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3,472 results for “Balance”
Estimating surface water availability in high mountain rock slopes using a numerical energy balance model
<p>Model output, forcing data and physical parameters used to estimate water and energy balance. The model was calibrated with field measurements from a study site in the Mont-Blanc massif, at 3842 m a.s.l, at a slope of 55 deegrees and aspect azimut of 150 degrees (south-east). The different ModelOutput files are from simulations at different elevastions (from 4800 m to 2700 m at steps of 300 m). We used the CryoGrid community model (version 1.0) toolbox (Westermann et al., 2022) to simulate the 1D ground thermal regime and ice/water balance, and estimate the availability of surface water and its potential for infiltration in rock fractures. The S2M-SAFRAN dataset combines output from a numerical weather prediction model and <em>in situ</em> observations, and was originally developed for operational needs to estimate avalanche hazard in mountainous areas (Durand et al., 1993). The S2M-SAFRAN dataset that we used is available for various mountain areas, at elevation steps of 300 m, and with an hourly resolution between the years 1958 to 2021 (Vernay et al., 2022). It includes most parameters that are required for modeling with CryoGrid: Relative humidity, air T, incoming long wavelength radiation, incoming short wavelength solar radiation, and wind speed. To complete the forcing data we used top of the atmosphere incident solar radiation from ERA5 global reanalysis dataset (Hersbach et al., 2020).</p>
Arctic glaciers mass balance from satellite gravimetry only
<p>This document describes the LEGOS-Magellium mass balance dataset for Arctic glacier regions based on satellite gravimetry only. A similar dataset using an a priori based on DEM differencing has been submitted to GLAMBIE (Pfeffer et al., 2024). previously. The dataset submitted here does not use an a priori, but uses only satellite gravimetry data. </p> <p>The total mass balance is evaluated for five regions of the 6th version of the Randolph Glacier Inventory , namely the Arctic Canada North, Arctic Canada South, Iceland, Svalbard, and, Russian Arctic. The total mass balance of Arctic glaciers is evaluated mainly based on satellite gravimetry measurements. An ensemble approach updated from Blazquez et al. (2018) is adopted to evaluate uncertainties associated with the processing and post-processing of GRACE (Gravity Recovery And Climate Experiment) and GRACE-FO (GRACE-Follow On) data. The effect of land hydrology is estimated for each region, but not corrected in the total mass balance dataset, because of the small water mass balance values and large errors inherent to hydrological models. Total mass changes expressed in Gt are estimated from April 2002 to September 2022 for five RGI regions. The uncertainty on total mass changes is provided with a confidence interval of 95%. The dataset is provided in the GlaMBIE CSV file format.</p> <p>The data product has been developed in collaboration between LEGOS and Magellium within the scope of the hybridation<br>challenge funded by the CNES (R&T Hybrid Spatial Gravimetry 2022/2023).</p> <p><strong>Reference</strong>:</p> <ul> <li>Blazquez, A., Meyssignac, B., Lemoine, J., Berthier, E., Ribes, A., & Cazenave, A. (2018). Exploring the uncertainty in GRACE estimates of the mass redistributions at the Earth surface : Implications for the global water and sea level budgets. Geophysical Journal International, 215(1), 415‑430. https://doi.org/10.1093/gji/ggy293</li> <li>Pfeffer, J., Coupry, B., Berthier, E., Blazquez, A., & Barnoud, A. (2024). Arctic Glaciers Mass Balance from satellite gravimetry and DEM differencing [Jeu de données]. Zenodo. https://doi.org/10.5281/ZENODO.13134559</li> </ul> <p> </p>
Dataset and models of TMLR 2024 Paper "Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis"
<p>This is a part of dataset and models of the paper published in TMLR 2024 (Transactions on Machine Learning Research, <a href="https://jmlr.org/tmlr/" target="_blank" rel="noopener">https://jmlr.org/tmlr/</a>).</p> <p>Including training datasets, testing datasets, and models.</p> <p>The example program for using this file will be put on the author's github repo branch: <a href="https://github.com/DSobscure/cgi_drl_platform/tree/game_balance_measures_tmlr" target="_blank" rel="noopener">https://github.com/DSobscure/cgi_drl_platform/tree/game_balance_measures_tmlr</a></p> <p> </p>
MARv3.10 outputs: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates
