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Fig. 4. The potential distribution map for B. bombina under projected 2050 in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 4. The potential distribution map for B. bombina under projected 2050 climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
HYDRO-CSI, Project 1.2: In-stream hydrology. Part 2: instantaneous injections
<p>The continuous exchange of water between surface water and groundwater is a key environmental process controlling the transport and the fate of nutrients, solutes and pollutants in river networks. The dynamics of the near-stream groundwater has a non-negligeable role on controlling flow direction and solutes exchange between the stream water with the adjacent groundwater, however it is rarely considered in solute transport experiments. Despite the amount of individual studies, we are still uncertain about how the physical processes controlling in-stream solutes transport change with different hydrologic conditions and how these processes can be inferred by modelling outcomes.</p> <p>In this project we investigated solute transport in a headwater stream reach via instantaneous (slug) solute injections. The study site is a 55 m long corridor downstream of the Weierbach experimental catchment (see <a href="https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.14140">Hissler et al., 2021)</a>. The stream channel is unvegetated and consists of deposited colluvial material and fragmented schists (up to 50 cm depth) with underlying fractured slate bedrock that sporadically forms the streambed. The average channel slope is 6% and a 50 cm step riffle sits between wells 7W1 and 7W2 (see <a href="https://onlinelibrary.wiley.com/doi/10.1002/hyp.14310">Bonanno et al., 2021</a>). The stream reach is divided in 11 sections that define the nomenclature of the groundwater observation well network (eg. section one is indicated by wells 1W1 and 1W2). The complete list of groundwater measurements has been published in a previous Zenodo dataset and can be found <a href="https://zenodo.org/record/6245818#.YlacmehBxD9">HERE</a>.</p> <p>The tracer chosen for the experiments is chloride. For each experiment, we prepared an NaCl solution using 2 liters of stream water and a fixed mass of reagent-grade NaCl. We injected the solution in a turbulent pool at the beginning of the stream reach (right before section 1) to assure complete mixing in the stream water. Electrical conductivity was measured via portable conductivity meter (Multisonde WTW). Conversion between EC and chloride concentration has been deduced via EC-chloride concentration plots in laboratory where a fixed amount of NaCl solution with known concentration has been progressively added to a sample the stream water collected before the experiment. Every regression equation between EC and chloride concentration plot had a R<sup>2</sup>>0.998.</p> <p>The dataset includes 30 files of chronologically-numbered instantaneous injections. The instantaneous injections have been conducted from 6-Dec-2018 to 11-June-2021. Every file includes:</p> <p>> A map of the investigated stream reach;<br> > WTW sensor location along the stream reach and their distance from injection point;<br> > The amount of NaCl mass injected in the stream;<br> > Pictures of the stream channel and streamflow;<br> > Notes about presence of leaf packs;<br> > Time and net chloride concentration [mg/l] for each sensor. </p> <p>All the experiments, data cleaning, sensor calibration, and conversion from EC to Cl- concentration have been conducted by Bonanno Enrico between 2018 and 2021 as part of the Ph.D. project HYDRO-CSI (PRIDE15/10623093).<br> François Barnic, Laurent Gourdol, Jean François Iffly and Jérôme Juilleret calibrated the Multisonde WTW and provided the necessary training.<br> Laurent Pfister and Julian Klaus managed the project and were responsible for the founding acquisition.</p>
Studying the Practices of Deploying Machine Learning Projects on Docker
<p>This repository contains the dataset for our study titled above:</p> <p>Below is the abstract:</p> <p>Docker is a containerization service that allows for convenient deployment of websites, databases, applications' APIs, and machine learning (ML) models with a few lines of code. Studies have recently explored the use of Docker for deploying general software projects with no specific focus on how Docker is being used to deploy ML-based projects. In this study, we conducted an exploratory study to understand how Docker is being used to deploy ML-based projects. As the initial step, we examined the categories of ML-based project that use Docker. We then examined why and how these projects use Docker, and the characteristics of the resulting Docker images. Our results indicate that six categories of ML-based projects use Docker for deployment, including ML Applications, MLOps/ AIOps, Tookits, DL Frameworks, Models, and Documentation. We derived the taxonomy of 21 major categories representing the purposes of using Docker, including