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1,582 results for “manuscript”
Data used in the manuscript - A Hierarchical Approach for Evaluating Athlete Performance with an Application in Elite Basketball
<p>The database contains several datasets and files with NBA statistical data spanning four seasons (2015-2016 to 2018-2019). These datasets were procured from the Basketball Reference database (https://www.basketball-reference.com/), a publicly accessible source of NBA data. </p> <p>The main file, `dat.cleaned.csv`, includes the Win/Loss records for all thirty NBA teams, along with box scores and advanced statistics. The data captured over the four seasons correspond to about 4,920 regular-season games. A distinguishing feature of this dataset is the repeated measurements per player within a team across the seasons. However, it's important to note that these repeated measurements are not independent, necessitating the use of hierarchical modelling to properly handle the data.</p> <p>Two sets of additional text files (`per_2017.txt`, `per_2018.txt`, `rpm_2017.txt`, `rpm_2018.txt`) provide specific metrics for player performance. The 'PER' files contain the Athlete Efficiency Rating (PER) for the years 2017 and 2018. The 'RPM' files contain the ESPN-developed score called Real Plus-Minus (RPM) for the same years.</p> <p>However, potential biases or limitations within the datasets should be acknowledged. For instance, the Basketball Reference website might not include data from some matches or may exclude certain variables, potentially affecting the quality and accuracy of the dataset. </p>
Supplementary Material no. 2 to the manuscript: Reemission of inorganic pollution from permafrost? – a freshwater hydrochemistry study in the lower Kolyma basin (North-East Siberia)
<p>A dataset on the inorganic chemistry of permafrost-related creeks and ice, thermokarst lakes and the Kolyma river and its tributaries in late July 2021.<br> Companion dataset to the manuscript: "Reemission of inorganic pollution from permafrost? – a freshwater hydrochemistry study in the lower Kolyma basin (North-East Siberia)".<br> Current abstract of the manuscript (prior to peer review):</p> <p>Permafrost regions are under particular pressure from climate change resulting in widespread landscape changes, which impact also freshwater chemistry. We investigated a snapshot of hydrochemistry in various freshwater environments in the lower Kolyma river basin (North-East Siberia, continuous permafrost zone) to explore the mobility of metals, metalloids and non-metals resulting from permafrost thaw. Particular attention was focused on heavy metals as contaminants potentially released from the secondary source in the permafrozen Yedoma complex. Permafrost creeks represented the Mg-Ca-Na-HCO<sub>3</sub>-Cl-SO<sub>4</sub> ionic water type (with mineralisation in the range 600-800 mg/L), while permafrost ice and thermokarst lake waters were the HCO<sub>3</sub>-Ca-Mg type. Multiple heavy metals (As, Cu, Co, Mn and Ni) showed much higher dissolved phase concentrations in permafrost creeks and ice than in Kolyma and its tributaries, and only in the permafrost samples and one Kolyma tributary have we detected dissolved Ti or Hg. In thermokarst lakes, several metal and metalloid dissolved concentrations increased with water depth (Fe, Mn, Ni and Zn - in both lakes; Al, Cu, K, Sb, Sr and Pb in either lake), reaching 1370 µg/L Cu, 4610 µg/L Mn, and 687 µg/L Zn in the bottom water layers. Permafrost-related waters were also enriched in dissolved phosphorus (up to 512 µg/L in Yedoma-fed creeks). The impact of permafrost thaw on river and lake water chemistry is a complex problem which needs to be considered both in the context of legacy permafrost shrinkage and the interference of the deepening active layer with newly deposited antropogenic contaminants.</p>
Data associated with the Turbet et al. 2023 manuscript (GCM simulations of TRAPPIST-1b, c and d)
<p>GCM simulations of TRAPPIST-1b, c and d, assuming H2O- and CO2-dominated atmospheres (in netCDF format). The GCM simulations were performed with the Generic PCM, historically known as the LMD Generic GCM. The simulations were used in the Turbet et al. 2023 manuscript.</p>
Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"
