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9,882 results for “Scale”
Data from: Macroecological predictors of evolutionary and plastic potential do not apply at microgeographic scales for a freshwater Cladoceran under climate change
<p>Rapid evolutionary adaptation could reduce the negative impacts of climate change if sufficient heritability of key traits exists under future climate conditions. Plastic responses to climate change could also reduce negative impacts. Understanding which populations are likely to respond via evolution or plasticity could therefore improve estimates of extinction risk. A large body of research suggests that the evolutionary and plastic potential of a population can be predicted by the degree of spatial and temporal climatic variation it experiences. However, we know little about the scale at which these relationships apply. Here, we test if spatial and temporal variation in temperature affect genetic variation and plasticity of fitness and a key thermal tolerance trait (critical thermal maximum; CTmax) at microgeographic scales using a metapopulation of Daphnia magna. Specifically, we ask if: (1) there is microgeographic adaptation of CTmax and fitness to differences in temperature among the pools, (2) pools with greater temporal temperature variation have more genetic variation or plasticity in CTmax or fitness, and (3) increases in temperature affect the heritability of CTmax and fitness. Although we observed genetic variation and plasticity in CTmax and fitness, and differences in fitness among pools, we did not find support for the predicted relationships between temperature variation and genetic variation or plasticity. Furthermore, the genetic variation and plasticity we observed in CTmax is unlikely sufficient to reduce the impacts of climate change. CTmax plasticity was minimal and heritability was 72% lower when D. magna developed at the higher temperatures predicted under climate change. In contrast, the heritability of fitness increased by 53% under warmer temperatures suggesting an increase in overall evolutionary potential unrelated to CTmax under climate change. More research is needed to understand evolutionary and plastic potential under climate change and how that potential will be altered in future climates.</p>
Data for: Fear before food: Scale-dependence in elk habitat selection
<ol> <li>Habitat selection is a critical aspect of a species' ecology requiring complex decision-making that is both hierarchical and scale-dependent, since factors that influence selection may be nested or unequal across scales.</li> <li>Elk (<em>Cervus</em> <em>canadensis</em>) ranged widely across diverse habitats in North America prior to European settlement and subsequent eastern extirpation. Most habitat studies have occurred within their contemporary western range, even after eastern elk reintroductions began. As habitat selection can vary by geographic location, available cover, season, and diel period, it is important to understand how a non-migratory, reintroduced population in northern Wisconsin, USA is limited by the lack of variation in topography, elevation, and vegetation.</li> <li>We tested scale-dependent habitat selection on 79 adult elk from 2017–2020. We used resource selection functions across both temporal and spatial scales to understand differences in selection of topographic and environmental features.</li> <li>We found that selection varied both spatially and temporally and elk selected areas with the greatest potential to influence fitness at larger scales (i.e., landscape scale), meaning elk selected areas closer to escape cover and further from "risky" features (e.g., wolf territory centers, county roads and highways). We found stronger avoidance to wolf territory centers during spring, suggesting elk were selecting safer habitats during calving season. We found elk selected habitats with less canopy cover across both spatial scales and all seasons, suggesting that elk selected these areas for better access to forage as forest stands in early seral stages have greater nutritional value and forage biomass than closed-canopy forests and direct solar radiation to provide warmth in the cooler seasons.</li> <li>This study highlights how processes at different spatial and temporal scales influence species' decision-making. It provides insight into the complexity of making informed decisions in which an individual is responding to their immediate environment while simultaneously making decisions in the context of the larger landscape. Scale-dependent behavior is crucial to understand within specific geographic regions as these decisions scale up to influence population dynamics. </li> </ol>