<p>MARv3.10 outputs used in:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></p> <ul> <li>MARv3.10 forced by ERA-Interim outputs with monthly values of SMB and components (kg m<sup>-2</sup> month<sup>-1</sup>), and (near-) surface temperature (°C) over the Antarctic ice sheet (1981--2018)</li> <li>Grid file used in MAR simulation</li> </ul> <p>Be carreful that the unit metadata in the netcdf files from SMB and its components are uncorrect. <strong>Values are in kg m<sup>-2</sup> month<sup>-1</sup></strong> instead of kg m<sup>-2</sup> day<sup>-1</sup>.<br> <br> If you need other variables or output frequencies from MAR, write me (c2kittel@gmail.com) and I will be glad to help you. I will also be happy to share the scripts I have developed to analyse the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR outputs. However, note that these outputs are now considered as deprecated since new outputs using a more recent model version (MARv3.11) and forcing (ERA5) have been published (see Kittel et al., 2021: https://tc.copernicus.org/articles/15/1215/2021/).<br> <br> Data usage notice:</p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgements should have language similar to the below that contained informations related to MAR. In order to document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact C. Kittel and C. Agosta to add their works in the list of MAR-related publications. </p> <p>"We thank the MAR team which make available the model outputs, as well agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR simulations."</p> <p>You should also refer to and cite the following paper:</p> <p><em>Mottram, R., Hansen, N., Kittel, C., van Wessem, M., Agosta, C., Amory, C., Boberg, F., van de Berg, W. J., Fettweis, X., Gossart, A., van Lipzig, N. P. M., van Meijgaard, E., Orr, A., Phillips, T., Webster, S., Simonsen, S. B., and Souverijns, N.: What is the Surface Mass Balance of Antarctica? An Intercomparison of Regional Climate Model Estimates, The Cryosphere Discuss. [preprint], https://doi.org/10.5194/tc-2019-333, accepted, 2020.</em></p>
A database solutions for the type two assembly line balancing problems
<p> Assembly Line Balancing Problems have a significant impact on performance of manufacturing systems, specially for the cases of mass production. These problems are widely cited and treated in the literature. </p> <p>One from the most important variants of those problems is the “Task Restrictions Assembly Line Balancing Problem” of type 2. For this problem, a set of tasks need to be affected to a predefined number of stations m from the way that minimises the cycle time and respects a set of constraints related to precedence and compatibility between tasks (Triki et al., 2016).</p> <p>For this variant we suggest an innovative speed and effective approach based on the hybridisation of two powerful tools: the ant colony optimisation and the genetic algorithm. The effectiveness of this approach is evaluated through a set of instances collected from the literature (Thomas, 1990; Triki et al., 2016) .</p> <p>This document presents the best generated solutions for those problems. </p> <p> </p> <p> </p>
Visual perturbation of balance suggests impaired motor control but intact visuomotor processing in Parkinson's disease, J Neurophysiol (2021): Data
<p>Data set accompanying the publication:</p> <p>Engel, D., Student, J., Schwenk, J., Morris, A. P., Waldthaler, J., Timmermann, L., & Bremmer, F. (2021). Visual perturbation of balance suggests impaired motor control but intact visuomotor processing in Parkinson's disease. <em>Journal of neurophysiology</em>, <em>126</em>(4), 1076–1089. https://doi.org/10.1152/jn.00183.2021</p>
Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework"
<p><em>Amonthly_files.tar.gz</em> contains the gridded monthly averaged quantities used in the manuscript Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" for each year between 2000 and 2018.</p> <p>Files containing "simba" in their name contain quantities related to the sea ice mass balance (volume of melt/growth...)</p> <p>Files containing "icemod" in their name contain other quantities related to sea ice properties (thickness, concentration...)</p> <p>In case information is missing, do not hesitate to contact guillaume.boutin@nersc.no , heather.regan@nersc.no or einar.olason@nersc.no</p> <p>This research has been funded by the Norwegian Research Council (Nansen Legacy: grant no. 27673, FRASIL: grant no. 263044, and ARIA: grant no. 302934), JPI Climate and JPI Oceans (MEDLEY project, under agreement with the Norwegian Research Council, grant no 316730), and by Copernicus Marine Environment Monitoring Service (CMEMS) WIzARd project. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union<br> Copernicus Marine Environment Monitoring Services (contract no.<br> 69), and the European Space Agency through the Cryosphere Virtual Laboratory (CVL, grant no. 4000128808/19/I-NS).</p>