<p><strong>File 1:</strong> raw_data_BOULDERING.zip</p> <p><strong>Size:</strong> 8.8 GB</p> <p><strong>Summary: </strong>It contains all of the rasters (planetary images) and labeled boulders (raw data):</p> <ul> <li> <p>a boulder-mapping file, which is the manually digitized outline of boulders.</p> </li> <li> <p>a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted.</p> </li> <li> <p>a global-tiles file, which shows all of the image patches within a raster.</p> </li> </ul> <p>There are multiple locations/images per planetary body.</p> <p><strong>Structure:</strong></p> <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-ROM.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif ├── mars/ │ └── image_name/ │ ├── shp/ │ │ ├── <image_name>-ROM.shp │ │ ├── <image_name>-boulder-mapping.shp │ │ └── <image_name>-global-tiles.shp │ └── raster/ │ └── <image_name>.tif └── moon/ └── image_name/ ├── shp/ │ ├── <image_name>-ROM.shp │ ├── <image_name>-boulder-mapping.shp │ └── <image_name>-global-tiles.shp └── raster/ └── <image_name>.tif</pre> <p> </p> <p><strong>File 2:</strong> best_model.zip</p> <p><strong>Size:</strong> 624.7 MB</p> <p><strong>Summary:</strong></p> <p>This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics):</p> <ul> <li> <p>augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information.</p> </li> </ul> <ul> <li> <p>Base-RCNN-FPN.yaml (base model setup file).</p> </li> <li> <p>config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file).</p> </li> <li> <p>Mars-MoonEarth-v050...yaml (model setup file).</p> </li> <li> <p>log.txt (log during training of the algorithm).</p> </li> <li> <p>model_0055999.pth (model weights at second last saving step)</p> </li> <li> <p>model_0063999.pth (model weights at last saving step)</p> </li> </ul> <p>We advice the use of model weights model_0055999.pth (to avoid slight overfitting).</p> <p><strong>File 3:</strong> Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images)</p> <p><strong>Size:</strong> 252.8 MB</p> <p><strong>Summary:</strong></p> <p>This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files.</p> <ul> <li> <p>The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json".</p> </li> <li> <p>The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset.</p> </li> <li> <p>The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles.</p> </li> <li> <p>The shapefile folder is actually empty (it should not be there!).</p> </li> </ul> <p><strong>Structure:</strong></p> <pre>. └── preprocessed_inputs/ ├── json ├── pkl ├── preprocessing/ │ ├── train/ │ │ ├── images │ │ └── labels │ ├── validation/ │ │ ├── images │ │ └── labels │ └── test/ │ ├── images │ └── labels └── shp</pre> <p> </p>
Test film for Dating ancient manuscripts using radiocarbon and AI-based writing style analysis
<p>This film is associated with the following article:<br> Title: <strong>Dating ancient manuscripts using radiocarbon and AI-based writing style analysis</strong><br> Authors: Mladen Popović, Maruf A. Dhali, Lambert Schomaker, Johannes van der Plicht, Kaare Lund Rasmussen, Jacopo La Nasa, Ilaria Degano, Maria Perla Colombini, and Eibert Tigchelaar<br> <em>(under review)</em></p> <p> </p> <p>This film is made for the ERC project:<br> The Hands that Wrote the Bible: Digital Palaeography and Scribal Culture of the Dead Sea Scrolls<br> PI: Mladen Popović<br> Grant agreement ID: 640497<br> Project website: <a href="https://cordis.europa.eu/project/id/640497">https://cordis.europa.eu/project/id/640497</a><br> Videographer: <a href="https://videobrouwers.nl/">https://videobrouwers.nl/</a></p> <p><strong>Copyright (c) </strong> University of Groningen, 2023. All rights reserved.<br> <strong>Disclaimer and copyright notice for this file:</strong></p> <p><strong>1)</strong> permission is hereby granted to use the video for research purposes. It is not allowed to distribute this video for commercial purposes.</p> <p><strong>2) </strong>provider gives no express or implied warranty of any kind, and any implied warranties of merchantability and fitness for purpose are disclaimed.</p> <p><strong>3) </strong>provider shall not be liable for any direct, indirect, special, incidental, or consequential damages arising out of any use of this video.</p> <p><strong>4) </strong>the user should refer to the first public article mentioned above in this video.</p> <p><strong>5) </strong>the recipient should refrain from proliferating the video to third parties external to his/her local research group. Please refer interested researchers to this site to obtain their own copy.</p> <p> </p> <p><strong>Film description:</strong><br> A test was conducted on 6 July 2021. The test consisted of giving unseen <sup>14</sup>C results to the AI experts to see whether Enoch (date prediction model) would give date prediction estimates that match the <sup>14</sup>C results. However, at the start of the test, it was unknown to the AI experts that the samples were chosen because <sup>14</sup>C results were available for them. The <sup>14</sup>C results were taken from the 1990s <sup>14</sup>C dating of the Dead Sea Scrolls [1,2]. The assumption was that the manuscripts chosen were not contaminated with castor oil as these manuscripts were not handled by the original team of editors in the 1950s [3,4,5]. This applies to 1QIsa<sup>a</sup>, 1QpHab, 1QapGen, 1QS, 1QH<sup>a</sup>, 11Q19, Mas1l. Two more manuscripts were added for other reasons. 