Consistent predictors of microbial community composition across spatial scales in grasslands reveal low context-dependency
<p><span>Environmental circumstances shaping soil microbial communities have been studied extensively. However, due to disparate study designs, it has been difficult to resolve whether a globally consistent set of predictors exists, or context-dependency prevails. Here, we used a network of 18 grassland sites (11 of those containing regional plant productivity gradients) to examine i) if similar abiotic or biotic factors predict both large-scale (across sites) and regional-scale (within sites) patterns in bacterial and fungal community composition, and ii) if microbial community composition differs consistently at two levels of regional plant productivity (low vs high).</span><span> We found that </span><span>bacteria were consistently associated with certain soil properties and both bacteria and fungi were consistently associated with plant community composition within different sites. Moreover, there was a microbial community signal that clearly distinguished high and low-productivity soils that was shared across different grasslands independent of their location in the world.</span><span> In this study, we show that </span><span>there is high congruence between predictors of bacterial and fungal community composition at different spatial scales and that regional productivity differences are typified by characteristic soil microbial communities across the grassland biome. These results suggest that it might be feasible to predict the overall effects of global changes on soil microbial community composition in different grasslands, as well as to discriminate fertile from infertile systems using generally applicable microbial indicators.</span></p>
Data accompanying the publication: Measurements and modelling of pore-pressure gradients in the swash zone under large-scale laboratory bichromatic waves
<p>Data accompanying the publication: Measurements and modelling of pore-pressure gradients in the swash zone under large-scale laboratory bichromatic waves. See the README.txt file for more detail.</p>
Classification Of Large-Scale Environments That Drive The Formation Of Mesoscale Convective Systems Over Southern West Africa
<p>This is a set of datasets used for the publication of the article titled: Classification Of Large-Scale Environments That Drive The Formation Of Mesoscale Convective Systems Over Southern West Africa</p>
Loghub-2.0: a collection of large-scale datasets for log parsing
<p>See dataset details: <a href="https://github.com/logpai/LogPub">https://github.com/logpai/Loghub-2.0</a></p> <p>The datasets are freely available for research or academic work, subject to the following condition: For any usage or distribution of the LogPub datasets, please refer to the LogPub repository URL (<a href="https://github.com/logpai/LogPub">https://github.com/logpai/Loghub-2.0</a>) and cite the LogPub paper (<a href="https://arxiv.org/abs/2308.10828">A Large-scale Evaluation for Log Parsing Techniques: How Far are We?</a>) where applicable.</p>
Reactive Multiphase Flow in Porous Media at the Darcy Scale: a Benchmark proposal
<p>Description and data files for a "Reactive Two-phase flow Benchmark"</p> <p>Modeling reactive multiphase multicomponent flow in porous media leads to a highly nonlinear coupled system of degenerate partial differential equations and algebraic and/or ordinary differential equations, requiring special numerical treatment. The Benchmark consists of five test problems in total (both in 1D and in 2D), with varying degrees of difficulty, designed to verify the algorithms and<br> the codes dedicated to simulate coupled isothermal Hydro-Chemical processes during injection and storage of CO2 in the subsurface. It is intended to be used as a basis for comparing codes in order to better understand different couplings such as chemical reactions with two-phase flow, phase behavior with equilibrium reactions, dissolution and precipitation</p>
Domestication shapes the pig gut microbiome and immune traits from the scale of lineage to population
