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Data for "Towards quantum gravity with neural networks: Solving quantum Hamilton constraints of 3d Euclidean gravity in the weak coupling limit"
<h2>1. Repository Information</h2> <p>This repository contains the data produced during the work discussed in in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad7c14" target="_blank" rel="noopener">Towards quantum gravity with neural networks: Solving quantum Hamilton constraints of 3d Euclidean gravity in the weak coupling limit</a>". Please refer to this paper for more details on how the data was produced.</p> <p> </p> <h2>2. Citing</h2> <p>In addition to citing this repository, please also cite the paper mentioned above if you use the data. The citation is:</p> <p>Hanno Sahlmann and Waleed Sherif 2024 <em>Class. Quantum Grav.</em> <strong>41</strong> 215006</p> <p> </p> <h2>3. File Description</h2> <p>In this repository, you will find 4 general directories (here called parent directories):</p> <ol> <li>Ground Energy + Fluctuations</li> <li>Misc</li> <li>Quantum Constraint</li> <li>Volume</li> </ol> <p>Each of these directories correposnd to different data produced and discussed in the corresponding parts in the paper mentioned above (e.g. the directory "Ground Energy + Fluctuations" contains the data used in Table 1 and Table 2 in the paper while the "Volume" directory contains the data used in Section 4.3 of the paper).</p> <p>Some of these parent directories, which involve simulations solving constraints, contain within them several sub-directories (child directories) corresponding to different produced data. The raw data of the simulation can be found in a <code>.json</code> file inside the child directories.</p> <p> </p> <h2>4. Usage</h2> <h3>4.1 Raw Simulation Data</h3> <p>The <code>.json</code> files include the raw data produced during the study. These files can be easily accessed using a python script, as an example, by using:</p> <p><code>import json</code></p> <p><code>filePath = ...</code></p> <p><code>data = json.load(open(filePath))</code></p> <p>where <code>filePath</code> should hold the correct path to the local data once downloaded. Once loaded, the data is handled as a python <code>dict</code>.</p> <p> </p> <p>The dictionary will have <em>at least one</em> parent key called "Energy". The data in the "Energy" key corresponds to the data being minimised. The data in any other parent key correspond to operators which were being observed during the simulation. For example, in the data in the "Quantum Constraint" directory, some .json files will have multiple parent keys such as FG, H, HG, .... Each of these keys correspond to different operators which were observed during that simulation. Each parent key is yet another dictionary in itself. The structure of the dictionaries corresponding to any parent key are always the same and always include the keys:</p> <ul> <li>iters</li> <li>Mean</li> <li>Variance</li> <li>Sigma</li> <li>R_hat</li> <li>TauCorr</li> </ul> <p>Hence, to access the "Mean" values, you use <code>data["Energy"]["Mean"]</code> (or alternatively <code>data["FG"]["Mean"]</code> if you wish to observe the value of the F + G operator during the simulation). The data represents the values during a simulation of typically 500 iterations, hence, each of the keys mentioned above will correspond to an array of 500 items. The <code>iters</code> array includes merely the iteration number. The <code>Mean</code> array includes the value of the expectation value of the constraint at the corresponding iteration. The <code>Variance</code>, <code>Sigma</code>, <code>R_hat</code> and <code>TauCorr</code> includes the values of the variance and error in the expectation value at the given iteration as well as the split R-hat diagnostic and the time correlation also in the given iteration. </p> <p> </p> <h3>4.2 Variational State Data</h3> <p><em><strong>The files for the variational arrays are too large to be uploaded to a general repository hosting service. Therefore, they will be provided directly upon request in a direct download link. Please contact the author of the paper (Waleed Sherif, email: waleed.sherif@fau.de) for accessing the data. </strong></em></p> <p> </p> <h3>4.3 Fluctuation results</h3> <p>In some child directories, there will be a <code>.txt</code> file which includes the output of the calculation of the expectation value of some operators and their quantum fluctuations. These are only results, and not data, as the data can only be computed during the simulation.</p> <p> </p> <h2>4.4 Probabilities</h2> <p>The "Misc/Probabilities" directory contains <code>.npy</code> files which should be handled in the same manner as the variational states. These files correspond to the probability simulations conducted in section 4.4.4 in the paper.</p> <p> </p> <h2>5. Contact</h2> <p>Shall you have any unanswered questions regarding the usage of the data, please contact the author:</p> <p>Waleed Sherif</p> <p>email: waleed.sherif@fau.de</p> <p> </p> <h2>6. References</h2> <p>The data provided in this repository was produced using the <a href="https://github.com/netket" target="_blank" rel="noopener">NetKet</a>[1] package</p> <p>[1] <a href="https://doi.org/10.21468/SciPostPhysCodeb.7" target="_blank" rel="noopener">doi: 10.21468/SciPostPhysCodeb.7</a></p>
Fig. 6 in Constraints on Phylogenetic Interrelationships among Four Free-living Litostomatean Lineages Inferred from 18S rRNA gene-ITS Region sequences and Secondary Structure of the ITS2 molecule
Fig. 6. Consensus secondary structure of the ITS2 molecule of free-living litostomateans. The ITS2 molecule shows an internal loop, radiating two helices. There is an A-G bulge in the stem of helix III.