A striatal circuit balances learned fear in the presence and absence of sensory cues
<p>This repository stores the raw data that gave rise to the study by Kintscher et al. 2023, published by eLife (eLife2023;12:e75703; DOI:https://doi.org/10.7554/eLife.75703), as well as a preprint at bioRxiv (doi: 10.1101/2021.12.09.471922). Please refer to the original publication regarding experimental design and methodological details of data acquisition and analysis. Below we supply information on the provided metadata files which, in turn, refer to individual raw data files.</p> <p><strong>General structure of the repository:</strong></p> <ul> <li>the raw data is organized in 22 subsets defined by the figures or supplementary figures they contribute to. Each subset is documented by its own metadata file;</li> <li>the metadata files listing names of the individual data files were made in “.csv” format, one per data subset. Field separator: comma;</li> <li>all metadata are summarized in the main metadata file “metadata_master.csv”. Field separator: comma;</li> <li>the dataset expands into the second repository accessible at the following doi: 10.5281/zenodo.7522958</li> </ul> <p> </p> <p><strong>Description of the non-textual data formats:</strong></p> <ul> <li>due to its excessive volume, the raw data for in-vivo microendoscopic Ca<sup>2+</sup> recordings is not present in the current repository but will be made available upon request from the corresponding author;</li> <li>certain blocks of data, such as the output of CaImAn package, or slide scanner imaging projects, contained multiple smaller files and thus were compressed into encryption- and password-free .zip archives;</li> <li>video recordings of animal behavior during the fear conditioning protocol were provided as unmodified “.wmv” files created by the VideoFreeze acquisition software (Med Associates Inc). Video stream parameters: wmv3 codec, color space yuv420p, 320x240 pixels, 30 fps, bitrate 300 kb/s. Alternatively (Figure 4 – figure supplement 3), videos were stored by EthoVisionXT software as “.mpg” files with the following parameters: AVI container, mpeg4 codec, color space yuv420p, 1280x1024 pixels, 30 fps, bitrate 1206 kb/s;</li> <li>widefield fluorescent images of single coronal sections were either uploaded as original unmodified data in a proprietary .vsi format of Olympus slide scanning microscope (for Figure 1 – figure supplement 1, Figure 3 – figure supplement 1, Figure 7, Figure 7 – figure supplement 1), or were first binned and saved as a composite TIFF format at a resolution ~10.3 micrometers/pixel (for all the post-hoc images showing the virus expression and the fiber/lens placement). Both “.vsi” and TIFF formats are readable by Bioformats plugin working under open-source free software packages FIJI/ImageJ (<a href="https://fiji.sc/">https://fiji.sc/</a> , <a href="https://imagej.net/Fiji/Downloads">https://imagej.net/Fiji/Downloads</a>), or QuPath (<a href="https://qupath.github.io/">https://qupath.github.io/</a>). Imaging metadata such as pixel resolution is embedded inside the individual .vsi or TIFF files. Correspondence between the fluorescent signals and image color channels is provided in the .csv metadata files;</li> <li>confocal fluorescent image stack underlying images in Figure 7B is provided in original proprietary “.lsm” format (Carl Zeiss). The imaging metadata is embedded in .lsm format, which can be read by the Bioformats plugin under FIJI/ImageJ or QuPath;</li> <li>data for patch clamp recordings (Figure 5 – figure supplement 2, Figure 8, Figure 8 – figure supplement 1, Figure 9) are provided as original .dat files from the acquisition software PatchMaster (HEKA Elektronik, Germany). These files can be imported using an Igor Pro extension “bpc_ReadHeka.xop” (for 32-bit Igor Pro versions 5.xx - 6.37) by Holger Taschenberger (<a href="https://www.wavemetrics.com/project/bpc_ReadHeka">https://www.wavemetrics.com/project/bpc_ReadHeka</a>), or using a Python script by Luke Campagnola (<a href="https://github.com/campagnola/heka_reader">https://github.com/campagnola/heka_reader</a>), or using a Matlab script “HEKA PatchMaster Importer” by Christian Keine (<a href="https://github.com/ChristianKeine/HEKA_Patchmaster_Importer">https://github.com/ChristianKeine/HEKA_Patchmaster_Importer</a>).</li> </ul>
