et al.
<p>Reduced neutron reflectometry (NR) data associated with the study "The evolution of surface structure during atmospheric ageing of nano-scale coatings of an organic surfactant aerosol proxy" by Milsom et al.. One folder contains the raw data for fitted parameters obtained from NR curves and supporting figure 3 in the study. The other contains a set of sub-folders which have reduced NR data along with python scripts which were used to create and fit the interfacial model to the data. Fitting bounds for each parameter are found in these scripts. </p>
Data assimilation products by using multiple climate model simulations and different combinations of proxies
<p>This dataset of the climate reconstruction by data assimilation using isotope ratios provides annual surface air temperature, precipitation amount, and other climate variables during 850–2000.</p> <p>Two isotopes-incorporated atmospheric general circulation models and 129 isotopic proxy data (65 corals, 43 ice cores, and 21 tree-ring cellulose) were used in this study. There are nine experiments using three type of simulations and three combinations of proxies.</p> <p>The associated publication: Shoji, S., Okazaki, A., & Yoshimura, K. (2020). Impact of proxies and prior estimates on data assimilation using isotope ratios for the climate reconstruction of the last millennium. (submitted to Earth and Space Science)</p> <p>[Data structure]<br> X(lon) x Y(lat) x Z(2) x Variables(8) x Year(1151)<br> Z(1): analyses<br> Z(2): priors</p>
Annexes numériques de la thèse de doctorat "Comportements alimentaires et mobilité au Néolithique. Analyses biogéochimiques multi-proxies de restes bioarchéologiques de la vallée de l'Yonne (Ve millénaire av. J.-C., France)"
<p><strong>Annexe N1_carte_geol_Sr</strong> : Carte de France (Géoportail) permettant un grossissement interactif de la zone d’intérêt, illustrant les principales unités géologiques et la localisation des sites de notre corpus dans la vallée de l’Yonne entourés d’un rayon de 100 km dans lequel sont indiqués les prélèvements Sr de la base de données iRhum (Willmes <em>et al.</em>, 2013) utilisés comme valeurs de référence dans ce travail.</p> <p><strong>Annexe N2_Profiles_pictures</strong> (dossier): Tracés des profils réalisés sur chaque échantillon humain et animal d’émail et d’os pour l’ablation laser, d’après photographie des moulages. Photographies et DAO : L. Rey.</p> <p><strong>Annexe N3_Raw_data_Sr et TE_Python</strong> (dossier) : Totalité des fichiers de <strong>résultats bruts</strong> obtenus par ablation laser pour chaque séquence (LR_X) et (LR_X_SEQ) ainsi que les scripts Python-Spyder nécessaires à l’extraction et à la correction des données. Le dossier Sr comprend les données obtenues par LA-MC-ICPMS pour le strontium isotopique (isotopes du Sr, Rb et Kr) et le dossier TE comprend les données obtenues par LA-Q-ICPMS pour les éléments traces (ces fichiers comprennent les mesures de 65 isotopes de 41 éléments dont ceux d’intérêt : Sr, Ba, Ca, Mg, Mn et U).</p> <p><strong>Annexe N4_Scripts</strong> (dossier) : Ensemble des <strong>scripts nécessaires au traitement des données</strong> acquises par ablation laser. 1.script_process servant au traitement et à la correction des données brutes (sous Python) ; 2.script_extraction servant à l’extraction des données d’intérêt au format .csv (sous R) ; 3.script_gener_moyIndiv servant à la création d’un fichier synthétisant l’ensemble des données moyennes « propres » par individu (sous R) ; 4.script_gph_profils servant à la création des courbes de données pour chaque profil et chaque individu (sous R) ; 5.script_correl servant à la création des matrices et graphiques de corrélation entre proxies biogéniques et diagénétiques pour chaque profil (sous R). Les fichiers sources correspondant sont disponibles en Annexes N3 (Python) et N8 (R, .csv).