4Q53 was added because scholars assume that it was written by the same scribe as 1QS. 4Q319 was added because it is actually the same manuscript as 4Q259 [6], which was subjected to <sup>14</sup>C dating by our own project. The test was filmed. The film captures the whole process that was conducted in one go.</p> <p><strong>If you have any questions, please get in touch with us:</strong><br> Mladen Popović <m.popovic(at)rug.nl><br> Maruf A. Dhali <m.a.dhali(at)rug.nl><br> Lambert Schomaker <l.r.b.schomaker(at)rug.nl></p> <p><strong>References:</strong><br> <em>1.Bonani, G., Ivy, S., Wölfli, W., Broshi, M., Carmi, I., & Strugnell, J. (1992). Radiocarbon dating of fourteen Dead Sea scrolls. Radiocarbon, 34(3), 843-849.<br> 2. Jull, A. T., Donahue, D. J., Broshi, M., & Tov, E. (1995). Radiocarbon dating of scrolls and linen fragments from the Judean desert. Radiocarbon, 37(1), 11-19.<br> 3. Doudna, G., Flint, P. W., & VanderKam, J. C. (1998). Dating the Scrolls on the basis of radiocarbon analysis. The Dead Sea scrolls after fifty years. A comprehensive assessment. Volume one, 1, 430-471.<br> 4. Carmi, I. (2002). Are the 14C dates of the Dead Sea Scrolls affected by castor oil contamination? Radiocarbon, 44(1), 213-216.<br> 5. Rasmussen, K. L., van der Plicht, J., Doudna, G., Nielsen, F., Højrup, P., Stenby, E. H., & Pedersen, C. T. (2009). The effects of possible contamination on the radiocarbon dating of the Dead Sea Scrolls II: empirical methods to remove castor oil and suggestions for redating. Radiocarbon, 51(3), 1005-1022.<br> 6. Hempel, C. (2020). The Community Rules from Qumran: A Commentary (Vol. 183). Mohr Siebeck.</em></p>
Research generated data supporting the article manuscript "Setting Grounds for Data Literacy in the Sector of Agriculture: Learning About and with Open Data"
<p>In the research 345 MS courses and 216 MS courses data from the ECTS catalogue (2019) of University of Zagreb Faculty of Agriculture were mapped onto the data literacy competence areas (theme) and DL competence areas sub-themes adapted ODI Data Skills Framework (2020) expanding the term “skill” to “competence” to include knowledge and attitudes. Teaching staff was interviewed in semi-structured interviews on the data literacy competences covered in their courses and open data use and teaching in their courses as well as their perceived importance for the sector of the course.</p> <p>The upload consists of the following .csv files:</p> <table> <tbody> <tr> <td>readme_DL_OD_Salamonetal.csv</td> </tr> <tr> <td>01DL_OD_Salamonetal.csv</td> </tr> <tr> <td>02DL_OD_Salamonetal.csv</td> </tr> <tr> <td>03DL_OD_Salamonetal.csv</td> </tr> <tr> <td>04DL_OD_Salamonetal.csv</td> </tr> <tr> <td>05DL_OD_Salamonetal.csv</td> </tr> <tr> <td>06DL_OD_Salamonetal.csv</td> </tr> <tr> <td>07DL_OD_Salamonetal.csv</td> </tr> </tbody> </table> <p> </p> <p> </p> <p> </p>
Supplementary files for the manuscript "Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand", submitted to JGR Earth Surface
<p>This repository contains supplementary files to the manuscript ""Elevation-dependent periglacial and paraglacial processes modulate tectonically-controlled erosion of the Western Southern Alps, New Zealand" submitted to JGR: Earth Surface. It contains: </p> <p>- The Matlab script used to find the optimal distance-from-fault and elevation windows ("elevation_distance_window_optimization"), and 3 text files used for input in this script ("data_erates" contains the erosion rates, "data_elev" the number of pixels in each elevation bin, "data_distAF" the number of pixels in each distance-from-fault bin). </p> <p>- An Excel spreadsheet with the same information that the input text files contain, but specifiying the elevation or distance from fault bin values ("elevation and distance from fault with bins")</p> <p>- A shapefile of catchment outlines ("WSAcatch") for the catchments sampled for CRN denudation rates</p> <p>- Raw CRN data ("Table 2_new_CRN_data")</p> <p>- Excel spreadsheet with the compilation of themochronometric cooling ages used in the age2exhume code (van der Beek & Schildgen, 2023; <a href="https://doi.org/10.5281/zenodo.7341603">https://doi.org/10.5281/zenodo.7341603</a>).</p> <p>CRN data and catchment outlines will also be uploaded to the OCTOPUS database (<a href="https://octopusdata.org/">https://octopusdata.org/</a>) after manuscript acceptance.</p>
Data supporting manuscript "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble"
<p>Data supporting the results presented in the article Milovac et al: "Regional scaling of sea surface temperature with global warming levels in the CMIP6 ensemble".</p> <p>1. data_raw.tar contains annual and seasonal, global and regional (i.e. over ocean IPCC regions and ocean biomes), mean sea surface and near surface temperatures, calculated for the selected 26 CMIP6 global climate models (GCMs) at low resolution (listed in the file models_low_res.txt) and 1 GCM at high resolution (listed in the file models_high_res.txt). The original files, downloaded from one of the ESGF data centers, were all interpolated onto a common grid with the 1-degree resolution for low-resolution output and the 0.25-degree resolution for high-resolution output. The output was generated using the cdo tool (<a href="https://zenodo.org/record/7112925">https://zenodo.org/record/7112925</a>).</p> <p>2. data_txt.tar contains the results used to obtain all the figures given in the article.</p>