<p><span>Animal ecology and evolution have long been known to shape host physiology, but more recently, the gut microbiome has been identified as a mediator between animal ecology and evolution and health</span><span>. The gut microbiome has been shown to differ between wild and domestic animals, but the role of these differences for domestic animal evolution remains unknown. </span><span>Gut microbiome responses to new animal genotypes and local environmental change during domestication may promote specific host phenotypes that are adaptive (or not) to the domestic environment. Because the gut microbiome supports host immune function, understanding the effects of animal ecology and evolution on the gut microbiome and immune phenotypes is </span><span>critical. </span><span>We investigated how domestication affects the gut microbiome and host immune state in multiple pig populations across five domestication contexts representing domestication status and current living conditions: free-ranging wild, captive wild, free-ranging domestic, captive domestic in research or industrial settings. We observed that domestication context explained much of the variation in gut microbiome composition, pathogen abundances, and immune markers, yet the main differences in the repertoire of metabolic genes found in the gut microbiome were between the wild and domestic genetic lineages. We also documented population-level effects within domestication contexts, demonstrating that fine scale environmental variation also shaped host and microbe features. Our findings highlight that understanding </span><span>which gut microbiome and immune traits respond to host genetic lineage and/or scales of local ecology could inform </span><span>targeted interventions that manipulate the gut microbiome to achieve beneficial health outcomes. </span></p>
Fine-scale environmentally associated spatial structure of Lumpfish ( Cyclopterus lumpus) across the Northwest Atlantic
<p><span>Lumpfish, <em>Cyclopterus lumpus</em>, have historically been harvested throughout Atlantic Canada and are increasingly in demand as a solution to controlling sea lice in Atlantic salmon farms – a process which involves both the domestication and the transfer of lumpfish between geographic regions. Here, we have 70K SNP array data and whole genome re-sequencing data (WGS) for a variety of sample sites across the Northwest Atlantic. </span></p>
Large-scale genomic data reveal the phylogeny and evolution of owlet moths (Noctuoidea)
<p>Owlet moths (Noctuoidea; ~43–45K described species; Nieukerken et al. 2011) are one of the most ecologically diverse and speciose superfamilies of animals. Moreover, they comprise some of the world's most notorious pests of agriculture and forestry. Despite their contributions to terrestrial biodiversity and impacts on ecosystems and economies, the evolutionary history of Noctuoidea remains unclear because the superfamily lacks a statistically robust phylogenetic and temporal framework. We reconstructed the phylogeny of Noctuoidea using data from 1,234 genes (946.4 kb of nucleotides) obtained from the genome and transcriptome sequences of 76 species representing all six families. Relationships among the families of Noctuoidea were well resolved and consistently recovered based on both concatenation and gene coalescence approaches, supporting the following relationships: Oenosandridae + (Notodontidae + (Erebidae + (Nolidae + (Euteliidae + Noctuidae)))). A Yule tree prior with three unlinked molecular clocks was identified as the preferred BEAST analysis using marginal-likelihood estimations. The crown age of Noctuoidea was estimated to be 74.5 Ma, with most families originating before the end of Paleogene (23 Ma). Our study provides the first statistically robust phylogenetic and temporal framework for Noctuoidea, including all families of owlet moths, based on large-scale genomic data.</p>
Data, simulations/projections, and supporting materials for simulated hydrologic responses to climate-change projections for the Lake Tahoe basin: subbasin-scale results
<p>This dataset contains data, simulations, projections, and supporting materials associated with simulated hydrologic responses, at the scale of 60 subbasins comprising the Lake Tahoe Basin, California and Nevada, to a 16-member ensemble of statistically-downscaled projections of climate change, 1950-2099. The process and results are described in detail in</p> <div>Dettinger, M., & Rajagopal, S., 2023, Simulated hydrologic responses to climate-change </div> <div>projections for the Lake Tahoe Basin: Desert Research Institute Publication 41292, 99 p.,</div> <div> </div> <div>which is included here. </div> <div> </div> <div>The dataset as presented here contains 6 files, including one large zipped file that itself contains some 1,975 files, mostly simple text files of data, simulation outputs, and analytical results organized into dozens and dozens of subdirectories to allow specific elements to be identified and isolated. Beyond this large collection, the zipped file also contains copies of the report(s), selected supporting documents, graphical files, codes, and copious #readme.txt files to orient the user. </div>