those specific to models such as model management tasks (e.g., testing, training), data management (e.g., migration, persistent storage, sharing), software testing, interactive development. We then showed that ML engineers use Docker images mostly to help with the platform portability, such as transferring the software across the operating systems, runtimes such as GPU-accelerated, and language constraints. However, we also found that more resources may be required to run the Docker images for building ML-based software projects due to the large number of files contained in the image layers with deeply nested directories. Through this study we hope to shed light on the emerging practices of deploying ML software projects using containers and highlight aspects that should be improved.</p>
Descriptive catalogue of the Texts Surrounding Texts Project
<p>The descriptive catalogue can be browsed at <a href="https://tst-project.github.io/mss">https://tst-project.github.io/mss</a></p>
Supplementary Data: Mapping of local lattice parameter ratios by projective Kikuchi pattern matching
<p>This is the experimental dataset which was analyzed in:</p> <p>"Mapping of local lattice parameter ratios by projective Kikuchi pattern matching"<br> Aimo Winkelmann, Gert Nolze, Grzegorz Cios, and Tomasz Tokarski<br> Phys. Rev. Materials <strong>2</strong> (2018) 123803<br> https://doi.org/10.1103/PhysRevMaterials.2.123803</p> <p>We describe a lattice-based crystallographic approximation for the analysis of distorted crystal structures via electron backscatter diffraction (EBSD) in the scanning electron microscope. EBSD patterns are closely linked to local lattice parameter ratios via Kikuchi bands that indicate geometrical lattice plane projections. Based on the transformation properties of points and lines in the real projective plane, we can obtain continuous estimations of the local lattice distortion based on projectively transformed Kikuchi diffraction simulations for a reference structure. By quantitative image matching to a projective transformation model of the lattice distortion in the full solid angle of possible scattering directions, we enforce a crystallographically consistent approximation in the fitting procedure of distorted simulations to the experimentally observed diffraction patterns. As an application example, we map the locally varying tetragonality in martensite grains of steel.</p>
List of wind power projects in Vietnam
<p>Historical inventory of wind power projects in Vietnam updated 2022-05-04 "post FIT race"</p> <p>This release is complete for projects with COD or PPA at the end of the FIT period.</p> <p>The number of records is 548, of which 360 refer to active projects phases, and 215 have investment costs.</p> <p>Each record describes a wind project phase. The table includes projects at all stages of the lifecycle. Some have been mentioned years ago and are now of historical interest only. Some have just a signed exploration MoU. All records are referenced to one or several public sources.</p> <p>Fields: Record status, Project stage, Project name, Owner, Location (hamlet, commune, province, region, longitude, latitude), Location type (onshore/nearshore/offshore), Capacity (MWp), Connection plan, Turbines, Investment (VND or USD),</p> <p>dates (MoU, Investment Licence,, Groundbreaking), Note and sources.</p> <p>The dataset also include:</p> <ul> <li>Descriptive data paper.</li> <li>A CSV export of the table.</li> <li>Python module to read the data from the CSV and python script to print the descriptive statistics.</li> </ul>
All-India district-scale climate projections for current (2006-2015), mid-century (2041-50), and end-century (2091-2100) periods
<ol> <li>This dataset contains monthly mean data for 10 meteorological variables for each district of India for current, mid-century, and end-century periods for all the districts of India. </li> <li>To generate this data, we first dynamically downscaled the CMIP5 CESM RCP8.5 projections over India for the Current (2006-2015), Mid-Century (2041-50), and End-Century (2091-2100) periods using the Weather Research and Forecasting (WRF) model to 10 km resolution. The 30 years of data are archived in the World Data Center for Climate (WDCC) at DKRZ (Barik et al. 2021 and 2022). Next, we processed the 10-km gridded downscaled data to calculate the monthly climatological mean by averaging over 10 years for each of the 3 periods. Finally, the monthly climatological means were processed in ArcGIS to develop the district scale datasets.</li> </ol>
UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)
<p>Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.</p>
Video - The PTANOIS POSIN project, digital publication
<p>Video presentation of the PTANOIS POSIN project website: description of the project; presentation and description of the digital product</p>
Citizen science for traffic counts: WeCount project dataset for inner-city streets of Ljubljana