</p> <p><strong>Annexe N5_Data_out_Sr et TE</strong> (dossier) : Totalité des fichiers de <strong>résultats corrigés</strong> (correction semi-automatisée avec le logiciel Python-Spyder 2.7) obtenus par ablation laser pour chaque séquence (LR_X_processed). <strong>Le dossier Sr</strong> comprend les données relatives à l’étude du Sr isotopique dont le rapport <sup>87</sup>Sr/<sup>86</sup>Sr calibré par encadrement des standards « bracketing calibration » et corrigé par rapport au <sup>85</sup>Rb et <sup>83</sup>Kr (nommé « Sr8786_RbKrcorr_brack ») et le signal contrôle <sup>88</sup>Sr). <strong>Le dossier TE</strong> comprend les données relatives à l’étude des éléments trace, dont les données sont exprimées relativement à l’abondance naturelle de l’isotope considéré (T_isotope), relativement au Ca (R_T_ isotope) et corrigées par encadrement des standards « bracketing calibration » (Corr_R_T_isotope). Ces fichiers comprennent les mesures de 65 isotopes de 41 éléments dont seuls Sr, Ba, Ca, Mg, Mn, U, Rb, Fe, Zn, Pb et Th ont été corrigés. Les tables de calibration relative par rapport à la position de l’échantillon ainsi que l’ordre des échantillons et standards de chaque séquence sont données dans les derniers onglets de chaque fichier.</p> <p><strong>Annexe N6_Global_File_Sr et TE</strong> : Fichiers (.csv) comprenant l’ensemble des valeurs corrigées des profils d’échantillons et des valeurs de standards pour <sup>87</sup>Sr/<sup>86</sup>Sr et <sup>88</sup>Sr (ce dernier non corrigé) dans l’un (« fichier_global_Sr.csv ») et Sr/Ca, Ba/Ca, Mg/Ca, Mn/Ca (non corrigé), U/Ca, <sup>88</sup>Sr (non corrigé) et <sup>43</sup>Ca (non corrigé) dans l’autre (« fichier_global_TE.csv »).</p> <p><strong>Annexe N7_TOT_profiles</strong> : Fichier Excel comprenant 4 onglets pour les éléments traces dans l’émail (TE_enamel) et dans l’os (TE_bone) et pour le Sr isotopique dans l’émail (Sr_enamel) et dans l’os (Sr_bone) avec <strong>l’ensemble des données pour chaque profil d’échantillon</strong> - les valeurs en rouges sont celles qui sont supprimées lors du nettoyage de la diagénèse - ainsi que les valeurs moyennes, l’écart-types et la quantité de données de chaque profil total (TOT) et nettoyé (select). Il a été démontré par la suite que les valeurs dans l’os et les valeurs <sup>87</sup>Sr/<sup>86</sup>Sr ne doivent pas être interprétés directement en termes biogéniques.</p> <p><strong>Annexe N8_csv_source</strong> (dossier) : Fichiers sources au format .csv de l’ensemble des valeurs corrigées pour chaque profil dans l’os (_bone) et dans l’émail (_enamel) pour le Sr isotopique (profilesSr) et les éléments traces (profilesTE). Les fichiers « TOT_ » comprennent les données des profils totaux (avant nettoyage de la diagénèse) et les fichiers « Nett_ » les données des profils nettoyés de la diagénèse (sans les valeurs en rouge du fichier « TOT_profils »). Ces fichiers sont ceux qui peuvent être directement utilisés pour l’analyse statistique des résultats. Attention, il a été démontré par la suite que les valeurs nettoyées dans l’os et les valeurs <sup>87</sup>Sr/<sup>86</sup>Sr ne doivent pas être interprétés directement en termes biogéniques.</p> <p><strong>Annexe N9_Curves</strong> (dossier) : <strong>Courbes des valeurs</strong> de Sr/Ca, Ba/Ca, Mg/Ca, U/Ca, Mn/Ca et <sup>44</sup>Ca pour l’étude des éléments traces et de <sup>87</sup>Sr/<sup>86</sup>Sr et <sup>88</sup>Sr pour l’étude du Sr isotopique de <strong>chaque profil</strong> dans l’émail (<strong>a</strong>) et dans l’os (<strong>b</strong>) et pour <strong>chaque individu</strong> dans l’émail (<strong>c</strong> : les profils d’un même individu sont mis bout à bout et séparés par une ligne verticale pointillée – attention les profils ne sont pas nécessairement jointifs et la ligne peut correspondre à une lacune de plusieurs milliers de points ; se référer aux tracés des profils sur photographies de l’Annexe N2_Profiles_pictures). Les courbes de droite correspondent aux profils nettoyés de la diagénèse. Les profils TE et Sr sont calés approximativement, en fonction de la longueur totale du profil, en µm (les profils TE et Sr n’ayant pas le même nombre de points – le nombre de points est converti en µm). Les profils commencent de l’extrémité occlusale jusqu’au collet pour les humains et les cochons, ils sont inversés (du collet à l’extrémité occlusale) pour les herbivores (cf. Annexe N2). Il a été démontré par la suite que les valeurs dans l’os et les valeurs <sup>87</sup>Sr/<sup>86</sup>Sr ne doivent pas être interprétées directement en termes biogéniques.</p> <p><strong>Annexe N10_Correl</strong> (dossier) : Graphiques et matrices de corrélation entre les rapports Sr/Ca et Ba/Ca biogéniques et les rapports Mn/Ca, U/Ca et Mg/Ca diagénétiques pour les profils totaux (TOT) et nettoyés (Nett), dans l’émail (enamel) et dans l’os (bone). Les matrices de corrélation indiquent les coefficients de corrélation de Pearson (les valeurs les plus proches de 0 sont les moins corrélées).</p>
