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Time-series of 5 minute water temperatures averages from Toolik Lake, Toolik Field Station, Alaska, Summer 2000.

Time-series of temperatures were measured using self-contained temperature loggers on taut-line moorings with a subsurface float 1 m below the air-water. Theses are the 5 minute averages of 10 second measuremsents.

Time-series of 5 minute water temperatures averages from Toolik Lake, Toolik Field Station, Alaska, Summer 2001

Time-series of 5 minute water temperatures averages from Toolik Lake, Toolik Field Station, Alaska, Summer 2002.

Time-series of 5 minute water temperatures averages from Toolik Lake, Toolik Field Station, Alaska, Summer 2003.

Time-series of 5 minute water temperatures averages from Toolik Lake, Toolik Field Station, Alaska, Summer 2005.

opencc-zeroDec 2014View details →

Data from: Inferring skeletal production from time-averaged assemblages: skeletal loss pulls the timing of production pulses towards the modern period

Age-frequency distributions of dead skeletal material on the landscape or seabed—information on the time that has elapsed since the death of individuals—provide decadal- to millennial-scale perspectives both on the history of production and on the processes that lead to skeletal disintegration and burial. So far, however, models quantifying the dynamics of skeletal loss have assumed that skeletal production is constant during time-averaged accumulation. Here, to improve inferences in conservation paleobiology and historical ecology, we evaluate the joint effects of temporally variable production and skeletal loss on postmortem age-frequency distributions (AFDs) to determine how to detect fluctuations in production over the recent past from AFDs. We show that, relative to the true timing of past production pulses, the modes of AFDs will be shifted to younger age cohorts, causing the true age of past pulses to be underestimated. This shift in the apparent timing of a past pulse in production will be stronger where loss rates are high and/or the rate of decline in production is slow; also, a single pulse coupled with a declining loss rate can, under some circumstances, generate a bimodal distribution. We apply these models to death assemblages of the bivalve Nuculana taphria from the Southern California continental shelf, finding that: (1) an onshore-offshore gradient in time averaging is dominated by a gradient in the timing of production, reflecting the tracking of shallow-water habitats under a sea-level rise, rather than by a gradient in disintegration and sequestration rates, which remain constant with water depth; and (2) loss-corrected model-based estimates of the timing of past production are in good agreement with likely past changes in local production based on an independent sea-level curve.

opencc-zeroDec 2015View details →

Data from: Explaining high-diversity death assemblages: undersampling of the living community, out-of-habitat transport, time-averaging of rare taxa, and local extinction

Molluscan benthic assemblages provide unique opportunities for understanding both spatial and temporal patterns of biodiversity. Species richness in the shell remains found at a site (i.e. the death assemblage) is typically several times higher than in the counterpart living assemblage, reflecting a complex history of settlement, dissemination and decomposition post-mortem. We used high-density temporal and spatial sampling (>37'000 individuals representing 196 taxa) of a shallow (5-8 m) nearshore sandy habitat off the coast of south-east Sardinia (Italy, Mediterranean Sea) to study the factors responsible for differences in the relative diversity of living and death assemblages. We found that death assemblages at all sites were considerably more diverse than living communities (1.5-3.5x more dead than living taxa after sample-size standardization), with 78% of all taxa solely recovered as empty shells, resulting in low live-dead agreement. By carefully filtering the raw data and combining them with habitat information extracted from the literature, we disentangled the major causes of this discordance and quantified their individual effects. Increased dead diversities could not be attributed to undersampling of the living community, but instead resulted from three phenomena of decreasing importance: the post-mortem, out-of-habitat transport of non-indigenous taxa (57% of dead-only taxa were allochthonous), the time-averaged presence of rare indigenous taxa (40% of dead-only taxa), and the likely local extirpation of a small number of species (3% of dead-only taxa). Our approach demonstrates how ecological inferences based on death assemblages can be improved by restricting analyses to demonstrably indigenous taxa, and highlights how mollusc shell remains can be used to provide information over both ecological and evolutionary timescales.

opencc-by-4.0Oct 2023View details →

Pre-training and fine-tuning dataset for transformers consisting of basic blocks and their execution times (average, minimum, and maximum) along with the execution context of these blocks, for various Cortex processors M7, M4, A53, and A72.