Dataset associated with the manuscript "A comprehensive assessment of anthropogenic and natural sources and sinks of Australasia's carbon budget" by Villalobos et al. (2023), part of the the second phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2).
<p>Dataset associated with the manuscript "A comprehensive assessment of anthropogenic and natural sources and sinks of Australasia’s carbon budget" by Villalobos et al. (2023), part of the the second phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2). </p>
Code and data for manuscript: Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir.
<p>This is the source code and data required to reproduce data analysis and figures from the manuscript, "Incorporating environmental heterogeneity and observation effort to predict host distribution and viral spillover from a bat reservoir". </p>
DMS measurements dataset for manuscript "Classification of Volatile Organic Compounds by Differential Mobility Spectrometry Based on Continuity of Alpha Curves"
<p>Differential mobility spectrometry dispersion plots collected for the manuscrit "Classification of Volatile Organic Compounds by Differential Mobility Spectrometry Based on Continuity of Alpha Curves". The measurement files are located in the folders that represent certain week and day of measurement. The folders containing measurements are named with the following pattern: [chemical abbreviation]_[dilution rate]. For example "2PEtOH_1o10k" means that the folder contains measurement of 2-phenylethanol diluted with propylene glycol in volumetric proportion 1/10 000. Another example is "nBuOH_1o100" - n-Butanol diluted with propylene glycol in volumetric proportion 1/100. Please find the abbreviations in the article referred.</p> <p>For the first five weeks only 1/100 dilutions were measured. The last two weeks (weeks 6 and 7) 1/10 000 dilutions were measured. However, Carvone 1/100 was measured again on week 6 due to suspicion of faulty measurements during the previous weeks. The faulty measurements were not confirmed, and thus there 25 more samples of Carvone with dilution rate 1/100.</p>
Scripts, models, and data for manuscript "On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media"
<p>This repository contains Matlab scripts, Comsol Multiphysics models, and numerical simulation data used to generate the plots in the manuscript</p> <p>Bücker, M., Flores Orozco, A., Undorf, S., and Kemna, A., 2019, <em>On the Role of Stern- and Diffuse-Layer Polarization Mechanisms in Porous Media</em>, submitted to JGR: Solid Earth.</p> <p>If you find this data useful in your own research, please cite this manuscript.</p>
MCR LTER: Coral Reef: Data to support manuscript: Experimental Support for Alternative Attractors on Coral Reefs
Ecological theory predicts that ecosystems with multiple basins of attraction can get locked in an undesired state, which has profound ecological and management implications. Despite their significance, alternative attractors have proven to be challenging to detect and characterize in natural communities. On coral reefs, it has been hypothesized that persistent coral-to-macroalgae ‘phase shifts’ that can result from overfishing of herbivores and/or nutrient enrichment may reflect a regime shift to an alternate attractor, but to date the evidence has been equivocal. Our field experiments in Moorea, French Polynesia, revealed: (1) hysteresis in the herbivory - macroalgae relationship, creating the potential for coral - macroalgae bistability at some levels of herbivory, and (2) that macroalgae were an alternative attractor under prevailing conditions in the lagoon but not on the fore reef where ambient herbivory fell outside the experimentally delineated region of hysteresis. These findings help explain the different community responses to disturbances between lagoon and fore reef habitats of Moorea over the past several decades, and reinforce the idea that reversing an undesired shift on coral reefs can be difficult. Our experimental framework represents a powerful diagnostic tool to probe for multiple attractors in ecological systems, and as such, can inform management strategies needed to maintain critical ecosystem functions in the face of escalating stresses. These data are associated with a manuscript currently in review: Schmitt, R. J., Holbrook, S. J., Davis, S. L., Brooks, A. J., Adam, T. C. Experimental support for alternative attractors on coral reefs. The dataset includes data from two different experimental tests of alternate attractors.