Data Archive for "Acceleration as a proxy for energy expenditure in a facultative-soaring bird: comparing dynamic body acceleration and time-energy budgets to heart rate"
<p>Heart rate, acceleration, and respirometry data from four wild-caught gulls during climate chamber and treadmill calibration measurements (2018), as well as heart rate and acceleration data from five free-ranging gulls from a colony on Texel, NL during the breeding season (May - July, 2019). </p> <p> </p>
Shorter CWR cycling tests as proxies for longer tests in highly trained cyclists
<p class="MsoNormal">Severe-intensity constant work rate (<span>CWR) cycling tests </span>simulate the high-intensity competition environment<span> and are </span>useful for monitoring training progression and adaptation, yet <span>impose significant </span>physiological and psychological strain, require substantial recovery, and may disrupt athlete <span>training or competition preparation.</span> A brief, minimally fatiguing test providing comparable information is desirable.<strong><em> </em></strong></p> <p class="MsoNormal"><strong>Purpose</strong>: To determine whether physiological variables measured during, and functional decline in maximal power output immediately after, a <span>2-min</span> CWR test can act as a proxy for <span>4-min</span> test outcomes.</p> <p class="MsoNormal"><strong>Methods</strong>: <span>Physiological stress (</span><span>V̇O<sub>2</sub> kinetics, heart rate, blood lactate concentrations </span>([La<sup>-</sup>]<sub>b</sub>)<span>) </span><span>was monitored and performance fatigability was estimated (</span>as p<span>re-to-post-CWR changes in 10-s sprint power) during 2- and 4-min CWR tests in 16 high-level cyclists (</span>V̇O<sub>2peak</sub>=64.4±6.0 ml∙kg<sup>-1</sup>∙min<sup>-1</sup>). <span>The relationship between the 2- and 4-min CWR tests and the physiological variables that best relate to the performance fatigability were investigated.</span></p> <p class="MsoNormal"><strong>Results</strong>: The 2-min CWR test evoked a smaller decline in sprint mechanical power (32% vs. 47%, <em>p</em><0.001). Both the physiological variables (r=0.66-0.96) and sprint mechanical power (r=0.67-0.92) were independently and strongly correlated between 2- and 4-min tests. Differences in <span>V̇O<sub>2peak</sub></span> and [La<sup>-</sup>]<sub>b</sub> in both CWR tests were strongly associated with the decline in sprint mechanical power.</p> <p class="MsoNormal"><strong>Conclusion</strong>: Strong correlations between 2- and 4-min severe-intensity CWR test outcomes indicated that the shorter test can be used as a proxy for the longer test. A shorter test may be more practical within the elite performance environment due to lower physiological stress and performance fatigability and should have less impact on subsequent training and competition preparation.</p>
Multi-proxy paleolimnological records provide evidence for a shift to a new ecosystem state in the Northern Great Plains, USA
<p>Wetlands in the Prairie Pothole Region of the North American Northern Great Plains perform multiple ecosystem services and are biodiversity hotspots. However, climatological changes can result in sudden shifts in these important ecosystems. For example, marked increases in precipitation in the last few decades have resulted in a widespread shift in wetlands across the Prairie Pothole Region to a new ecohydrological state. We used multi-proxy analyses (diatom community composition and invertebrate stable isotopes) of 210Pb-dated sediment cores from two adjacent, but morphologically and hydrologically different, prairie-pothole wetlands to assess the effects of hydroclimatic variability on these wetland ecosystems. Our results provide evidence that the recent ecohydrological shift in the region's wetlands is unprecedented over the past ca.178 years. Oxygen stable isotopes in chironomid head capsules provide a record of paleo-hydrology changes. The most recent sediments (i.e., those deposited after the state shift) from both wetlands revealed novel changes in diatom communities that differed greatly from earlier community compositions. Additionally, a depleted signal in deuterium and 13C carbon stable isotopes observed in chironomid head capsules and Daphnia ephippia, respectively after 1993 is likely related to an increase in methane production in these wetlands. Our study highlights the importance of considering basin morphometry including whether a wetland has an overflow point, and multiple biological indicators to study climate-change influences on freshwater ecosystems. Research using these techniques can lead to an improved understanding of recent ecosystem shifts, an understanding that will be essential for future climate-change adaptation and mitigation in this ecologically important region.