We are making public the dataset used for training CAWET, a tool for estimating the Worst-Case Execution Time (WCET) of basic blocks using the Transformer XL model. CAWET leverages the Transformer architecture for accurate WCET predictions, and its training involves two main phases: self-supervised pre-training and fine-tuning.CAWET undergoes a pre-training process on a substantial corpus of basic blocks to enable the Transformer to grasp the intricacies of the assembly language in focus. For this, we utilized CodeNet \cite{codenet}, a comprehensive collection of publicly submitted solutions to competitive programming challenges, comprising roughly 900,000 C programs. These programs were cross-compiled to the target architecture and subsequently disassembled using GNU binary utilities with objdump. The textual output from objdump, post a series of basic parsing operations (e.g., address extraction, separation of basic blocks), serves as the foundation for an extensive pre-training dataset. We employed this dataset to develop a vocabulary model utilizing sentence piece \cite{sentencepiece}. Following the completion of the sentence piece model's training, it becomes ready for use in tokenizing any binary programs written in the target instruction set.The fine-tuning phase of CAWET involves its adaptation to basic blocks along with their contextual information. Here, we used a varied and openly accessible collection of programs, namely, The Algorithms (accessible at: <a href="https://github.com/TheAlgorithms/C">https://github.com/TheAlgorithms/C</a>), MiBench \cite{mibench}, and Polybench \cite{polybench}.The provided zip file encompasses the following directories:Fine_Tuning: This includes four distinct files, each tailored for a specific processor: Cortex_M4, Cortex_M7, Cortex_A53, and Cortex_72. Each file encompasses the basic block under analysis (bbUA), the preceding 10 basic blocks executed prior to it, and timing information related to the bbUA (mean, min, max, normalization, etc.).Pre_Training: This comprises two extensive files, dataset_CortexA and dataset_CortexM, utilized for pre-training the transformers for the Masked Language Modeling Task (MLM). Additionally, it includes the sentence piece model and the necessary code to facilitate accurate tokenization.For additional information, please refer to the CAWET paper or contact us at <a href="mailto:ea_amalou@esi.dz">ea_amalou@esi.dz</a> Citation:@inproceedings{amalou2023cawet, title={CAWET: Context-Aware Worst-Case Execution Time Estimation Using Transformers}, author={Amalou, Abderaouf N and Fromont, Elisa and Puaut, Isabelle}, booktitle={35th Euromicro Conference on Real-Time Systems (ECRTS 2023)}, year={2023}, organization={Schloss Dagstuhl-Leibniz-Zentrum f{\"u}r Informatik}} Bibliography:codenet@article{codenet2021, title={CodeNet: A large-scale AI for code dataset for learning a diversity of coding tasks}, author={Puri, Ruchir and Kung, David S and Janssen, Geert and Zhang, Wei and Domeniconi, Giacomo and Zolotov, Vladimir and Dolby, Julian and Chen, Jie and Choudhury, Mihir and Decker, Lindsey and others}, journal={arXiv preprint arXiv:2105.12655}, year={2021}}sentencepiece@article{sentencepiece2018, title={Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing}, author={Kudo, Taku and Richardson, John}, journal={arXiv preprint arXiv:1808.06226}, year={2018}}mibench@inproceedings{polybench2014, title={Understanding polybench/c 3.2 kernels}, author={Yuki, Tomofumi}, booktitle={International workshop on polyhedral compilation techniques (IMPACT)}, pages={1--5}, year={2014}}polybench:  @inproceedings{mibench, title={MiBench: A free, commercially representative embedded benchmark suite}, author={Guthaus, Matthew R and Ringenberg, Jeffrey S and Ernst, Dan and Austin, Todd M and Mudge, Trevor and Brown, Richard B}, booktitle={4th IEEE international workshop on workload characterization}, year={2001}}

opencc-by-4.0Oct 2021View details →

Azimuthally averaged tangential wind and radial wind for Hurricane Patricia simulations and SAMURAI analysis at validation time

Azimuthally averaged tangential wind and radial wind for Hurricane Patricia simulations (the data format is .mat) and SAMURAI analysis at validation time (the data format is netcdf).

opencc-by-4.0Aug 2022View details →

Time-averaged simulations results for bi-phasic blood flow simulations in realistic microvascular networks for multi-capillary dilation scenarios mimicking pericyte ablation