Dijkstra's Algorithm using a Fibonacci Heap, Binary Heap and Self-balancing Binary Tree
<p>Efficient C++ implementation of Dijkstra's algorithm using Fibonacci Heaps, Binary Heaps and Self-balancing Binary Trees. Also contains two .csv data sets from expeiments using directed planar graphs and random graphs of varying densities.</p> <p>Paper is published at </p> <p>Lewis, R. (2023), "A Comparison of Dijkstra's Algorithm Using Fibonacci Heaps, Binary Heaps, and Self-Balancing Binary Trees", <a href="https://arxiv.org/abs/2303.10034">arXiv:2303.10034</a>, <a href="https://doi.org/10.48550/arXiv.2303.10034">https://doi.org/10.48550/arXiv.2303.10034</a></p>
Data for: Detecting Long-Term Balancing Selection Using Allele Frequency Correlation
<p>Genome-wide and top 1% scores for 1KG project data output from BetaScan reported in:</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/28981714/">Detecting Long-Term Balancing Selection Using Allele Frequency Correlation.</a></p> <p>Siewert KM, Voight BF. Mol Biol Evol. 2017 Nov 1;34(11):2996-3005. doi: 10.1093/molbev/msx209.</p> <p>PMID: 28981714</p> <p>Code available at: https://github.com/ksiewert/BetaScan</p>
Data for: BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data
<p>Genome-wide scan using BetaScan2 in 1KG populations report in:</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/32011695/">BetaScan2: Standardized Statistics to Detect Balancing Selection Utilizing Substitution Data.</a></p> <p>Siewert KM, Voight BF.Genome Biol Evol. 2020 Feb 1;12(2):3873-3877. doi: 10.1093/gbe/evaa013.</p> <p>PMID: 32011695 </p> <p>Code available at: https://github.com/ksiewert/BetaScan</p>
Dataset for "A computational fluid dynamics—Population balance equation approach for evaporating cough droplets transport"
<p>Dataset for figures and tables of the article "A computational fluid dynamics—Population balance equation approach for evaporating cough droplets transport" submitted to "International Journal of Multiphase Flow".</p>
Simulation result from "Simulating Bark Beetle Outbreak Dynamics and their Influence on Carbon Balance Estimates with ORCHIDEE r7791"
<p>Eight locations were selected which represent the range of climatic conditions within the distribution area of spruce in Europe (<em>Picea Abies</em> Karst L.) as shown in Table 4. Half-hourly weather data from the FLUXNET database <a href="https://www.zotero.org/google-docs/?ibBw15">(Pastorello et al., 2020)</a> for these locations were used to drive ORCHIDEE. Some of these locations (FON, SOR, HES, COL, WET) are not populated with spruce but all are located within the species distribution. For each location, a pure spruce stand was simulated and the available FLUXNET data was looped to simulate a 100-year period. The study did not investigate the effect of species mixture in the simulation experiments. Other inputs, including soil texture, pH and soil color were obtained from the USDA map derived from <a href="https://www.zotero.org/google-docs/?aaWPI6">Eswaran et al. (2003</a>), for the corresponding pixel.</p> <p>The amount of fresh breeding woody substrate inputs used by the bark beetles to breed was controlled by modifying the maximum wind speed of a windthrow event in ORCHIDEE. Seven wind speeds ranging between 19 m/s and 40 m/s were selected (Table 3). This range is justified by the observation that mean wind speeds below 19 m/s could not trigger a windthrow event in ORCHIDEE <a href="https://www.zotero.org/google-docs/?jEqNDm">(Chen et al., 2018)</a> while for wind speeds exceeding 40 m/s, more than 60% of the trees are uprooted, leaving too few living trees to trigger a bark beetle outbreak within the same pixel. </p> <p>To investigate the impact of windthrow intensity and background climate on bark beetle outbreaks, the study conducted a total of 56 [8 sites x 7 wind speed intensities] simulations as given in table 3. The same 56 simulations were also used to analyze the sensitivity of the carbon balance of spruce forests to windthrow intensity and background climate.</p> <p>Where most land surface models use a turnover time to simulate continuous mortality <a href="https://www.zotero.org/google-docs/?5jGfXF">(Thurner et al., 2014; Pugh et al., 2019)</a>, ecological reality is better described by abrupt mortality events. An idealized simulation experiment was used to qualify the impact of abrupt mortality on net biome productivity by changing from a framework in which mortality is approximated by a constant background mortality to a framework in which mortality occurs in abrupt, discrete events. To test the impact of a change in mortality framework two versions of ORCHIDEE were compared to create an idealized simulation experiment: (1) a version simulating mortality as a continuous process, labeled ”the continuous version”, and (2) the version capable of simulating abrupt mortality from windthrow and subsequent bark beetle outbreaks, labeled ”the abrupt version”. The effect of simulating abrupt mortality was evaluated over 20-, 50-, and 100-year time horizons.