</p>
NGHP-01 Site 19 (Mahanadi Basin, Bay of Bengal) multi-proxy paleoenvironmental record since 110 ka
<p>In this dataset we include carbon and nitrogen elemental and isotopic (TOC, TN, CaCO3, d<sup>13</sup>C-TOC, d<sup>15</sup>N-TN), biogenic silica, X-ray fluorescence (XRF), grain size, physical property core logs, benthic foraminifer O and C isotopes, planktic foraminifer radiocarbon ages, and mass accumulation rate data from India National Gas Hydrate Program Expedition 01 Hole 19B. Hole 19B is located in the Mahanadi Basin ~70 km off the eastern Indian Margin in 1422 m water depth (18° 58.65680' N, 85° 39.52020' E). These data are from the upper 12 m below seafloor, corresponding to the last 110 kyr.</p> <p>Details of these analyses are published in:</p> <p><strong>Phillips, S.C., Johnson, J.E., Giosan, L., and Rose, K., 2014. Monsoon-influenced variation in productivity and lithogenic sediment flux since 110 ka in the offshore Mahanadi Basin, northern Bay of Bengal. <em>Marine and Petroleum Geology</em> 58A, 502-525, https://doi.org/10.1016/j.marpetgeo.2014.05.007</strong> <a href="https://www.sciencedirect.com/science/article/pii/S0264817214001809">Link</a></p>
Stable water isotopes reveal the onset of bud dormancy in temperate trees, whereas water content is a better proxy for dormancy release
<p><span>Earlier spring growth onset in temperate forests is a visible effect of global warming and affects global water and carbon cycling. Therefore, it is crucial to accurately predict the shift in spring phenology under projected future warming. However, current phenological models lack physiological information and are rarely experimentally validated.</span><span> </span><span>Therefore, twig cuttings of five deciduous tree species were sampled at two climatically different sites throughout the winter of 2019/2020. Twig budburst success, thermal time to budburst, bud water content, and short-term <sup>2</sup>H-labelled water uptake into buds were quantified to link bud dormancy status with vascular water transport efficacy.</span><span> We found strong <sup>2</sup>H-labelled water uptake into buds during leaf senescence, followed by a sharp decrease that we attributed to the initiation of dormancy. However, we did not find increasing <sup>2</sup>H-labelled water uptake into buds with progression of winter, whereas all species showed a linear relationship between bud water content and dormancy status. Our results show that short term <sup>2</sup>H-labelled water uptake appears to be a poor tracer of dormancy release, but could be a promising method to track dormancy induction of deciduous trees, whereas bud water content seems to be an inexpensive and more reliable indicator of dormancy release. </span></p>
Dataset for Paul et al., "Trace metals in coastal marine sediments: Natural and anthropogenic sources, correlation matrices, and proxy potentials", Science of The Total Environment (STOTEN-175789, 2024)
<p>The datafile (.xlsx format) contains the solid phase data for all study sites presented in the associated STOTEN publication <a href="https://doi.org/10.1016/j.scitotenv.2024.175789" target="_blank" rel="noopener">(10.1016/j.scitotenv.2024.175789)</a></p> <ol> <li>specifics on the ICP-MS and ICP-OES analysis</li> <li>solid-phase data (Pb, Cd, Cu, Zn, Sb, Sn, Ni, As, Tl, V, Mo, U, Re, TOC, S, Fe, Mn, and Al)</li> <li>coefficient of variation test results (as a basis for evaluating the applicability of data normalizations to the data; i.e. calculation of Enrichment Factors, cf. method section of the original publication)</li> </ol> <p>Please note that the title of the paper and the publication number has changed upon final publication. The Excel file description still refers to the original manuscript number and title "Trace metals in coastal marine sediments: anthropogenic sources, correlation patterns, and proxy potentials".</p>