Rapid and reversible humidity-dependent colour change by water film formation in a scaled springtail: Image compilations and video recordings
<p><span>Colour is often not a static trait but can change over time either through biotic or abiotic factors. Humidity-dependent colour change can occur through either morphological change (e.g. to feather barbules in birds) or by the replacement of air by water causing a shift in refractive index, as seen in arthropod multilayer cuticles or scales. The scaled springtail <em>Lepidocyrtus</em> <em>cyaneus</em> has scales that produce color largely via thin film interference from their lamina. We observed a marked colour change from golden to violet/purple colouration in humid conditions. Light microscopy, microspectrophotometry, contact angle goniometry and optical modelling indicate that the formation of a thin film of water on top of the hydrophilic scales increases their laminar thin film thickness, causing a shift towards violet/purple colour. Evaporation of the water film causes the metallic golden colour to return. This constitutes a remarkably rapid colour change (in the order of seconds), only limited by the speed of water film condensation and evaporation, that may serve as inspiration for new dynamically coloured materials and sensors.</span></p>
Multi-colony tracking of two pelagic seabirds with contrasting flight capability illustrates how windscapes shape migratory movements at an ocean-basin scale
<p>Migration is a common trait among many animals allowing the exploitation of spatiotemporally variable resources. It often implies high energetic costs to cover large distances, for example between breeding and wintering grounds. For flying or swimming animals, the adequate use of winds and currents can help reduce the associated energetic costs. Migratory seabirds are good models because they dwell in habitats characterized by strong winds while undertaking very long migrations. We tested the hypothesis that seabirds migrate through areas with favourable winds. To that end, we used a multi-colony geolocator tracking dataset of two North Atlantic seabirds with contrasting flight capabilities, the black-legged kittiwake (<em>Rissa</em> <em>tridactyla</em>) and the Atlantic puffin (<em>Fratercula</em> <em>arctica</em>), and wind data from the ERA5 climate reanalysis model. Both species had on average positive wind support during migration. Their main migratory routes were similar and followed seasonally prevailing winds. The general migratory movement had a loop-shape at the scale of the North Atlantic, with an autumn route (southward) along the east coast of Greenland, and a spring route (northward) closer to the British Isles. While migrating, both species had higher wind support in spring than in autumn. Kittiwakes migrated farther and benefited from higher wind support than puffins on average. The variation in wind conditions encountered while migrating was linked to the geographical location of the colonies. Generally, northernmost colonies had better wind support in autumn while the southernmost colonies had a better wind support in spring, with some exceptions. Our study helps in understanding how the physical environment shapes animal migration, which is crucial to further predict how migrants will be impacted by ongoing environmental changes.</p>
Early life telomeres are influenced by environments acting at multiple temporal and spatial scales
