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26 results for “scaling laws”
Experimental data for 'Scaling laws for coastal overwash morphology'
<p>This dataset contains the experimental data described in Lazarus, ED (2016) Scaling laws for coastal overwash morphology, <em>Geophysical Research Letters</em>, 43, 12113–12119, <a href="https://doi.org/10.1002/2016GL071213">https://doi.org/10.1002/2016GL071213</a>.</p> <p>The physical experiments that produced these data were conducted at St Anthony Falls Laboratory (University of Minnesota, USA) in December 2014. The experiments were conducted in a 3 x 5 x 0.6 m tank filled with well-sorted coarse river sand. The tank and the experimental trials are detailed in Text S1 of the Supporting Information for Lazarus (2016): <a href="https://agupubs.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2F2016GL071213&file=grl55284-sup-0001-SI.pdf">https://agupubs.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2F2016GL071213&file=grl55284-sup-0001-SI.pdf</a></p> <p>This dataset consists of two *.csv files:</p> <ul> <li>'...THROATS.csv' – morphometric data for <strong>erosional</strong> (throat) features in the experimental barrier</li> <li>'...WASHOVER.csv' – morphometric data for <strong>depositional</strong> (washover) features on the back-barrier floodplain</li> </ul> <p>Both files have the same general column headings: feature width (in the alongshore dimension) [m], feature length (in the cross-shore dimension) [m], feature area [m<sup>2</sup>], feature volume [m<sup>3</sup>], alongshore spacing (centroid-to-centroid distance to neighbouring feature) [m], and real alongshore position [m].</p> <p>All features were formed along an initially geometrically uniform (topographically homogenous) trapezoidal barrier under inundation-type forcing (denoted in 'forcing' column). These data report the compiled results of three experimental trials (denoted in 'trial' column).</p> <p>Note that these data are also available as part of the Supporting Information for Lazarus (2016), but the format in which they were originally uploaded is not conducive to straightforward integration into open-source analysis. Publishing them here, in this tidier format, is an effort to rectify that.</p>
High-frequency light attenuation measurements in 35 lakes: companion data from "Coefficients in Taylor’s Law increase with the time scale of water clarity measurements in a global suite of lakes"
Identifying the scaling rules describing ecological patterns across time and space is a central challenge in ecology. Taylor’s Law of fluctuation scaling, which states that the variance of a population is proportional to a positive power of the mean, has been widely observed in population dynamics and characterizes variability in multiple scientific domains. However, it is unclear if this phenomenon accurately describes ecological patterns across many orders of magnitude in time, and therefore links otherwise disparate observations. This dataset uses light attenuation observations from 10,531 days of high-frequency measurements in 35 globally distributed lakes to test this unknown. We focus on water clarity as an integrative ecological characteristic that responds to both biotic and abiotic drivers. We provide documentation that variations in ecological measurements across diverse sites and temporal scales exhibit variance patterns consistent with Taylor’s Law, and that model coefficients increase in a predictable yet non-linear manner with decreasing observation frequency.
Dataset used in the paper: "Scaling laws and dynamics of hashtags on Twitter"
<p>This dataset was used in the manuscript "Scaling laws and dynamics of hashtags on Twitter"..</p> <p>The Twitter data was obtained from a sample of 10% of all public tweets, provided by the Twitter streaming application programming interface. We extracted the hashtags from each tweet and counted how many times they were used in different time intervals. Time intervals of three different lengths were used: days, hours, and minutes. The tweets were published between November 1st 2015 and November 30th 2016, but not all time intervals between these dates are available.</p> <p>The <strong>four files</strong> in this dataset correspond each to one folder (collected using tar). Each folder contains compressed .csv files (compressed using gzip). The content of the .csv files in each folder are:</p> <p><em><strong>hashtags_frequency_day.tar</strong></em><br> Counts of hashtags in each day. The name of each file in the folder indicates the date (GMT). The entries in each file are the hashtag and the count in the interval.<br> <br> <em><strong>hashtags_frequency_hour.tar</strong></em><br> Counts of hashtags in each hour. The name of each file in the folder indicates the date (GMT). The entries in each file are the hashtag and the count in the interval.<br> <br> <em><strong>hashtags_frequency_minutes.tar</strong></em><br> Counts of hashtags in each minute. The name of each file in the folder indicates the date (GMT, only a fraction of all days is available). The entries in each file are the hashtag and the count in the interval.<br> <br> <em><strong>number_of_tweets.tar</strong></em><br> Counts of the number of tweets in each minute. The name of each file in the folder indicates the day. The entries in each file are the minute in the day (GMT) and count of tweets in our dataset.</p>
Strength-mass scaling law governs mass distribution inside honey bee swarms