Tree rings as a proxy for seasonal precipitation variability and Early Neolithic settlement dynamics in Bavaria, Germany
<p>The file contains original measurements of total ring width (TRW) of 540 subfossil oak trees from the Main region (southern Germany) spanning the period from 6228 to 4057 BCE (2172 years).</p> <p>Latitude: 49.6-50.2°N</p> <p>Longitude: 10.2-11.2°E</p> <p>Resolution of measurement: 0.01 mm</p> <p>File format: Tucson (.tuc)</p>
Microclimate proxy measurements from a logging gradient in Malaysian Borneo (BALI project)
<b>Description: </b><p>Overview<br>We focused our empirical measurements in Sabah, Borneo. We measured a microclimate thermal proxy sensitive to radiative, convective, and conductive heat fluxes. This thermal proxy is relevant to a range of organismal processes including plant regeneration, animal behavior, and soil nutrient fluxes. Measurements were made across space and time within three 1-ha plots comprising a gradient from old growth to heavy selective logging. We then combined these data with nearby weather station air temperature data, as well as measurements of topography and canopy structure derived from detailed ground surveys and airborne LIDAR.<br>Sampling design<br>Study location<br>We identified three sites along a logging intensity gradient in the Malaysian state of Sabah in Borneo. Sites each contain a 1-ha plot in lowland mixed dipterocarp forest. The plots are chosen to contrast an unlogged old growth forest with a moderately logged forest and a heavily logged forest <br>Microclimate<br>To map the microclimate thermal proxy in each plot, we installed dataloggers on a semi-regular grid pattern varying in minimum distance from 1 to 14 meters. Each datalogger was a Thermochron iButton (DS1921G, Maxim), capable of logging up to 2048 temperature values between -30°C and 70°. Each datalogger was waterproofed by wrapping in plastic paraffin film (Parafilm, Bemis) and then in light yellow duct tape. Dataloggers were attached using plastic zip-ties onto PVC stakes at a height of 1-3 cm immediately above the forest floor. The tape color was chosen to approximate the albedo of vegetation/soil, and the size of the sensor package was chosen to have a similar boundary layer to many small organisms (e.g. tree seedlings, fallen branches, large insects). The sensor packages intentionally did not include a radiation shield, as the intent was not to measure air temperature. Temperatures recorded by the loggers therefore reflect a combination of conductive, convective, and radiative heat fluxes, and can be considered a rough proxy for those experienced by small organisms. <br>Each plot contains 25 20 × 20 m2 subplots, demarcated by 1 m-high PVC stakes embedded in the soil. Each subplot also contains at its center a mesh litter trap suspended on PVC stakes at 1-m height. Exact locations for all subplot corner and center stakes were determined using ground-based Field-Map software (IFER, Jílové u Prahy, Czech Republic). Spatial positions were recorded in three-dimensional space (local x, y, z-coordinates) using an Impulse 200 Standard laser rangefinder, MapStar Module II electronic compass (Laser Technology, Colorado, USA).<br>We installed a datalogger on these stakes at the corner of each subplot and the center of each subplot. We also chose at random three focal subplots in each plot for higher-resolution sampling. Within these subplots we established a cross-type design, with six additional dataloggers deployed at 1 to 5 m distance on additional PVC stakes located near each litter trap. A total of 239 dataloggers were installed.<br>Dataloggers were deployed during the end of the dry season in late 2015. Each recorded 28 days of data at 20-minute intervals. Start times were synchronized among dataloggers within plots. The exact date of deployment was 1 November for the heavily logged plot and 9 November for the moderately logged and old growth plot. Weather conditions during November-December 2015 were consistently dry and hot, so we do not anticipate any biases from the differing start dates. In nearly all cases dataloggers were recovered in their original location, except for a small number that were transported 1-2 meters down slopes. We treated data from these as though they were in their original position. A small number of dataloggers also failed due to being lost or punctured by animal bites. 