<p><span>An individual's telomere length early in life may reflect or contribute to key life history processes sensitive to environmental variation. Yet, the relative importance of genetic and</span><span> environmental factors in shaping early life telomere length is not well understood as it requires samples collected from multiple generations with known developmental histories. We used a confirmed pedigree and conducted an animal model analysis of telomere lengths obtained from nestling house sparrows (<em>Passer domesticus</em>) sampled over a span of 22 years. We found significant additive genetic variation for early life telomere length, but it comprised a relatively small proportion (9%) of the total biological variation. Three sources of environmental variation were important: among cohorts, among breeding attempts within years and families, and among nestmates. The magnitude of variation among breeding attempts and among nestmates also differed by cohort, suggesting that interactive effects of environmental factors across time or spatial scales were important, yet we were unable to identify the specific causes of these interactions. The mean amount of precipitation during the breeding season positively predicted telomere length, but neither weather during a given breeding attempt nor date in the breeding season contributed to an offspring's telomere length. At the residual level, level of individual nestlings, offspring sex, size, and mass at 10 days of age also did not predict telomere length. Environmental effects appear especially important in shaping early life telomere length in some species, and an array of complex more focus on how environmental factors that interactions across scales may help to explain some of the variation observed among studies.</span></p>
Stability of the next-to-tribimaximal mixings under radiative corrections with the variation of SUSY breaking scale in MSSM
<pre>We analyse the radiative stability of the next-to-tribimaximal mixings ($NTBM$) with the variation of SUSY breaking scale ($m_S$) in MSSM, for both normal ordering (NO) and inverted ordering (IO) at the fixed input value of seesaw scale $M_R = 10^{15}$ GeV and two different values of $\tan \beta$. All the neutrino oscillation parameters receive varying radiative corrections irrespective of the $m_S$ values at the electroweak scale, which are all within $3\sigma$ range of the latest global fit data at low value of $\tan \beta$ (30). NO is found to be more stable than IO for all four different NTBM mixing patterns.</pre>
A Large-Scale AIS Datset from Finnish Water
<p>The proposed AIS dataset encompasses a substantial temporal span of 20 months, spanning from April 2021 to December 2022. This extensive coverage period empowers analysts to examine long-term trends and variations in vessel activities. Moreover, it facilitates researchers in comprehending the potential influence of external factors, including weather patterns, seasonal variations, and economic conditions, on vessel traffic and behavior within the Finnish waters.</p> <p>This dataset encompasses an extensive array of data pertaining to vessel movements and activities encompassing seas, rivers, and lakes. Anticipated to be comprehensive in nature, the dataset encompasses a diverse range of ship types, such as cargo ships, tankers, fishing vessels, passenger ships, and various other categories.</p> <p>The AIS dataset exhibits a prominent attribute in the form of its exceptional granularity with a total of 2 293 129 345 data points. The provision of such granular information proves can help analysts to comprehend vessel dynamics and operations within the Finnish waters. It enables the identification of patterns and anomalies in vessel behavior and facilitates an assessment of the potential environmental implications associated with maritime activities.</p> <p>Please cite the following publication when using the dataset:</p> <p><em>TBD</em></p> <p>The publication is available at: <em>TBD</em></p> <p>A preprint version of the publication is available at <em>TBD</em></p> <h3>csv file structure</h3> <h4><a href="#yyyy-mm-dd-locationcsv"></a>YYYY-MM-DD-location.csv</h4> <p>This file contains the received AIS position reports. The structure of the logged parameters is the following: <code>[timestamp, timestampExternal, mmsi, lon, lat, sog, cog, navStat, rot, posAcc, raim, heading]</code></p> <p><code><strong>timestamp</strong></code> I beleive this is the UTC second when the report was generated by the electronic position system (EPFS) (0-59, or 60 if time stamp is not available, which should also be the default value, or 61 if positioning system is in manual input mode, or 62 if electronic position fixing system operates in estimated (dead reckoning) mode, or 63 if the positioning system is inoperative).</p> <p><code><strong>timestampExternal</strong></code> The timestamp associated with the MQTT message received from <a href="http://www.digitraffic.fi/" target="_blank" rel="nofollow noreferrer noopener">www.digitraffic.fi</a>. It is assumed this timestamp is the Epoch time corresponding to when the AIS message was received by digitraffic.fi.