<p>The Horizon 2020 project WeCount is a citizen science project that involves citizens in all steps from problem definition to data collection and analysis. This is currently one of the most common methods of citizen participation. The ethical criteria that such a project must meet in order to be classified as citizen science, and the form of transparency or informed consent that should be a necessary part of the ethical conduct of citizen science projects, were on Telraam platform for examination at the international and national level during the collection of data on traffic flows for WeCount Ljubljana. Engaged citizens were given low-cost sensors which they placed on the inside of the windowpane in their home or office facing the street at different distances (from 3 to 15 meters). </p>
Extreme sea level rise along the Indian Ocean coastline: Observations and 21st century projections
<p>This file include all the data sets used to make the figures in the paper " <strong><em>Extreme sea level rise along the Indian Ocean coastline: Observations and 21<sup>st</sup> century projections</em></strong> "</p>
CRISI-ADAPT II: free downscaled climate projection layers
<p>CRISI-ADAPT II project had as one of its main purposes to develop coherent, reliable and usable downscaled climate projections from the last CMIP6 in order to construct the basis for efficient support to climate adaptation and decision making of the related stakeholders. These projections were obtained with also the purpose to be freely available for further use in subsequent studies and, hence, foster adaptation to climate change in more areas.</p> <p>For further details, find here a brief of the methodology followed:</p> <p> </p> <p><strong> Methodology</strong></p> <p>Information provided by 10 models belonging to CMIP6 have been included. Each model has a historical archive, from 01/01/1950 to 31/12/2014 and 4 future scenarios (ssp126, ssp245, ssp370 and ssp585) ranging from 01/01/2015 to 31/12/2100. The relation of the selected models is detailed in the next Table: </p> <p><em>Table. Information about the ten climate models belonging to the 6 Coupled Model Intercomparison Project (CMIP6) corresponding to the sixth report of the IPCC. Models were supplied by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) archives. </em></p> <table> <tbody> <tr> <td> <p><strong>CMPI6 MODELS</strong> </p> </td> <td> <p><strong>Resolution</strong> </p> </td> <td> <p><strong>Responsible Centre</strong> </p> </td> <td> <p><strong>References</strong> </p> </td> </tr> <tr> <td> <p><strong>BCC-CSM2-MR</strong> </p> </td> <td> <p>1,125º x 1,121º </p> </td> <td> <p>Beijing Climate Center (BCC), China Meteorological Administration, China. </p> </td> <td> <p>Wu, T. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>CanESM5</strong> </p> </td> <td> <p>2,812º x 2,790º </p> </td> <td> <p>Canadian Centre for Climate Modeling and Analysis (CC-CMA), Canadá. </p> </td> <td> <p>Swart, N.C. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>CNRM-ESM2-1</strong> </p> </td> <td> <p>1,406º x 1,401º </p> </td> <td> <p>CNRM (Centre National de Recherches Meteorologiques), Meteo-France, Francia. </p> </td> <td> <p>Seferian, R. (2019) </p> </td> </tr> <tr> <td> <p><strong>EC-EARTH3</strong> </p> </td> <td> <p>0,703º x 0,702º </p> </td> <td> <p>EC-EARTH Consortium </p> </td> <td> <p>EC-Earth Consortium. (2019) </p> </td> </tr> <tr> <td> <p><strong>GFDL-ESM4</strong> </p> </td> <td> <p>1,250º x 1,000º </p> </td> <td> <p>National Oceanic and Atmospheric Administration (NOAA), E.E.U.U. </p> </td> <td> <p>Krasting, J.P. et al. (2018) </p> </td> </tr> <tr> <td> <p><strong>MPI-ESM1-2-HR</strong> </p> </td> <td> <p>0,938º x 0,935º </p> </td> <td> <p>Max-Planck Institute for Meteorology (MPI-M), Germany. </p> </td> <td> <p>Von Storch, J. et al. (2017) </p> </td> </tr> <tr> <td> <p><strong>MRI-ESM2-0</strong> </p> </td> <td> <p>1,125º x 1,121º </p> </td> <td> <p>Meteorological Research Institute (MRI), Japan. </p> </td> <td> <p>Yukimoto, S. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>UKESM1-0-LL</strong> </p> </td> <td> <p>1,875º x 1,250º </p> </td> <td> <p>Uk Met Office, Hadley Centre, United Kingdom </p> </td> <td> <p>Good, P. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>NorESM2-MM</strong> </p> </td> <td> <p>1,250º x 0,942º </p> </td> <td> <p>Norwegian Climate Centre (NCC), Norway. </p> </td> <td> <p>Bentsen, M. et al. (2019) </p> </td> </tr> <tr> <td> <p><strong>ACCESS-ESM1-5</strong> </p> </td> <td> <p>1,875º x 1,250º </p> </td> <td> <p>Australian Community Climate and Earth System Simulator (ACCESS), Australia </p> </td> <td> <p>Ziehn, T. et al. (2019)</p> </td> </tr> </tbody> </table> <p>Since the case studies are distributed among Portugal, Spain, Italy, Malta and Cyprus, a grid covering the whole Mediterranean area, between latitudes 30°N and 50°N and longitudes between 15°W and 40°E, has been chosen for the study. The atmospheric variables available from CMIP6 are wind, temperature, humidity and rainfall at a daily timescale and sea level rise at a monthly timescale. However, it is possible simulate sub-daily rainfall (e.g. for the sector of Flooding and Emergency Response) thanks to the index-n method (Monjo <em>et al.