90% of dataloggers (214/239) were successfully recovered and downloaded.<br>Air temperature data<br>To compare the microclimate thermal proxy to other temperature metrics, we obtained off-plot (open site) and on-plot (below canopy) weather station data. To represent off-plot data for both the moderately and heavily logged plots, a weather station was located in a cleared area at the SAFE base camp (4.724341°N, 117.601449°E), at a distance of 2.0 km from the heavily logged plot and 3.9 km from the moderately logged plot. Data were logged continuously (Datahog, Skye Instruments, UK). Measurements included air temperature (°C) and photosynthetically active radiation (W m-2). Data were available for all of the study period. To represent off-plot data for the old growth plot, another weather station was located in a cleared area at the Maliau Basin Studies Center (4.736263°N, 116.97662°E), at a distance of 1.4 km from the plot. Available data only included photosynthetically active radiation (W m-2). Data were available for approximately 25% of the study period. We predicted air temperature values at this plot for these dates by calibrating a LOESS regression model of air temperature based on time of day (seconds after midnight) and photosynthetically active radiation, calibrated with weather station data from an open clearing at the SAFE base camp (78 km distance, 184 m lower elevation). Because of the small elevation change we did not include a further lapse rate correction for temperature. The fitted model, which had a residual standard error of 0.9°C, was used to predict off-plot air temperature at the old growth plot.<br>To represent on-plot air temperature, we located air temperature sensors (HOBO, U23-002) within radiation shields at 1.5 m height in a subplot within each plot (corresponding to a focal subplot with a higher density of microclimate dataloggers: old growth, subplot 18; moderately logged, subplot 24; heavily logged, subplot 25). Temperature was measured hourly.<br>LiDAR data<br>Discrete airborne LiDAR data were acquired by NERC's Airborne Research Facility (ARF) in November of 2014 using a Leica ALS50-II LiDAR sensor flown on a Dornier 228-20 at 41 points m-2 density, with up to four returns recorded per pulse. Georeferencing of the point cloud was ensured by incorporating data from a Leica base station in the study area. LiDAR point clouds were classified into ground and non-ground points, and used to produce a 1 m resolution canopy height model by averaging the first returns. Gaps in the canopy height model were filled by averaging neighboring cells.<br>Topography<br>The ground-mapped coordinates of the subplot corners, subplot centers, and all stems were used to construct a digital elevation model (DEM) for the plot. Elevation was interpolated onto a 1 m grid using ordinary kriging with a minimum of 4 points and search radius of 30 meters. This grid was then aligned to the LIDAR-determined location and elevation of the plot corners. The DEM was then used to estimate slope (in degrees) and cosine of aspect (with higher values indicating more southerly exposures) for each location.<br>Forest structure<br>Forest structure was determined from field surveys and from airborne laser scanning. For the field survey, all trees ≥10 cm diameter at 1.3 m height were censused in each plot in 2016. Diameter was measured at 1.3 m with a tape measure, height with laser rangefinders, and x-y position of each stem were determined using the same system as the subplot corners. The horizontal crown projection of every tree was mapped by measuring spatial positions (x and y-coordinates) of 5 to 30 points (depending on the size of the crown) at the boundary of a crown projected to the horizontal plane and then smoothed using Field-Map software. <br>Field stem maps were then converted into raster grids of stem basal area density (smoothed with 2-meter Gaussian kernel, and then rasterized to 1 m resolution), canopy density (number of overlying canopies per unit area) (1 m resolution), and plant area index (PAI) (10 m native resolution, interpolated