</p> <p><code><strong>mmsi</strong></code> MMSI number, Maritime Mobile Service Identity (MMSI) is a unique 9 digit number that is assigned to a (Digital Selective Calling) DSC radio or an AIS unit. Check <a href="https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity" target="_blank" rel="nofollow noreferrer noopener">https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity</a></p> <p><code><strong>lon</strong></code> Longitude, Longitude in 1/10 000 min (+/-180 deg, East = positive (as per 2's complement), West = negative (as per 2's complement). 181= (6791AC0h) = not available = default)</p> <p><code><strong>lat</strong></code> Latitude, Latitude in 1/10 000 min (+/-90 deg, North = positive (as per 2's complement), South = negative (as per 2's complement). 91deg (3412140h) = not available = default)</p> <p><code><strong>sog</strong></code> Speed over ground in 1/10 knot steps (0-102.2 knots) 1 023 = not available, 1 022 = 102.2 knots or higher</p> <p><code><strong>cog</strong></code> Course over ground in 1/10 = (0-3599). 3600 (E10h) = not available = default. 3 601-4 095 should not be used</p> <p><code><strong>navStat</strong></code> Navigational status, 0 = under way using engine, 1 = at anchor, 2 = not under command, 3 = restricted maneuverability, 4 = constrained by her draught, 5 = moored, 6 = aground, 7 = engaged in fishing, 8 = under way sailing, 9 = reserved for future amendment of navigational status for ships carrying DG, HS, or MP, or IMO hazard or pollutant category C, high speed craft (HSC), 10 = reserved for future amendment of navigational status for ships carrying dangerous goods (DG), harmful substances (HS) or marine pollutants (MP), or IMO hazard or pollutant category A, wing in ground (WIG); 11 = power-driven vessel towing astern (regional use); 12 = power-driven vessel pushing ahead or towing alongside (regional use); 13 = reserved for future use, 14 = AIS-SART (active), MOB-AIS, EPIRB-AIS 15 = undefined = default (also used by AIS-SART, MOB-AIS and EPIRB-AIS under test)</p> <p><code><strong>rot</strong></code> ROTAIS Rate of turn</p> <ul> <li>0 to +126 = turning right at up to 708 deg per min or higher</li> <li>0 to -126 = turning left at up to 708 deg per min or higher</li> <li>Values between 0 and 708 deg per min coded by ROTAIS = 4.733 SQRT(ROTsensor) degrees per min where ROTsensor is the Rate of Turn as input by an external Rate of Turn Indicator (TI). ROTAIS is rounded to the nearest integer value.</li> <li>+127 = turning right at more than 5 deg per 30 s (No TI available)</li> <li>-127 = turning left at more than 5 deg per 30 s (No TI available)</li> <li>-128 (80 hex) indicates no turn information available (default).</li> </ul> <p>ROT data should not be derived from COG information.</p> <p><code><strong>posAcc</strong></code> Position accuracy, The position accuracy (PA) flag should be determined in accordance with the table below:</p> <ul> <li>1 = high (<= 10 m)</li> <li>0 = low (> 10 m)</li> <li>0 = default</li> </ul> <p>See <a href="https://www.navcen.uscg.gov/?pageName=AISMessagesA#RAIM" target="_blank" rel="nofollow noreferrer noopener">https://www.navcen.uscg.gov/?pageName=AISMessagesA#RAIM</a></p> <p><code><strong>raim</strong></code> RAIM-flag Receiver autonomous integrity monitoring (RAIM) flag of electronic position fixing device; 0 = RAIM not in use = default; 1 = RAIM in use. See Table <a href="https://www.navcen.uscg.gov/?pageName=AISMessagesA#RAIM" target="_blank" rel="nofollow noreferrer noopener">https://www.navcen.uscg.gov/?pageName=AISMessagesA#RAIM</a></p> <p>Check <a href="https://en.wikipedia.org/wiki/Receiver_autonomous_integrity_monitoring" target="_blank" rel="nofollow noreferrer noopener">https://en.wikipedia.org/wiki/Receiver_autonomous_integrity_monitoring</a></p> <p><code><strong>heading</strong></code> True heading, Degrees (0-359) (511 indicates not available = default)</p> <h4><a href="#yyyy-mm-dd-metadatacsv"></a>YYYY-MM-DD-metadata.csv</h4> <p>This file contains the received AIS metadata: the ship static and voyage related data. The structure of the logged parameters is the following: <code>[timestamp, destination, mmsi, callSign, imo, shipType, draught, eta, posType, pointA, pointB, pointC, pointD, name]</code></p> <p><code><strong>timestamp</strong></code> The timestamp associated with the MQTT message received from <a href="http://www.digitraffic.fi/" target="_blank" rel="nofollow noreferrer noopener">www.digitraffic.fi</a>. It is assumed this timestamp is the Epoch time corresponding to when the AIS message was received by digitraffic.fi.