</em> 2016). Other variables such as fog and wave height requires to be obtained from model post-processing. </p> <p>In addition to these models, information has also been combined to the ERA5-LAND, which has a resolution of 0.07°×0.07°. For each climate variable simulated by the CMIP6 models, a statistical downscaling was applied according to seven steps: </p> <ol> <li> <p>Firstly, as a reference field, a purely geo-statistical downscaling of the original Era5-Land grid (0.07°×0.07°) was performed for each variable to a 1km×1km grid, using linear stepwise regression with topological and geographical parameters (altitude, latitude, longitude and distance to the Atlantic Ocean and Mediterranean Sea), and bilinear model for the residual errors. </p> </li> <li>For all models and their corresponding scenarios, the average values for the study area have been calculated for the periods 1981-2010, 2021-2050 and 2071-2100 and their rate of variation between the periods 2071-2100 and 2021-2050. </li> <li> <p>The model scenario with the highest rate of variation and the model scenario with the lowest rate of variation have been chosen to range future variations of the variables. Quantiles 90th, 50th and 10th scenarios have been called Upper, Medium and Lower, respectively. </p> </li> <li>For these scenarios, Upper, Medium and Lower, the empirical values corresponding to the return periods of 5, 10, 20 and 30 years for the periods 1981-2010, 2021-2050, 2046-2075 and 2071-2100 have been calculated for each grid point in the model. </li> <li> <p>Once the above results were obtained, an interpolation to a grid of 1km×1km was performed using the bilinear method. </p> </li> <li>Then, the increment or difference with respect to the same return periods of the period 1981-2010 has been calculated for each period of 30 years (2021-2050, 2046-2075 and 2071-2100) and for each return period. Relative increment (instead of absolute increment) was considered for some variable such as precipitation and wind. </li> <li> <p>Finally, the absolute o relative increment of each scenario and return period (step 6) was added to the reference values of each variable (step 1), obtaining climate scenarios in a 1km×1km grid (see for instance Figure 8). This entire process, applied to return-period values, is an empirical quantile mapping by increment from reanalysis (Monjo et al. 2013). </p> </li> </ol>
500-yr Projections of Thwaites Glacier, Antarctica, with MALI, including glacial isostatic adjustment
<p>This archive contains model code, results, and analysis scripts for<br> reproducing the material presented in the manuscript "Stabilizing effect of<br> bedrock uplift on retreat of Thwaites Glacier, Antarctica, at centennial<br> timescales" by Cameron Book, et al. Questions should be directed to Matt<br> Hoffman (mhoffman@lanl.gov).</p> <p>This archive contains the following directories:</p> <p>|-- MALI_code<br> |-- PIGL_control<br> |-- analysis<br> |-- control<br> |-- run_setup<br> |-- N1<br> |-- N2<br> |-- N3<br> |-- N4<br> `-- PIGL_N3</p> <p><br> 'MALI_code' is a snapshot of the MALI repository used for these simulations,<br> commit 454e0fc8bf384bee1c1560d6c3eaa5fae43bfdda.<br> This commit is present in an older MALI repository that is no longer<br> maintained, at https://github.com/MPAS-Dev/MPAS-Model<br> MALI is currently maintained on Github at https://github.com/MALI-Dev/E3SM<br> Building MALI requires the Albany multiphysics library, which is available<br> at https://github.com/sandialabs/Albany. The simulations presented use Albany<br> master from March 5, 2021.</p> <p>'analysis' contains the scripts used to process the model output and produce<br> the figures and results presented in the manuscript. Filepaths will have to<br> be adjusted to your local layout.</p> <p>'run_setup' is a directory of files and scripts necessary to reproduce the<br> model simulations presented. The GIA model giapy is in the file<br> 'giascript.py'. giapy can also be found on Github at https://github.com/skachuck/giapy</p> <p>'control' is the control run with the GIA model disabled. It corresponds to<br> the run labeled CTRL in the manuscript.</p> <p>'PIGL_control' is the control run using the high melt forcing. It corresponds<br> to the run labeled HM-CTRL in the manuscript.</p> <p>The five run directories included here (N1-N4, PIGL_N3) are the standard<br> ensemble described in the manuscript. (There is a separate archive for the<br> runs briefly mentioned that exclude the elastic response of the lithosphere.)<br> The individual runs have the following correspondence to the manuscript:<br> N1=TYP<br> N2=BEST2<br> N3=VLV-THIN<br> N4=VLV<br> PIGL_N3=HM-VLV-THIN<br> Within each run directory, are the following model output files:<br> globalStats.nc: MALI global, scalar time-series<br> iceload_all.nc: Thwaites Glacier ice load on the GIA grid<br> output_*.nc: MALI spatial output fields, separated by century<br> uplift_GIA_all.nc: GIA output on GIA grid</p>