Evaluation of cattle breeds and year of insemination on conception rate and artificial insemination constraints in Ethiopia
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Performance of Slab Geometry Constraints on Rapid Geodetic Slip Models, Tsunami Amplitude, and Inundation Estimates in Cascadia
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FIGURE 5 in Constraints on the timescale of animal evolutionary history
FIGURE 5. Calibration diagram for actinopterygians (basal teleosts and Clupeocephala).
FIGURE 6 in Constraints on the timescale of animal evolutionary history
FIGURE 6. Calibration diagram for actinopterygians (Acanthomorpha).
FIGURE 3 in Constraints on the timescale of animal evolutionary history
FIGURE 3. Calibration diagram for gnathostomes.
FIGURE 8 in Constraints on the timescale of animal evolutionary history
FIGURE 8. Calibration diagram for amniotes.
FIGURE 1 in Constraints on the timescale of animal evolutionary history
FIGURE 1. Calibration diagram for metazoans.
FIGURE 4 in Constraints on the timescale of animal evolutionary history
FIGURE 4. Calibration diagram for actinopterygians (Chondrostei, Holostei).
FIGURE 7 in Constraints on the timescale of animal evolutionary history
FIGURE 7. Calibration diagram for tetrapods.
DNA codes under multiple constraints
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Data for: Thermochronologic constraints on the extensional history of the southeastern Black Mountains, Death Valley, CA
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Fit summary of "X-Shooting ULLYSES: Massive Stars at low metallicity IX: Empirical constraints on mass-loss rates and clumping parameters for OB supergiants in the Large Magellanic Cloud"
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Data from: Ecotypic divergence in Crepis tectorum (Asteraceae): inferring trait lability and correlational constraints from hormonally manipulated phenotypes
Despite long-standing interest in the evolutionary role of plant hormones, relatively few studies have used hormonally manipulated phenotypes to address questions about phenotypic evolution in plants. In the present investigation, I subjected plants of Crepis tectorum subsp. pumila to simple gibberellin (GA) treatments under both field and greenhouse conditions to assess developmental lability and correlational constraints of phenotypic traits that distinguish this dwarf ecotype from conspecific populations of the much taller weed ecotype (subsp. tectorum). The hormonally manipulated plants largely phenocopied the weed ecotype in leaf shape, plant stature and branching habit, indicative of both high lability and tight integration of traits reflecting gross morphology. Floral size traits sometimes declined after GA application, especially under field conditions. The latter result conflicts with the positive correlations between floral and vegetative size traits seen in previous comparative analyses and point to a tradeoff that could act as a constraint on ecotype divergence. The response to GA was consistent in direction for most traits, as opposed to the magnitude of response, which varied depending on the trait, the population, the growing environment, and the timing and level of hormone application. Taken together, the results highlight the potential for simple hormonal changes to cause large, plastic shifts in phenotype, but also illustrate the constrained nature of such influences.
Data from: Comparing process-based and constraint-based approaches for modeling macroecological patterns
Ecological patterns arise from the interplay of many different processes, and yet the emergence of consistent phenomena across a diverse range of ecological systems suggests that many patterns may in part be determined by statistical or numerical constraints. Differentiating the extent to which patterns in a given system are determined statistically, and where it requires explicit ecological processes, has been difficult. We tackled this challenge by directly comparing models from a constraint-based theory, the Maximum Entropy Theory of Ecology (METE) and models from a process-based theory, the size-structured neutral theory (SSNT). Models from both theories were capable of characterizing the distribution of individuals among species and the distribution of body size among individuals across 76 forest communities. However, the SSNT models consistently yielded higher overall likelihood, as well as more realistic characterizations of the relationship between species abundance and average body size of conspecific individuals. This suggests that the details of the biological processes contain additional information for understanding community structure that are not fully captured by the METE constraints in these systems. Our approach provides a first step towards differentiating between process- and constraint-based models of ecological systems and a general methodology for comparing ecological models that make predictions for multiple patterns.