to 1 m resolution). Spatial variation in PAI was mapped from the LiDAR point cloud using the MacArthur-Horn method. The method assumes that the leaves are randomly distributed within the laterally homogeneous canopy layers, so the PAI is proportional to the logarithm of the fraction of LiDAR pulses, β, penetrating through the canopy: PAI = -1/κ ln(β), where κ is a correction factor that accounts for canopy features, such as clumping and the distribution of leaf angles. We assumed a constant value of κ=0.7. Only the first returns, representing the first interaction of each LiDAR pulse with the canopy, are considered. We employed a lower cutoff of 2 m to avoid confusing ground returns with low-lying vegetation. PAI was estimated for point locations along a 1 m regular grid using circular sampling neighborhood of 10 m. This sampling window size is used to capture a sufficient number of LiDAR returns to avoid saturation effects in the more densely vegetated parts of the plots. This approach for calculating canopy closure may be biased, as clumping of vegetation, variation in leaf angle, and canopy edges (i.e. at gaps) should lead to spatial variation in the κ coefficient. It was not possible with our data to constrain κ using hemispherical photos due to saturation effects.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/22"><b>Drivers of microclimate variation in disturbed forests</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC independent research fellowship (Standard grant, NE/M019160/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000/2/2(JLD.3((126) )</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3266822">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Blonder_Mircoclimate.xlsx, Mircoclimate_proxy.zip</p><p><b>Blonder_Mircoclimate.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Mircoclimate datalogger data</b> (described in worksheet Data_Sheet_S1)</p><p>Description: Raw temperature measurements for all microclimate dataloggers</p><p>Number of fields: 7</p><p>Number of data rows: 444416</p><p>Fields: </p><ul><li><b>Site</b>: The name of the site, corresponding to logging treatment intensity (Field type: Categorical)</li><li><b>Tag</b>: The field code for the data logger (Field type: ID)</li><li><b>Time.elapsed..s.</b>: Number of seconds elapsed since data logger began recording (Field type: Numeric)</li><li><b>Forest.floor.temperature..degC.</b>: Microclimate proxy reading (Field type: Numeric)</li><li><b>X..m.</b>: Location of the data logger in meters east in UTM zone 50N (Field type: Numeric)</li><li><b>Y..m.</b>: Location of the data logger in meters north in UTM zone 50N (Field type: Numeric)</li><li><b>Z..m.</b>: Location of the data logger in meters above sea level (Field type: Numeric)</li></ul></li><li><p><b>Weather station data</b> (described in worksheet Data_Sheet_S3)</p><p>Description: Raw off-plot and on-plot weather station air temperature data</p><p>Number of fields: 4</p><p>Number of data rows: 1397</p><p>Fields: </p><ul><li><b>Site</b>: The name of the site, corresponding to logging treatment intensity (Field type: Categorical)</li><li><b>Time.elapsed..s.</b>: Number of seconds elapsed since data logger began recording (Field type: Numeric)</li><li><b>Off.plot.weather.station.air.temperature..degC.</b>: Air temperature measurement of the off-plot weather station (Field type: Numeric)</li><li><b>On.plot.weather.station.air.temperature..degC.</b>: Air temperature measurement of the on-plot weather station (Field type: Numeric)</li></ul></li></ol><p><b>Mircoclimate_proxy.zip</b></p><p>Description: S2 Spatial datasets for microclimate, topography, and canopy structure. All datasets are spatially interpolated to 1m resolution and projected into UTM coordinates, zone 50N. </p><p><b>Date range: </b>2015-11-01 to 2015-12-30</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
IMF clock angle proxy: orbital averaged magnetic field data from MAVEN in Martian magnetosheath