Dataset: SentiSurvey for Sentiment Analysis in Software Projects
<p><strong>Description</strong></p> <p>In 2022, we conducted a survey about the perceptions of developers regarding sentiments in statements. We published a paper about the results. The dataset includes the survey questions and the answers of the total 180 participants.</p> <p><strong>Citation</strong></p> <p>Information on the study design and execution are presented in the paper linked below.</p> <p>Please, see also the references below for the papers to cite.</p>
Monthly averaged lightning and LCC lightning data extracted from present-day (2009-2011) and projected (2090-2095) EMAC simulations (T42L90MA resolution)
<pre>About Dataset Monthly averaged lightning and LCC lightning data extracted from present-day (2009-2011) and projected (2090-2095) EMAC simulations (T42L90MA resolution). Authors: Francisco J. Perez-Invernon, Francisco J. Gordillo-Vazquez, Patrick Joeckel and Heidi Huntrieser Description of the data PaR_T: Lightning parameterization based on cloud top height. PaR_L: Lightning parameterization based on cloud top height and modified over the oceans. Grewe: Lightning parameterization based on updraft velocity. AaP_P: Lightning parameterization based on convective precipitation. A AaP_M: Lightning parameterization based on Updraft strength at 440~hPa. PRaAP: Lightning parameterization based on cloud top height and updraft velocity. FinIF: Lightning parameterization based on updraft mass flux of ice at 440 hPa. extIF: Lightning parameterization based on updraft mass flux of ice at 440 hPa an isotherm. File format: netcdf Example: netcdf </pre> <p>2009_10h_______20090701_0000_mmlb_PRaAP.nc<br> netcdf \2009_10h_______20090701_0000_mmlb_PRaAP {<br> dimensions:<br> time = UNLIMITED ; // (1 currently)<br> lon = 128 ;<br> lat = 64 ;<br> tbnds = 2 ;<br> variables:<br> double time(time) ;<br> time:long_name = "time" ;<br> time:bounds = "time_bnds" ;<br> time:units = "day since 2009-01-01 00:00:00" ;<br> time:calendar = "gregorian" ;<br> double YYYYMMDD(time) ;<br> YYYYMMDD:long_name = "time" ;<br> YYYYMMDD:units = "days as %Y%m%d.%f" ;<br> YYYYMMDD:calendar = "gregorian" ;<br> double dt(time) ;<br> dt:long_name = "delta_time" ;<br> dt:units = "s" ;<br> double nstep(time) ;<br> nstep:long_name = "current time step" ;<br> float lon(lon) ;<br> lon:long_name = "longitude" ;<br> lon:units = "degrees_east" ;<br> float lat(lat) ;<br> lat:long_name = "latitude" ;<br> lat:units = "degrees_north" ;<br> float aps(time, lat, lon) ;<br> aps:long_name = "surface pressure" ;<br> aps:units = "Pa" ;<br> aps:representation = "GP_2D_HORIZONTAL" ;<br> aps:grid_type = "gaussian" ;<br> aps:table = 128 ;<br> aps:code = 134 ;<br> aps:REFERENCE_TO = "g3b: aps" ;<br> aps:coordinates = "lon lat" ;<br> aps:cell_methods = "time: point" ;<br> float aps_ave(time, lat, lon) ;<br> aps_ave:long_name = "surface pressure" ;<br> aps_ave:units = "Pa" ;<br> aps_ave:representation = "GP_2D_HORIZONTAL" ;<br> aps_ave:grid_type = "gaussian" ;<br> aps_ave:table = 128 ;<br> aps_ave:code = 134 ;<br> aps_ave:REFERENCE_TO = "g3b: aps" ;<br> aps_ave:coordinates = "lon lat" ;<br> aps_ave:cell_methods = "time: mean" ;<br> float fpscg(time, lat, lon) ;<br> fpscg:long_name = "CG flash frequency" ;<br> fpscg:units = "1/s" ;<br> fpscg:REFERENCE_TO = "lnox_PRaAP_gp: fpscg" ;<br> fpscg:coordinates = "lon lat" ;<br> fpscg:cell_methods = "time: point" ;<br> float fpscg_ave(time, lat, lon) ;<br> fpscg_ave:long_name = "CG flash frequency" ;<br> fpscg_ave:units = "1/s" ;<br> fpscg_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpscg" ;<br> fpscg_ave:coordinates = "lon lat" ;<br> fpscg_ave:cell_methods = "time: mean" ;<br> float fpsic(time, lat, lon) ;<br> fpsic:long_name = "IC flash frequency" ;<br> fpsic:units = "1/s" ;<br> fpsic:REFERENCE_TO = "lnox_PRaAP_gp: fpsic" ;<br> fpsic:coordinates = "lon lat" ;<br> fpsic:cell_methods = "time: point" ;<br> float fpsic_ave(time, lat, lon) ;<br> fpsic_ave:long_name = "IC flash frequency" ;<br> fpsic_ave:units = "1/s" ;<br> fpsic_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsic" ;<br> fpsic_ave:coordinates = "lon lat" ;<br> fpsic_ave:cell_methods = "time: mean" ;<br> float fpsm2cg(time, lat, lon) ;<br> fpsm2cg:long_name = "CG flash density" ;<br> fpsm2cg:units = "1/s/m2" ;<br> fpsm2cg:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2cg" ;<br> fpsm2cg:coordinates = "lon lat" ;<br> fpsm2cg:cell_methods = "time: point" ;<br> float fpsm2cg_ave(time, lat, lon) ;<br> fpsm2cg_ave:long_name = "CG flash density" ;<br> fpsm2cg_ave:units = "1/s/m2" ;<br> fpsm2cg_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2cg" ;<br> fpsm2cg_ave:coordinates = "lon lat" ;<br> fpsm2cg_ave:cell_methods = "time: mean" ;<br> float fpsm2ic(time, lat, lon) ;<br> fpsm2ic:long_name = "IC flash density" ;<br> fpsm2ic:units = "1/s/m2" ;<br> fpsm2ic:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2ic" ;<br> fpsm2ic:coordinates = "lon lat" ;<br> fpsm2ic:cell_methods = "time: point" ;<br> float