Data from: Sequencing of the needle transcriptome from Norway spruce (Picea abies Karst L.) reveals lower substitution rates, but similar selective constraints in gymnosperms and angiosperms
BACKGROUND: A detailed knowledge about spatial and temporal gene expression is important for understanding both the function of genes and their evolution. For the vast majority of species, transcriptomes are still largely uncharacterized and even in those where substantial information is available it is often in the form of partially sequenced transcriptomes. With the development of next generation sequencing, a single experiment can now simultaneously identify the transcribed part of a species genome and estimate levels of gene expression. RESULTS: mRNA from actively growing needles of Norway spruce (Picea abies) was sequenced using next generation sequencing technology. In total, close to 70 million fragments with a length of 76 bp were sequenced resulting in 5 Gbp of raw data. A de novo assembly of these reads, together with publicly available expressed sequence tag (EST) data from Norway spruce, was used to create a reference transcriptome. Of the 38,419 PUTs (putative unique transcripts) longer than 150 bp in this reference assembly, 83.5% show similarity to ESTs from other spruce species and of the remaining PUTs, 3,704 show similarity to protein sequences from other plant species, leaving 4,167 PUTs with limited similarity to currently available plant proteins. By predicting coding frames and comparing not only the Norway spruce PUTs, but also PUTs from the close relatives Picea glauca and Picea sitchensis to both Pinus taeda and Taxus mairei, we obtained estimates of synonymous and non-synonymous divergence among conifer species. In addition, we detected close to 15,000 SNPs of high quality and estimated gene expression differences between samples collected under dark and light conditions. CONCLUSIONS: Our study yielded a large number of single nucleotide polymorphisms as well as estimates of gene expression on transcriptome scale. In agreement with a recent study we find that the synonymous substitution rate per year (0.6 x 10-09 and 1.1 x 10-09) is an order of magnitude smaller than values reported for angiosperm herbs. However, if one takes generation time into account, most of this difference disappears. The estimates of the dN/dS ratio (non-synonymous over synonymous divergence) reported here are in general much lower than 1 and only a few genes showed a ratio larger than 1.
Data from: An expansion of age constraints for microbial clades that lack a conventional fossil record using phylogenomic dating
Most microbial taxa lack a conventional microfossil or biomarker record, and so we currently have little information regarding how old most microbial clades and their associated traits are. Building on the previously published oxygen age constraint, two new age constraints are proposed based on the ability of microbial clades to metabolize chitin and aromatic compounds derived from lignin. Using the archaeal domain of life as a test case, phylogenetic analyses, along with published metabolic and genetic data, showed that members of the Halobacteriales and Thermococcales are able to metabolize chitin. Ancestral state reconstruction combined with phylogenetic analysis of the genes underlying chitin degradation predicted that the ancestors of these two groups were also likely able to metabolize chitin or chitin-related compounds. These two clades were therefore assigned a maximum age of 1.0 Ga (when chitin likely first appeared). Similar analyses also predicted that the ancestor to the Sulfolobus solfataricus-Sulfolobus islandicus clade was able to metabolize phenol using catechol dioxygenase, so this clade was assigned a maximum age of 475 Ma. Inferred ages of archaeal clades using relaxed molecular clocks with the new age constraints were consistent with those inferred with the oxygen age constraints. This work expands our current toolkit to include Paleoproterozoic, Neoproterozoic, and Paleozoic age constraints, and should aid in our ability to phylogenetically reconstruct the antiquity of a wide array of microbial clades and their associated morphological and biogeochemical traits, spanning deep geologic time. Such hypotheses-although built upon evolutionary inferences-are fundamentally testable.
Data from: Patterns of thermal constraint on ectotherm activity
Thermal activity constraints play a major role in many aspects of ectotherm ecology, including vulnerability to climate change. Therefore, there is strong interest in developing general models of the temperature dependence of activity. Several models have been put forth (explicitly or implicitly) to describe such constraints; nonetheless, tests of the predictive abilities of these models are lacking. In addition, most models consider activity as a threshold trait instead of considering continuous changes in the vigor of activity among individuals. Using field data for a tropical lizard (Anolis cristatellus) and simulations parameterized by our observations, we determine how well various threshold and continuous-activity models match observed activity patterns. No models accurately predicted activity under all of the thermal conditions that we considered. In addition, simulations showed that the performance of threshold models decreased as temperatures increased, which is a troubling finding given the threat of global climate change. We also find that activity rates are more sensitive to temperature than are the physiological traits often used as a proxy for fitness. We present a model of thermal constraint on activity that integrates aspects of both the threshold model and the continuous-activity model, the general features of which are supported by activity data from other species. Overall, our results demonstrate that greater attention should be given to fine-scale patterns of thermal constraint on activity.
Variability in codon usage in Coronaviruses is mainly driven by mutational bias and selective constraints on CpG dinucleotide
<p>Supplementary Figures and Tables for the article called: " Variability in codon usage in Coronaviruses<em> </em>is mainly driven by mutational bias and selective constraints on CpG dinucleotide<sup>"</sup></p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.