Documentation to reproduce in silico analyses related to the manuscript Pericyte remodelling is deficient in the aged brain and contributes to impaired capillary flow and structure by Andrée-Anne Berthiaume, Franca Schmid, Stefan Stamenkovic, Vanessa Coelho-Santos, Cara D. Nielson, Bruno Weber, Mark W. Majesky and Andy Y. Shih Published in Nature Communications (doi: 10.1038/s41467-022-33464-w) All simulations are performed based on the in silico blood flow model with discrete red blood cell (RBC) tracking as described in Schmid et al., 2017, PLoS Comp Biol (doi: <a href="https://doi.org/10.1371/journal.pcbi.1005392">10.1371/journal.pcbi.1005392</a>). The bi-phasic blood flow simulations have been performed in two realistic microvascular networks from the somatosensory cortex of the mouse first published in Blinder et al., 2013, Nature Neuroscience (doi: 10.1038/nn.3426).  For further information and instructions please contact Franca Schmid (franca.schmid@unibe.ch, orcid.org/0000-0002-0689-9366). Simulation results: All time-averaged simulation results are saved as vascular graphs building on the python library igraph and stored as python pickle files (Python 2.7). For each simulation two files are available: verticesDict.pkl and edgesDict.pklcontaining all vertex and edge specific data, respectively. A summary of the vertex and edge attributes is provided below. The folder Baseline contains the simulation results for microvascular network 1 (MVN1) and MVN2 for the reference simulation, i.e. without any dilation. Folder Dilated contains the simulation results mimicking the four pericyte ablation scenarios. Subfolders dc_x.x contain the simulation results for the different diameter changes. Note that, folder dc_0.0 contains no new simulation results but is a dummy folder containing the information about the vessels to be dilated for the different dilation scenarios (namely edge attribute: toDilate and base_capillary).    Reproducing figure 8: Panels a-c: created by illustrating the simulation results with the open source software Paraview (v5.7.0). Panels d-f & h: can be generated by executing make_all_figures.py in Python 2.7 within the provided folder structure. Panel g: can be generated by executing make_figure_8g.py after installation of the the vgm-framework (further information see below).  Output: All created Figures are saved in the folder Figures. The associated source data is available in Excel format in the folder SourceData.   Edge attributes: diameter: vessel diameter [µm] mainAV: 1 if ascending venule main branch, 0 otherwise connectivity: vertex tuple to define location of edge flow: flow rate [µm3/ms] mainDA: 1 if descending arteriole main branch, 0 otherwise nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary htt: tube hematocrit [-] toDilate: 1 if vessel is dilated for the current dilation scenario, 0 otherwise base_capillary: 1 if vessel is the base capillary of the current dilation scenario, 0 otherwise   Vertex attributes: index: vertex index pressure: pressure [mmHg] nkind: 0: pial artery, 1: pial vein, 2: descending arteriole, 3: ascending venule, 4: capillary coords: vertex coordinates x,y,z [µm] pBC: pressure boundary conditions at inflow vertices [mmHg], None at internal nodes   Obtaining simulation results: General: <ul> <li>Running bi-phasic blood flow simulations requires setting-up the vgm-framework available at: <a href="https://github.com/Franculino/vgm.git">https://github.com/Franculino/vgm.git</a> (v.1.0).</li> <li>vgm is written in Python 2.7 and builds on standard python libraries.</li> <li>vgm has been used on macOS, Ubuntu and Windows Systems.</li> <li>Installation time < 5min. Further details available within the vgm README.</li> <li>Runtime depends on the network size, the chosen blood flow model and the initial conditions (e.g. ~8hrs for a Restart simulation of MVN1 with the bi-phasic blood flow model, see Restarty.py).</li> <li>scripts/Test.py provides an example how a simulation can be initiated. A Demo case is provided (details see below).</li> <li>Output: sampledict_BackUp_xx.pkl</li> <li>The bi-phasic blood flow model can be applied on all kind of microvascular graphs.</li> </ul> Specific for current application: <ul> <li>Simulations are a restart on the statistical steady state of the baseline cases.</li> <li>All relevant pre-processing functions for the current study are available in scripts/find_stroke_locations.py. Further details are available from the definition of the different functions.</li> <li>The simulations are initiated with scripts/Restart.py.</li> <li>To obtain the time-averaged simulation results scripts/01_put_together_sampledicts.py and scripts/02_convergenceDiscrete.py need to be executed. This results in the file G_averaged.pkl that is used for further analyses.</li> </ul> Demo: <ul> <li>Contains a small hexagonal microvascular network to test the code.</li> <li>1) Run Test.py to start the simulation</li> <li>2) Run 01_put_together_sampledicts.py</li> <li>3) Run 02_convergenceDiscrete.py to obtain time-averaged results (G_averaged.pkl)</li> </ul>

opencc-by-4.0Jan 2018View details →

Role of the horizontal SST structure in the modulation of heavy precipitations over the Ligurian Sea - time averaged data

Time averaged data used in the analysis the paper Meroni, A. N., A. Parodi and C. Pasquero "Role of the horizontal SST structure in the modulation of heavy precipitations over the Ligurian Sea". The variables are SST,  downwind SST gradient, SST laplacian, surface wind magnitude and convergence.

opencc-by-4.0Aug 2024View details →

Supplementary dataset for the paper "A time window averaging method to mitigate the impact of shell growth trends on Tridacna d18O records".

Data and code for the paper "A time window averaging method to mitigate the impact of shell growth trends on Tridacna δ18O records". We have included an example in the pseudo-Tridacna package v2.1 to demonstrate how users can generate pseudo-Tridacna series.

publicNov 2017View details →

Data from: Explaining high-diversity death assemblages: undersampling of the living community, out-of-habitat transport, time-averaging of rare taxa, and local extinction

Open the record for dataset details and reuse information.