fpsm2ic_ave(time, lat, lon) ;<br> fpsm2ic_ave:long_name = "IC flash density" ;<br> fpsm2ic_ave:units = "1/s/m2" ;<br> fpsm2ic_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2ic" ;<br> fpsm2ic_ave:coordinates = "lon lat" ;<br> fpsm2ic_ave:cell_methods = "time: mean" ;<br> float fpslcc10(time, lat, lon) ;<br> fpslcc10:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10:units = "1/s" ;<br> fpslcc10:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc10" ;<br> fpslcc10:coordinates = "lon lat" ;<br> fpslcc10:cell_methods = "time: point" ;<br> float fpslcc10_ave(time, lat, lon) ;<br> fpslcc10_ave:long_name = "LCC(>10 ms) flash frequency" ;<br> fpslcc10_ave:units = "1/s" ;<br> fpslcc10_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc10" ;<br> fpslcc10_ave:coordinates = "lon lat" ;<br> fpslcc10_ave:cell_methods = "time: mean" ;<br> float fpslcc20(time, lat, lon) ;<br> fpslcc20:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20:units = "1/s" ;<br> fpslcc20:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc20" ;<br> fpslcc20:coordinates = "lon lat" ;<br> fpslcc20:cell_methods = "time: point" ;<br> float fpslcc20_ave(time, lat, lon) ;<br> fpslcc20_ave:long_name = "LCC(>20 ms) flash frequency" ;<br> fpslcc20_ave:units = "1/s" ;<br> fpslcc20_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpslcc20" ;<br> fpslcc20_ave:coordinates = "lon lat" ;<br> fpslcc20_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc10(time, lat, lon) ;<br> fpsm2lcc10:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10:units = "1/s/m2" ;<br> fpsm2lcc10:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc10" ;<br> fpsm2lcc10:coordinates = "lon lat" ;<br> fpsm2lcc10:cell_methods = "time: point" ;<br> float fpsm2lcc10_ave(time, lat, lon) ;<br> fpsm2lcc10_ave:long_name = "LCC(>10 ms) flash density" ;<br> fpsm2lcc10_ave:units = "1/s/m2" ;<br> fpsm2lcc10_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc10" ;<br> fpsm2lcc10_ave:coordinates = "lon lat" ;<br> fpsm2lcc10_ave:cell_methods = "time: mean" ;<br> float fpsm2lcc20(time, lat, lon) ;<br> fpsm2lcc20:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20:units = "1/s/m2" ;<br> fpsm2lcc20:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc20" ;<br> fpsm2lcc20:coordinates = "lon lat" ;<br> fpsm2lcc20:cell_methods = "time: point" ;<br> float fpsm2lcc20_ave(time, lat, lon) ;<br> fpsm2lcc20_ave:long_name = "LCC(>20 ms) flash density" ;<br> fpsm2lcc20_ave:units = "1/s/m2" ;<br> fpsm2lcc20_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2lcc20" ;<br> fpsm2lcc20_ave:coordinates = "lon lat" ;<br> fpsm2lcc20_ave:cell_methods = "time: mean" ;<br> float fpssprite(time, lat, lon) ;<br> fpssprite:long_name = "Sprites flash frequency" ;<br> fpssprite:units = "1/s" ;<br> fpssprite:REFERENCE_TO = "lnox_PRaAP_gp: fpssprite" ;<br> fpssprite:coordinates = "lon lat" ;<br> fpssprite:cell_methods = "time: point" ;<br> float fpssprite_ave(time, lat, lon) ;<br> fpssprite_ave:long_name = "Sprites flash frequency" ;<br> fpssprite_ave:units = "1/s" ;<br> fpssprite_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpssprite" ;<br> fpssprite_ave:coordinates = "lon lat" ;<br> fpssprite_ave:cell_methods = "time: mean" ;<br> float fpsm2sprite(time, lat, lon) ;<br> fpsm2sprite:long_name = "Sprites flash density" ;<br> fpsm2sprite:units = "1/s/m2" ;<br> fpsm2sprite:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2sprite" ;<br> fpsm2sprite:coordinates = "lon lat" ;<br> fpsm2sprite:cell_methods = "time: point" ;<br> float fpsm2sprite_ave(time, lat, lon) ;<br> fpsm2sprite_ave:long_name = "Sprites flash density" ;<br> fpsm2sprite_ave:units = "1/s/m2" ;<br> fpsm2sprite_ave:REFERENCE_TO = "lnox_PRaAP_gp: fpsm2sprite" ;<br> fpsm2sprite_ave:coordinates = "lon lat" ;<br> fpsm2sprite_ave:cell_methods = "time: mean" ;<br> float bps(time, lat, lon) ;<br> bps:long_name = "BJ flash frequency" ;<br> bps:units = "1/s" ;<br> bps:REFERENCE_TO = "bluejetbPRaAP_gp: bps" ;<br> bps:coordinates = "lon lat" ;<br> bps:cell_methods = "time: point" ;<br> float bps_ave(time, lat, lon) ;<br> bps_ave:long_name = "BJ flash frequency" ;<br> bps_ave:units = "1/s" ;<br> bps_ave:REFERENCE_TO = "bluejetbPRaAP_gp: bps" ;<br> bps_ave:coordinates = "lon lat" ;<br> bps_ave:cell_methods = "time: mean" ;<br> float bpsm2(time, lat, lon) ;<br> bpsm2:long_name = "BJ flash density" ;<br> bpsm2:units = "1/s/m2" ;<br> bpsm2:REFERENCE_TO = "bluejetbPRaAP_gp: bpsm2" ;<br> bpsm2:coordinates = "lon lat" ;<br> bpsm2:cell_methods = "time: point" ;<br> float bpsm2_ave(time, lat, lon) ;<br> bpsm2_ave:long_name = "BJ flash density" ;<br> bpsm2_ave:units = "1/s/m2" ;<br> bpsm2_ave:REFERENCE_TO = "bluejetbPRaAP_gp: bpsm2" ;<br> bpsm2_ave:coordinates = "lon lat" ;<br> bpsm2_ave:cell_methods = "time: mean" ;<br> double time_bnds(time, tbnds) ;<br> time_bnds:long_name = "time bounds" ;<br> time_bnds:units = "days since 2009-01-01T00:00:00Z" ;<br> time_bnds:cell_methods = "time: point" ;</p> <p>// global attributes:<br> :MESSy = "MESSy version