</p> <p><code><strong>destination</strong></code> Maximum 20 characters using 6-bit ASCII; @@@@@@@@@@@@@@@@@@@@ = not available For SAR aircraft, the use of this field may be decided by the responsible administration</p> <p><code><strong>mmsi</strong></code> MMSI number, Maritime Mobile Service Identity (MMSI) is a unique 9 digit number that is assigned to a (Digital Selective Calling) DSC radio or an AIS unit. Check <a href="https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity" target="_blank" rel="nofollow noreferrer noopener">https://en.wikipedia.org/wiki/Maritime_Mobile_Service_Identity</a></p> <p><code><strong>callSign</strong></code> 7?=?6 bit ASCII characters, @@@@@@@ = not available = default Craft associated with a parent vessel, should use “A” followed by the last 6 digits of the MMSI of the parent vessel. Examples of these craft include towed vessels, rescue boats, tenders, lifeboats and liferafts.</p> <p><code><strong>imo</strong></code> 0 = not available = default – Not applicable to SAR aircraft</p> <ul> <li>0000000001-0000999999 not used</li> <li>0001000000-0009999999 = valid IMO number;</li> <li>0010000000-1073741823 = official flag state number.</li> </ul> <p>Check: <a href="https://en.wikipedia.org/wiki/IMO_number" target="_blank" rel="nofollow noreferrer noopener">https://en.wikipedia.org/wiki/IMO_number</a></p> <p><code><strong>shipType</strong></code></p> <ul> <li>0 = not available or no ship = default</li> <li>1-99 = as defined below</li> <li>100-199 = reserved, for regional use</li> <li>200-255 = reserved, for future use Not applicable to SAR aircraft</li> </ul> <p>Check <a href="https://www.navcen.uscg.gov/pdf/AIS/AISGuide.pdf" target="_blank" rel="nofollow noreferrer noopener">https://www.navcen.uscg.gov/pdf/AIS/AISGuide.pdf</a> and <a href="https://www.navcen.uscg.gov/?pageName=AISMessagesAStatic" target="_blank" rel="nofollow noreferrer noopener">https://www.navcen.uscg.gov/?pageName=AISMessagesAStatic</a></p> <p><code><strong>draught</strong></code> In 1/10 m, 255 = draught 25.5 m or greater, 0 = not available = default; in accordance with IMO Resolution A.851 Not applicable to SAR aircraft, should be set to 0</p> <p><code><strong>eta</strong></code> Estimated time of arrival; MMDDHHMM UTC</p> <ul> <li>Bits 19-16: month; 1-12; 0 = not available = default</li> <li>Bits 15-11: day; 1-31; 0 = not available = default</li> <li>Bits 10-6: hour; 0-23; 24 = not available = default</li> <li>Bits 5-0: minute; 0-59; 60 = not available = default</li> </ul> <p>For SAR aircraft, the use of this field may be decided by the responsible administration</p> <p><code><strong>posType</strong></code> Type of electronic position fixing device</p> <ul> <li>0 = undefined (default)</li> <li>1 = GPS</li> <li>2 = GLONASS</li> <li>3 = combined GPS/GLONASS</li> <li>4 = Loran-C</li> <li>5 = Chayka</li> <li>6 = integrated navigation system</li> <li>7 = surveyed</li> <li>8 = Galileo,</li> <li>9-14 = not used</li> <li>15 = internal GNSS</li> </ul> <p><code><strong>pointA</strong></code> Reference point for reported position.</p> <p>Also indicates the dimension of ship (m). For SAR aircraft, the use of this field may be decided by the responsible administration. If used it should indicate the maximum dimensions of the craft. As default should A = B = C = D be set to “0”</p> <p>Check: <a href="https://www.navcen.uscg.gov/?pageName=AISMessagesAStatic#_Reference_point_for" target="_blank" rel="nofollow noreferrer noopener">https://www.navcen.uscg.gov/?pageName=AISMessagesAStatic#_Reference_point_for</a></p> <p><code><strong>pointB</strong></code> See above</p> <p><code><strong>pointC</strong></code> See above</p> <p><code><strong>pointD</strong></code> See above</p> <p><code><strong>name</strong></code> Maximum 20 characters 6 bit ASCII "@@@@@@@@@@@@@@@@@@@@" = not available = default The Name should be as shown on the station radio license. For SAR aircraft, it should be set to “SAR AIRCRAFT NNNNNNN” where NNNNNNN equals the aircraft registration number.</p>
Block 73--63k scale--Survey of India Maps
<p>391 maps of the Survey of India covering block 73</p>
Block 58--63 scale--Survey of India Maps
<p>316 maps of the Survey of India covering block 58.</p>
Block 57--63k scale--Survey of India Maps
<p>349 maps of the Survey of India covering block 57.</p>
Block 56--63k scale--Survey of India Maps
<p>293 maps of the Survey of India covering block 56.</p>
ScienceDex guides
Understand access before you commit
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.