</p> <p>The effect of changing the framework of simulating mortality from continuous to abrupt was qualified on the basis of 112 simulations (8 sites x 7 wind speeds x 2 model versions) of 100 years each. The simulations with abrupt mortality were run first. Subsequently, the number of trees killed was quantified and used as a reference value for the continuous mortality set-up. This approach resulted in the same quantities of dead trees at the end of the simulation for both frameworks, which then differed only in the timing of the simulated mortality. This precaution is necessary to avoid comparing two different mortality regimes where the result would mainly be explained by the intensity of the mortality rather than by its underlying mechanisms. </p>
Model output, drivers and parameters for Ecosystem Recovery from Disturbance is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance Between Vegetation and Soil-Microbial Processes
Files used to generate the data for figures in: Rastetter, EB, Kling, GW, Shaver, GR, Crump, BC, Gough, L. Ecosystem Recovery from Disturbance Is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance between Vegetation and Soil-Microbial Processes. Ecosystems (2020). https://doi.org/10.1007/s10021-020-00542-3. This paper present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen.
Water Balance Modeling Project at the Sevilleta National Wildlife Refuge, New Mexico: Vegetation Plot Data (1995-1998)
The water balance vegetation plots were part of a larger water balance monitoring project at the Sevilleta LTER. The plots were designed to measure the percent cover of photosynthetic/transpiring (green) plant species at specific sites where time domain reflectometry (TDR) probes and weather stations were already installed. In 1995, there were three sites (Field Station, Deep Well and Rio Salado). A 30m x 30m plot was installed at each site, and collection of vegetation data commenced in July 1995. Percent cover (green) and species identities were recorded monthly at a representative sample of 1m square quadrats within each plot.
Performance results of LeanMD on the Joliot-Curie supercomputer using different load balancing algorithms
<p>This dataset contains the raw output files generated from the execution of LeanMD on the Joliot-Curie supercomputer (20 SKL Irene nodes), the scripts used to generate them, and the scripts used to parse these results for statistical analysis and plotting.</p> <p><strong>Software information</strong>:</p> <ul> <li>OS: Red Hat Enterprise Linux 7.6</li> <li>OpenMPI: version 2.0.4</li> <li>Compilers: C/C++ Intel 17.0.6.256</li> <li>Charm++ version: v6.9.0-rc3, build mpi-linux-x86_64 --with-production</li> <li>LeanMD source: <a href="https://charm.cs.illinois.edu/gerrit/gitweb?p=benchmarks/leanmd.git">https://charm.cs.illinois.edu/gerrit/gitweb?p=benchmarks/leanmd.git</a></li> <li>Additional load balancers source: <a href="https://github.com/viniciusmctf/packing-schemes/tree/packs_2019-v1">https://github.com/viniciusmctf/packing-schemes/tree/packs_2019-v1</a></li> <li>Charm++, LeanMD, and the load balancers were compiled with -O3</li> </ul> <p><strong>File information</strong>:</p> <p>The raw result files are organized in four directories (oct18, oct23, oct24, and oct24_2).<br> Each directory contains the results of one batched execution in the supercomputer.<br> Each batch is composed of 10 repetitions of a set of experiments.<br> Each set of experiments includes different load balancing algorithms and different problem sizes.<br> Each set is randomly ordered to avoid interference coming from a specific order of execution.<br> Each raw file contains the appended output of the application and its load balancer for all 10 repetitions.<br> The name of the files indicate the load balancer and size of the problem.<br> For instance, `PackStealLB.240` means that the application was run with PackStealLB and the problem size parameter is 240.