d2.55.1-62-g7ea171fc6-dirty_7ea171fc682744f70976123b863cca166dd2023b_2021-05-19T16:21:08+00:00_2021-06-04T13:28:34+0200, http://www.messy-interface.org" ;<br> :MESSy_switch = "version 1.0" ;<br> :MESSy_channel = "version 2.4.3" ;<br> :MESSy_tracer = "version 2.6" ;<br> :MESSy_timer = "version 0.1" ;<br> :MESSy_qtimer = "version 3.0" ;<br> :MESSy_import = "version 1.0" ;<br> :MESSy_grid = "version v1.5" ;<br> :MESSy_rnd = "version 1.1" ;<br> :MESSy_aeropt = "version 2.0.2" ;<br> :MESSy_cloud = "version 2.2" ;<br> :MESSy_cloudopt = "version 2.1b" ;<br> :MESSy_convect = "version 2.0" ;<br> :MESSy_gwave = "version 1.0" ;<br> :MESSy_lnox = "version 3.0" ;<br> :MESSy_orbit = "version 0.9" ;<br> :MESSy_orogw = "version 1.1" ;<br> :MESSy_rad = "version 2.2" ;<br> :MESSy_e5vdiff = "version 1.2" ;<br> :MESSy_surface = "version 1.2" ;<br> :MESSy_tropop = "version 2.1" ;<br> :MESSy_viso = "version 2.3" ;<br> :MESSy_experiment = "2009_10h" ;<br> :EXEC_CHECKSUM = "45e5ddd17ae5992f931a9ba4bab4a921 bin/echam5.exe (md5sum)" ;<br> :GCM = "ECHAM5 version 5.3.02, Max-Planck Institute for Meteorology, Hamburg" ;<br> :GCM_spherical_trunc_n = 42 ;<br> :GCM_spherical_trunc_m = 42 ;<br> :GCM_spherical_trunc_k = 42 ;<br> :GCM_vertical_mode = "middle atmosphere (MA)" ;<br> :GCM_horizontal_mode = "global" ;<br> :GCM_advection = "Lin&Rood" ;<br> :GCM_start_date_time = "20090101 000000" ;<br> :GCM_timestep = 900.f ;<br> :F95_COMPILER_VERSION = "ifort (IFORT) 17.0.2 20170213" ;<br> :F95_COMPILER_CALL = "/opt/mpi/bullxmpi_mlx/1.2.9.2/bin/mpif90" ;<br> :F95_COMPILER_FLAGS = "-sox -fpp -g -O2 -xCORE-AVX2 -fp-model strict -align all -save-temps -DBULL -I/sw/rhel6-x64/sys/bullxlib-1.0.0/include -L/sw/rhel6-x64/sys/bullxlib-1.0.0/lib -Wl,-rpath,/sw/rhel6-x64/sys/bullxlib-1.0.0/lib -lbullxMATH -no-wrap-margin" ;<br> :F95_PREPROC_DEFINITIONS = "-DMESSY -DLITTLE_ENDIAN -D_LINUX64 -DHAVE_PNETCDF -DPNCREGRID -DMPIOM_13B -D_VCSREV_=\'d2.55.1-62-g7ea171fc6-dirty_7ea171fc682744f70976123b863cca166dd2023b_2021-05-19T16:21:08+00:00_2021-06-04T13:28:34+0200\'" ;<br> :F95_COMPILER_INCLUDES = "-I/sw/rhel6-x64/netcdf/netcdf_fortran-4.4.2-intel14/include -I/sw/rhel6-x64/netcdf/netcdf_fortran-4.4.2-intel14/include -I/sw/rhel6-x64/netcdf/parallel_netcdf-1.6.0-bullxmpi-intel14/include" ;<br> :operating_date_time = "20210615 081833" ;<br> :operating_system = "Linux 2.6.32-754.33.1.el6.x86_64 on x86_64" ;<br> :operating_host = "mlogin102" ;<br> :operating_user = "Francisco-Javier Perez-Invernon (b309171)" ;<br> :channel_io_pe = 172 ;<br> :channel_time_slo = 5182200.f ;<br> :channel_name = "mmlb_PRaAP" ;<br> :channel_file_type = "output" ;<br> :channel_file_name = "2009_10h_______20090701_0000_mmlb_PRaAP.nc" ;<br> :channel_netcdf_lib = "4.3.2 of May 5 2015 13:21:25 $" ;</p>
Integral projection model results of the planktonic foraminifer Trilobatus sacculifer
<p>Developmental plasticity, where traits change state in response to environmental cues, is well-studied in modern populations. It is also suspected to play a role in macroevolutionary dynamics, but due to a lack of long-term records the frequency of plasticity-led evolution in deep time remains unknown. Populations are dynamic entities, yet their representation in the fossil record is a static snapshot of often isolated individuals. Here, we apply for the first time contemporary integral projection models (IPMs) to fossil data to link individual development with expected population variation. IPMs describe the effects of individual growth in discrete steps on long-term population dynamics. We parameterize the models using modern and fossil data of the planktonic foraminifer <em>Trilobatus sacculifer</em>. Foraminifera grow by adding chambers in discrete stages and die at reproduction, making them excellent case studies for IPMs. Our results predict that somatic growth rates have almost twice as much influence on population dynamics than survival and more than eight times more influence than reproduction, suggesting that selection would primarily target somatic growth as the major determinant of fitness. As numerous palaeobiological systems record growth rate increments in single genetic individuals, and imaging technologies are increasingly available, our results open up the possibility of evidence-based inference of developmental plasticity spanning macroevolutionary dynamics. Given the centrality of ecology in palaeobiological thinking, our model is one approach to help bridge eco-evolutionary scales while directing attention towards the most relevant life-history traits to measure.</p>
Bathymetry 2017 - 1m - HBC Project
<p>Digital Bathymetry data set., cell size 1 m×1m.</p>
The Impact of Working From Home on the Success of Scrum Projects: A Multi-Method Study