<p>To survive during colony reproduction, bees create dense clusters of thousands of suspended individuals. How does this swarm, which is orders of magnitude larger than the size of an individual, maintain mechanical stability? We hypothesize that the internal structure in the bulk of the swarm, about which there is little prior information, plays a key role in mechanical stability. Here, we provide the first-ever 3D reconstructions of the positions of the bees in the bulk of the swarm using x-ray computed tomography. We find that the mass of bees in a layer decreases with distance from the attachment surface. By quantifying the distribution of bees within swarms varying in size (made up of 4000–10,000 bees), we find that the same power law governs the smallest and largest swarms, with the weight supported by each layer scaling with the mass of each layer to the ≈1.5 power. This arrangement ensures that each layer exerts the same fraction of its total strength, and on average a bee supports a lower weight than its maximum grip strength. This illustrates the extension of the scaling law relating weight to strength of single organisms to the weight distribution within a superorganism made up of thousands of individuals.</p>
Towards Neural Scaling Laws for Foundation Models on Temporal Graphs
<p>Datasets provided in this storage are introduced in the paper: <em>Towards Neural Scaling Laws for Foundation Models on Temporal Graphs</em></p> <ul> <li>Each .csv file represents all transactions of the token network that has the same name as the file name (<em><tokenname.csv>)</em></li> <li>Each transaction corresponds to a row in each file.</li> <li>Each transaction has: <ul> <li> blockNumber : is the block ID of Ethereum that includes this transaction</li> <li>timestamp: time that the transaction is made in UNIX timestamp format</li> <li>tokenAddress : the address that specifies a unique ERC20 token</li> <li>from: address of sender</li> <li>to: address of receiver</li> <li>value: the amount the transaction</li> <li>fileBlock: we split the whole number of blocks count to 35 buckets and assigned the bucket ID to the transaction to trace the blocks </li> </ul> </li> <li>To use the same setting as described in the papers, we include edge list and label that contain node interactions and labels for each snapshot in each token network. <ul> <li>Each transaction in the edge list also has "from","to" and "amount" fields, but with an additional "snapshot" field to indicate the index of the snapshot that the transaction below to</li> <li>Each row in label file indicates the ground truth label of the snapshot having an index corresponding to the index of the row (e.g first row indicates the label of the first snapshot)</li> <li>We provided the way to generate edge lists and label files in the following Github repository: https://github.com/benjaminnNgo/ScalingTGNs/blob/main/script/utils/TGS.py</li> </ul> </li> <li>However, we also provide raw <em>.csv </em> to divide into generate <em>edgeslist </em>and <em>label with a different setting.</em></li> </ul> <p><br><br></p>
Supporting dataset underlying "Human cortical folding across regions within individual brains follows universal scaling law"
<p>This is the dataset underlying "Human cortical folding across regions within individual brains follows universal scaling law", the matlab scripts enable the user to direct reproduce the figures in the paper.</p> <p>2020 update: The matlab code for extraction of lobe-wise measures from Freesurfer subjects is now published on <a href="https://doi.org/10.5281/zenodo.3608675">Zenodo</a> and <a href="https://github.com/cnnp-lab/CorticalFoldingAnalysisTools">Github</a>.</p>
Data from: Allometric scaling laws linking biomass and rooting depth vary across ontogeny and functional groups in tropical dry forest lianas and trees
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Strength-mass scaling law governs mass distribution inside honey bee swarms
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Data from: Contextualizing macroecological laws: A big data analysis on electrofishing and allometric scalings in Ohio, USA
We investigated the influence of different electrofishing methods on allometric scaling features of fish assemblages in lotic environments. The ultimate aim was to elucidate to which extent the structure of fish assemblages is predictable by the three-quarter power law theory. Water bodies across the state of Ohio, USA, provided a suitable data set to analyze the size–biomass spectra of 2051 fish assemblages. For the first time, 41,070 allometric field observations were screened according to sampling methods (i.e., longline, tote barge, boat) adopted for sampling collection. Allometric patterns varied considerably in relation with the sampling method, in turn imposed by the local hydrology and morphology of the investigated water courses, as shown by the lowering of scalings from boatable to wadeable systems. There are several lines of evidence indicating that the chosen type of electrofishing acts as a pitfall for size spectra. Using individually weighted body-mass values as independent predictor of spectra we show that the specific sampling methodology required by the physical characteristics of different lotic habitats influence the allometric outcomes, a novel result that makes universality of community power laws not as straightforward as supposed until now.