</p> <p><strong>Problem sizes</strong>:</p> <ul> <li>80: 80×11×5 cells of dimensions 15×15×30</li> <li>120: 120×11×5 cells of dimensions 15×15×30</li> <li>160: 160×11×5 cells of dimensions 15×15×30</li> <li>240: 240×11×5 cells of dimensions 15×15×30</li> <li>320: 320×11×5 cells of dimensions 15×15×30</li> </ul> <p>Each execution of LeanMD ran for 301 iterations with load balancing calls at iterations 40, 140, and 240.</p> <p><strong>Raw output files</strong>:</p> <p>Each raw output file starts with a Charm++ header providing information on the execution.</p> <p>For each step of the application, its execution time in ms is provided.</p> <p>Load balancing calls usually provide information about their start time, end time, and duration. Depending on the load balancer, more information is provided.</p> <p>Each raw file contains ten executions of the application with a given load balancer and input size.</p> <p><strong>Generating plots</strong>:</p> <p>The analysis of the results can be done by running the Jupyter notebook named "Analysis of load balancing results.ipynb"</p>
Dataset: The SPIKE II experiment - Tracing the water balance
<p>This repository holds data collected during the “SPIKE II” tracer experiment. The experiment was carried out on a large vegetated lysimeter (2.5 m<sup>3</sup>) planted with two willow trees (clones) (<em>Salix viminalis</em>) within the EPFL campus (CH), in Switzerland. SPIKE II took place from May 10 to June 29 in 2018. This composite dataset contain stable isotopic composition (δ<sup>2</sup>H and δ<sup>18</sup>O) of more than 900 water samples of precipitation, soil water, bulk soil collected at different depths in the soil profile, xylem from willow, and leakage flow in the bottom of the lysimeter. The dataset comprises environmental conditions and water fluxes recorded during the experiment. This includes: meteorological conditions, soil moisture and tension, evapotranspiration in the lysimeters, and tree transpiration recorded at high resolution. Finally, the repository holds tree hydraulic and growth measurements and root traits.</p> <p>Specifically, this dataset contains six files:</p> <ul> <li>“METADATA_spikeII.txt” contains specific information about each recorded variable and data point collected throughout the experiment.</li> <li>“spike.hydrometric.II.csv” contains information about meteorological and soil conditions, evapotranspiration fluxes, and tree stem radius, including growth and tree water deficit.</li> <li>"spike.isotopes.II.csv” contains stable isotope data.</li> <li>“fineroots_spike.II.csv” contains root traits information.</li> <li>“events_chronology.csv” summarizes the main events that occurred during SPIKE II.</li> <li>“Figure1_SpikeII_Aerial_Image.PNG” illustrates the location and spatial display of the experiment at the EPFL campus.</li> </ul> <p>This data repository was used in the following SPIKE II publications:</p> <p>Nehemy, M. F., Benettin, P., Asadollahi, M., Pratt, D., Rinaldo, A., & McDonnell, J. J. (2021). Tree water deficit and dynamic source water partitioning. <em>Hydrological Processes</em>, <em>35</em>(1), e14004. doi:10.1002/hyp.14004</p> <p>Benettin, P., Nehemy, M. F., Cernusak, L. A., Kahmen, A., & McDonnell, J. J. (2021). On the use of leaf water to determine plant water source: A proof of concept. <em>Hydrological Processes</em>, <em>35</em>(3), e14073. doi:10.1002/hyp.14073</p> <p>Benettin, P., Nehemy, M. F., Asadollahi, M., Pratt, D., Bensimon, M., McDonnell, J. J., & Rinaldo, A. (2021). Tracing and closing the water balance in a vegetated lysimeter. <em>Water Resources Research</em>, 57, e2020WR029049. doi:org/10.1029/2020WR029049</p> <p>For any further inquiry, please contact Magali Nehemy or Paolo Benettin.</p> <p>We thank Kim Janzen for assistance with laser and mass spec analysis. We thank the Laboratory of Ecohydrology at EPFL (ECHO/IIE/ENAC/EPFL) for assistance throughout the experiment. We also thank Pierre Queloz and Scott Allen for precious help, Gabriel Cotte and Torsten Vennemann from University of Lausanne (CH) for the collection and analysis of atmospheric vapor samples. This research was supported by the American Geophysical – Horton Research Grant 2019 awarded to MFN, an NSERC CREATE in Water Security and an NSERC Discovery Grant to JJM, AR and PB thank ENAC school at EPFL for financial support and acknowledge the Swiss National Science Foundation grant number CRSII5\_186422.</p> <p> </p>
Balancing risks of injury and disturbance to marine mammals when pile driving at offshore windfarms