<p>The number of companies opting for remote working has been increasing over the years, and Agile methodologies, such as Scrum, were adapted to mitigate the challenges caused by the distributed teams. However, the COVID-19 pandemic imposed a fully working from home context, which has never existed before. To investigate this phenomenon, we used a two-phased Multi-Method study. In the first phase, we uncover how working from home impacted Scrum practitioners through semi-structured interviews. Then, in the second phase, we propose a theoretical model that we test and generalize using Partial Least Squares - Structural Equation Modeling (PLS-SEM) through a quantitative survey of 138 software engineers who worked from home within Scrum projects. From assessing our model, we can conclude that all the latent variables are reliable and all the hypotheses are significant. We emphasize the importance of supporting the three innate psychological needs of autonomy, competence, and relatedness in the home working environment. We conclude that the ability of working from<br> home and the use of Scrum both contribute to project success, with Scrum acting<br> as a mediator.</p>
CEDAR Project: A Whole-Atmospheric Perspective on Connections between Intra-Seasonal Variations in the Troposphere and Thermosphere
<p>This collaborative award is aimed at studying the relationship between the variability of thermospheric winds to the variability caused by wave structures generated in the tropical troposphere. This coupling is driven by wave excitation by deep convection in the tropical troposphere that can propagate vertically into the thermosphere. Tropospheric convection associated with the Madden‐Julian Oscillation (MJO), the dominant mode of intra-seasonal variability in tropical convection and circulation, is known to modulate the intensity of upward‐propagating gravity and Kelvin waves. Previous work demonstrated that a 90-day oscillation in tropospheric convection during 2009-2010 was imprinted on both thermospheric mean winds and the eastward propagating wavenumber 3 diurnal (DE3) tidal amplitudes. This modulation was observed by the GOCE and CHAMP satellites and modeled with the TIME-GCM. The research effort would broaden participation by involving and training two undergraduate student interns through the University of Colorado BOLD internship program that focuses on promoting the recruitment, retention, and development of traditionally underrepresented engineering students.<br> <br> The new research will follow up on the results obtained in recent studies that demonstrated that strong coupling between the troposphere and the thermosphere occurs on intra-seasonal timescales. The award will address the following questions:<br> Q1: How frequent, prevalent, and persistent are correlations between 30 to 100-day variations in the three regions of troposphere, mesosphere, and thermosphere, during the past two decades?<br> Q2: What plausible roles do large-scale upward propagating waves play in dynamically coupling tropical tropospheric intra-seasonal variability into the thermosphere?<br> Q3: Is there any observational evidence suggesting a connection between this troposphere-thermosphere intra-seasonal coupling and MJO, Quasi-Biennial Oscillation (QBO) and El Niño-Southern Oscillation (ENSO)?<br> The combination of available upper atmosphere satellite data with ground-, and model-based datasets would be studied to provide insight into whether the intra-seasonal variations in the waves are caused by variability in the tropospheric sources or by wave-mean flow interactions. In the case of the latter, the study would determine at which heights these interactions are occurring. This study will determine the contribution of global-scale wave coupling between the troposphere and the thermosphere, thus addressing outstanding issues of fundamental importance to the CEDAR community.</p> <p>This research primarily involves performing correlation analyses and extracting wave information from satellite (CHAMP, GOCE, Swarm-C, TIMED, OLR), ground (Kauai, Christmas Island, and Adelaide, Maui, Urbana, and Chile), and model (MERRA-2, TIE-GCM, and WACCM-X) -based datasets and processing, plotting, data produced in standard ways to draw scientific conclusions. </p> <p>This project does not generate any new physical or observational data. The Findable, Accessible, Interoperable and Reusable (FAIR) principles are followed by making data resources (e.g. code/software and metadata) resulting from this project publicly available.</p> <p>GOCE, CHAMP, Swarm-C data (V01) are available at ftp://anonymous@thermosphere.tudelft.nl/. SABER data (V2.0, L2B) are available at http://saber.gats-inc.com/data.php. Tl DI data (V3.7) are available at http:// timed.hao.ucar.edu/tidi/. OLR data are available at https://psl.noaa.gov/data/gridded/ data.interp_OLR.html. F10.7 data are available at http://www.swpc.noaa.gov/content/data-access. kp/ap data are available at ftp:// ftp.gfz-potsdam.de/pub/home/obs/ kp-ap/.</p>
In-network data collection and data processing - Supplementary materials for deliverable D5.1 - EU-H2020 FET project 'Watchplant'
<p>Supplementary material for D5.1 - Watchplant. Contains collected dataset from plant experiments with blue and red light stimuli, classification results based on statistical methods, and plots of the recorded electropotentials.</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.