<p>The By and Bz components can be used as IMF clock angle proxy (IMF direction in MSO y-z plane). See the publication by Dong et al. 2019 for details: Dong, Y., Fang, X., Brain, D. A., Hurley, D. M., Halekas, J. S., Espley, J. R., et al. (2019). Magnetic field in the Martian magnetosheath and the application as an IMF clock angle proxy. <em>Journal of Geophysical Research: Space Physics</em>, 124, 4295–4313. <a href="https://doi.org/10.1029/2019JA026522">https://doi.org/10.1029/2019JA026522</a></p> <p>Formats: IDL save file and ASCII file</p> <p>Data descirption:</p> <p>Time span: Nov 11 2014 - May 21 2024</p> <p>Time: averaged unix time of MAVEN in magnetosheath of each s/c orbit</p> <p># of data points: # of data points (time resolution: 4s) taken in magnetosheath of each s/c orbit. Suggest using only # of data points >150 (i.e. duration > 10 min)</p> <p>Bx, By, Bz in MSO coordinate system</p> <p> </p>
Data from: Surrogate taxa and fossils as reliable proxies of spatial biodiversity patterns in marine benthic communities
Rigorous documentation of spatial heterogeneity (β-diversity) in present-day and preindustrial ecosystems is required to assess how marine communities respond to environmental and anthropogenic drivers. However, the overwhelming majority of contemporary and palaeontological assessments have centred on single higher taxa. To evaluate the validity of single taxa as community surrogates and palaeontological proxies, we compared macrobenthic communities and sympatric death assemblages at 52 localities in Onslow Bay (NC, USA). Compositional heterogeneity did not differ significantly across datasets based on live molluscs, live non-molluscs, and all live organisms. Death assemblages were less heterogeneous spatially, likely reflecting homogenization by time-averaging. Nevertheless, live and dead datasets were greater than 80% congruent in pairwise comparisons to the literature estimates of β-diversity in other marine ecosystems, yielded concordant bathymetric gradients, and produced nearly identical ordinations consistently delineating habitats. Congruent estimates from molluscs and non-molluscs suggest that single groups can serve as reliable community proxies. High spatial fidelity of death assemblages supports the emerging paradigm of Conservation Palaeobiology. Integrated analyses of ecological and palaeontological data based on surrogate taxa can quantify anthropogenic changes in marine ecosystems and advance our understanding of spatial and temporal aspects of biodiversity.
Disentangle the Sediment Mixing from Geochemical Proxies and Detrital Zircon Geochronology
<p>Supplementary Data of the Manuscript titled 'Disentangle the Sediment Mixing from Geochemical Proxies and Detrital Zircon Geochronology '</p>
CESM data: Response of the low-level jet to precession and itsimplications for proxies of the Indian monsoon
<p>This repository contains the simulation output of precession minimum and maximum experiments carried out using the CESM 1.2.0. The experimental setup is the same as that of Bosmans et al. (2014). Each simulation is run of 100 years, and the Jun-Jul-Aug mean of the last 50 years of the simulation is provided here. The climatological monthly mean of total precipitation from the pre-industrial control simulation is also deposited here. </p> <p> </p> <p>Contact: Chetankumar Jalihal<br> email: jalihal@iisc.ac.in</p> <p> </p> <p>Acknowledgments:<br> These simulations were carried out as a part of Chetankumar Jalihal's Ph.D. thesis, funded by the Ministry of Human Resource Development, Government of India, and the Centre for Excellence in the Divecha Centre for Climate Change (DCCC), supported by the Department of Science and Technology, Government of India. We also acknowledge the Supercomputer Education and Research Centre (SERC), Indian Institute of Science, Bangalore, for making available the computation facilities to carry out the simulations.</p>
FIG. 9 in Micropaleontological parameters as proxies of late Miocene surface water properties and paleoclimate in Gavdos Island, eastern Mediterranean
FIG. 9. — SST curves based on the percentages of warm- and cool-water indices.
FIG. 8 in Micropaleontological parameters as proxies of late Miocene surface water properties and paleoclimate in Gavdos Island, eastern Mediterranean
FIG. 8. — The score plots of the factors revealed from the R-mode analysis for each section.
FIG. 6 in Micropaleontological parameters as proxies of late Miocene surface water properties and paleoclimate in Gavdos Island, eastern Mediterranean
FIG. 6. — Litho-, bio- and chronostratigraphy of the studied sections in Gavdos Island, Greece.
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.