<p>1. Offshore windfarms require construction procedures that minimise impacts on protected marine mammals. Uncertainty over the efficacy of existing guidelines for mitigating near-field injury when pile-driving recently resulted in the development of alternative measures, which integrated the routine deployment of acoustic deterrent devices (ADD) into engineering installation procedures without prior monitoring by Marine Mammal Observers.</p> <p>2. We conducted research around the installation of jacket foundations at the UK's first deep-water offshore windfarm to address data gaps identified by regulators when consenting this new approach. Specifically, we aimed to a) measure the relationship between noise levels and hammer energy to inform assessments of near-field injury zones, b) assess the efficacy of ADDs to disperse harbour porpoises from these zones.</p> <p>3. Distance from source had the biggest influence on received noise levels but, unexpectedly, received levels at any given distance were highest at low hammer energies. Modelling highlighted that this was because noise from pin pile installations was dominated by the strong negative relationship with pile penetration depth with only a weak positive relationship with hammer energy.</p> <p>4. Acoustic detections of porpoises along a gradient of ADD exposure decreased in the 3-hours following a 15-minute ADD playback, with a 50% probability of response within 21.7 km. The minimum time to the first porpoise detection after playbacks was > 2 hours for sites within 1 km of the playback.</p> <p>5. Our data suggest that the current regulatory focus on maximum hammer energies needs review, and future assessments of noise exposure should also consider foundation type. Despite higher piling noise levels than predicted, responses to ADD playback suggest mitigation was sufficiently conservative. Conversely, strong responses of porpoises to ADDs resulted in far-field disturbance beyond that required to mitigate injury. We recommend that risks to marine mammals can be further minimised by: 1) optimising ADD source signals and/or deployment schedules to minimise broad-scale disturbance; 2) minimising initial hammer energies when received noise levels were highest; 3) extending the initial phase of soft start with minimum hammer energies and low blow rates.Minhyuk Seo</p>
Societies in balance: Monumentality and feasting activities among southern Naga communities, Northeast India (Data repository)
<p>The files provide supplementary information for the paper "Societies in balance: Monumentality and feasting activities among southern Naga communities, Northeast India".</p> <p>In accordance with the content and research questions of the article, this repository includes information on the megalithic monuments, as well as transcripts of the interviews conducted in the village of Rünguzu (Nagaland, India). Therefore, both the quantitative, and the qualitative results presented in the article could be reconstructed and reproduced on the basis of this repository.</p> <p>Information concerning the megalithic monuments of all the villages included in the analyses of the article are given in .csv format. The files include details of the monument type, the orientation of the monuments, the metric measures of the monuments, as well as the social affiliation of the monument builders (if available). These data are the basis for the comparative analyses of the megalithic monuments of the different villages, as well as the detailed analyses of the village Rünguzu. All box plots and bar charts presented in the article are completely based on the data made available here.</p> <p>Secondly, this repository includes transcripts of the interviews which were conducted in the village of Rünguzu. The qualitative descriptions of the village structure itself, the feasting activities, as well as the details of megalithic building activities are based on these interviews. Additionally, social anthropological literature and studies were vital as additional sources of information. The transcripts are, apart from the interviewers, completely anonymised in accordance to ethical standards in the publication of interviews.</p>
Downscaled surface mass balance in Antarctica: impacts of subsurface processes and large-scale atmospheric circulation
<p>Here is the surface mass balance calculated from a offline subsurface model, that is used in the paper Downscaled surface mass balance in Antarctica: impacts of subsurface processes and large-scale atmospheric circulation.<br> More data are available by contacting nichsen@space.dtu.dk</p>
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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
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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
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