Data from: Cope's rule and the universal scaling law of ornament complexity
Luxuriant, bushy antlers, bizarre crests, and huge, twisting horns and tusks are conventionally understood as products of sexual selection. This view stems from both direct observation and from the empirical finding that the size of these structures grows faster than body size (i.e., ornament size shows positive allometry). We contend that the familiar evolutionary increase in the complexity of ornaments over time in many animal clades is decoupled from ornament size evolution. Increased body size comes with extended growth. Since growth scales to the quarter power of body size, we predicted that ornament complexity should scale according to the quarter power law as well, irrespective of the role of sexual selection in the evolution and function of the ornament. To test this hypothesis, we selected three clades (ammonites, deer, and ceratopsian dinosaurs) whose species bore ornaments that differ in terms of the importance of sexual selection to their evolution. We found that the exponent of the regression of ornament complexity to body size is the same for the three groups and is statistically indistinguishable from 0.25. We suggest that the evolution of ornament complexity is a by-product of Cope's rule. We argue that although sexual selection may control size in most ornaments, it does not influence their shape.
Data from: Validity, applicability and universality of fractal scaling laws for natural lakes
<p><span>We conducted a systematic investigation on the validity, applicability and universality of the fractal scaling relationships for natural lakes within China. Hydro-climatic zones from the Hydrological regionalization of China were taken as the statistical units for all the analyses. Lake size and hydrological parameters used in the analyses are from the HydroLAKES database. To test the validity of the fractal scaling relationships between lake properties, power law functions were fitted to these properties. To examine the effects of topographical relief on the power law size distribution, regional degree of topographical relief was calculated for each zone based on ASTER GDEM2. To explore the potential impacts of human activities on the applicability of power law size distribution, Human modification index data was jointly analyzed with the power law distribution of lake size.</span></p>
Raw images for: The narrowing of dendrite branches across nodes follows a well-defined scaling law
<p><span><span><span><span><span><span><span><span><span><span><span><span>The systematic variation of diameters in branched networks has tantalized biologists since the discovery of da Vinci's rule for trees. Da Vinci's rule can be formulated as a power law with exponent two: the square of the mother branch's diameter is equal to the sum of the squares of those of the daughters. Power laws, with different exponents, have been proposed for branching in circulatory systems (Murray's law with exponent 3) and in neurons (Rall's law with exponent 3/2). The laws have been derived theoretically, based on optimality arguments, but, for the most part, have not been tested rigorously. Using super-resolution methods to measure the diameters of dendrites in highly branched <i>Drosophila</i> Class IV sensory neurons, we have found that these types of power laws do not hold. In their place, we have discovered a different diameter-scaling law: the cross-sectional area is proportional to the number of dendrite tips supported by the branch plus a constant, corresponding to a minimum diameter of the terminal dendrites. The area proportionality accords with a requirement for microtubules to transport materials and nutrients for dendrite tip growth. The minimum diameter may be set by the force, on the order of a few piconewtons, required to bend membrane into the highly curved surfaces of terminal dendrites. Because the observed scaling differs from Rall's law, we propose that cell biological constraints such as intracellular transport and protrusive forces generated by the cytoskeleton are important in determining the branched morphology of these cells.</span></span></span></span></span></span></span></span></span></span></span></span></p>
Data-Error Scaling Laws in Machine Learning on Combinatorial Mutation-prone Sets: Proteins and Small Molecules
<div> </div> <h3>Data</h3> <p> This folder contains the raw data used during this work. `out_seq_total.txt` contains information on the sequences used (mutations, number of mutations, etc.). `output_energies_total.txt` contains the response variables, which include: a) unrelaxed EvoEF energies (peptides); b) relaxed EvoEF energies (peptides, `*_repaired.txt`); and c) solvation energies (molecules). 3D structures are provided in `.xyz` format in the subfolder `XYZ` (molecules).</p> <div> <div><strong>GB1 dataset</strong></div> <br> <div>The GB1 dataset was <strong>not</strong> generated by us (https://doi.org/10.48550/arXiv.2405.05167). If you use the GB1 dataset, please cite the original paper:</div> <br> <div>Wu, N. C., Dai, L., Olson, C. A., Lloyd-Smith, J. O., & Sun, R. (2016). <em>Adaptation in protein fitness landscapes is facilitated by indirect paths. </em><strong>eLife</strong>, 5:e16965. doi:10.7554/eLife.16965</div> </div> <h3>Results</h3> <p>This folder contains the results (outputs) of the ML models trained using the provided scripts (see github repository). Such results are incuded in the form of `.npy` files. To load the files please include the option `allow_pickle=True`.</p> <p> Each `.npy` file contains the following keys:<br> * `initial_parameters`: script inputs.<br> * `d_encoder`: encoder used (not always included).<br> * `ns_train`: number of training points used for the LCs (rounded, integers).<br> * `ns_train_float`: number of training points used for the LCs (not rounded, float).<br> * `ns_train_norm`: number of training points used for the LCs (normalized, float).<br> * `res`: test MAEs.<br> * `res_tot`: (train,validation,test) MAEs.<br> * `res_tot_mut`: (train,validation,test) MAEs sorted by mutation number.<br> * `l_opt`: optimal kernel length used during the test.<br> * `ls`: kernel lengths used for grid search.<br> * `idx_seeds`: indices used to reshuffle the data. If one want to rebild the initial order use `np.argsort(idx_seeds)`.<br> * `alpha_opt`: optimal regression parameters used to calculate the test error. To sort the data use `alpha_opt[i][ii][np.argsort(idx_seeds[ii,0:arg_train_max].astype(int)[:ns_train[i]]`. Where `i` is the replicate number (0-99) and ii is the idex in the LC.<br> * `valid_errs`: validation error (MAE) calculated for each point in the hyperparameter (kernel scale) optimisation.<br> * `test_errs`: test error (MAE) calculated for each point in the hyperparameter (kernel scale) optimisation.</p>
Data from: Cope’s rule and the universal scaling law of ornament complexity
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Scaling laws of political regime dynamics: Stability of democracies and autocracies in the 20th-century
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Data from: Contextualizing macroecological laws: A big data analysis on electrofishing and allometric scalings in Ohio, USA
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Data from: Validity, applicability and universality of fractal scaling laws for natural lakes
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Raw images for: The narrowing of dendrite branches across nodes follows a well-defined scaling law
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Data from: Re-evaluation of the law of constant extinction for ruminants at different taxonomical scale
<p><span>The "law of constant extinction," proposed by Van Valen, states that long and short-lived taxa have equal chances of going extinct. This pattern of age-independent extinction was originally inferred using the fossil record of several different taxa and relied on survivorship curves built from the literal reading of the fossil record. Van Valen's seminal work was mostly done at higher taxonomic levels, hence its prevalence at the species level could not be directly inferred. The surprisingly few subsequent studies done at the species level have challenged the prevalence of age-independent extinction, but those have, for the most part, failed to explicitly incorporate inherent biases of the fossil record. Using a recent Bayesian framework that accounts for several of those biases, including the fact that very short-living lineages might never make to the record itself, we showed that Ruminantia species present age-dependent extinction, where extinction probability decreases with species age. An analysis at the genus level suggested age-independent extinction but further examination suggested that the pattern might be more complex than previously reported by Van Valen. Our results indicate that different taxonomic levels may present different extinction regimes, which could justify the development of new macroevolutionary theory and methods.</span></p>
Data from: Ornament complexity is correlated with sexual selection: (a comment on Raia et al., "Cope's rule and the universal scaling law of ornament complexity")
Raia et al. propose that the evolution of the shape and complexity of animal ornaments (e.g., deer antlers) can be explained by interspecific variation in body size and is not influenced by sexual selection. They claim to show that ornament complexity is related to body size by an 0.25-power law and argue that this finding precludes a role for sexual selection in the evolution of ornament complexity. However, their study does not test alternative hypotheses and mismeasures antler shape allometry by omitting much of the published data. We show that an index of sexual selection (sexual size dimorphism) is positively correlated with size-corrected antler complexity and that the allometric slope of complexity is substantially greater than 0.25, contra Raia et al. We conclude that sexual selection and physical constraints both affect the evolution of antler shape.
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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.