MCR LTER: Coral Reef: Data for manuscript J.Exp.Bio 2020 Edmunds and Burgess
This dataset contains allometry measurements on corals in tanks conditioned to simulate varied carbon dioxide partial pressure to test how coral colony size modulates PCO2 and temperature sensitivity in a branching acroporid. Data for Figure 1 in a manuscript submitted by P. Edmunds and S. Burgess to J of Experimental Biology in 2020 titled 'Emergent properties of branching morphologies modulate the sensitivity of coral calcification to high PCO2' This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2020). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
ribosomeprofiling/ribo_manuscript_supplemental: Supplementary material for the Ribo ecosystem manuscript
<p>Supplementary material for the Manuscript RiboFlow, RiboR and RiboPy: An ecosystem for analyzing ribosome profiling data at read length resolution</p>
Data related to the manuscript "Bayesian Calibration and Validation of a Large-scale and Time-demanding Sediment Transport Model"
<p>1) Riverbed_Elevation_Measurements.txt<br> Description: Measured riverbed geometry of available years<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2002 [m asl], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation <br> 2013 [m asl]<br> ----------------------------------------------------------------------------------------------------------------------------<br> 2) Hydro_FT_2D_manual.txt<br> Description: Simulation results of the manually calibrated full model<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]</p> <p>3.1) Hydro_FT_2D_CollocationPointBase.txt<br> Description: Parameter combinations of the collocation point base for each of the 20 simulations conducted with the full model to <br> construct the surrogate<br> Rows: Critical Shields parameter, Grain Roughness, Grain Size distribution</p> <p>3.2) Hydro_FT_2D_CollocationResults.txt<br> Description: Simulation results of the 20 simulations conducted with the full model at the collocation points<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig<br> [m asl], Elevations 2010 [m asl] of simulation 1 through 20, Node ID, Easting [m asl], Northig [m asl], Elevations 2013 [m asl] of<br> simulation 1 through 20<br> ----------------------------------------------------------------------------------------------------------------------------<br> 4.1) aPC_MC_N_Combinations_Weights_prior.txt<br> Description: ID of prior MC runs with tested parameter combinations and corresponding importance weights<br> Rows: ID of MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights<br> 4.2) aPC_MC_2005_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2005<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of MC run 1 through 100,000<br> 4.3) aPC_MC_2010_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2010<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of MC run 1 through 100,000<br> 4.4) aPC_MC_2013_prior.txt<br> Description: aPC surrogate results of prior MC runs for 2013<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2013 [m asl] of MC run 1 through 100,000<br> <br> 4.5) aPC_MC_N_Combinations_Weights_posterior.txt<br> Description: ID of accepted (posterior) MC runs with tested parameter combinations and corresponding importance weights<br> Rows: ID of accepted MC runs, Critical Shields parameter, Grain Roughness, Grain Size distribution, importance weights<br> 4.6) aPC_MC_2005_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2005<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2005 [m asl] of accepted MC run 1 through 857<br> 4.7) aPC_MC_2010_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2010<br> Columns: Node ID, Easting [m], Northig [m], Elevations 2010 [m asl] of accepted MC run 1 through 857<br> 4.8) aPC_MC_2013_posterior.txt<br> Description: aPC surrogate results of posterior MC runs for 2013<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2013 [m asl] of accepted MC run 1 through 857<br> ----------------------------------------------------------------------------------------------------------------------------<br> 5) aPC_MAP.txt<br> Description: Simulation results conducted with the stochastically calibrated aPC surrogate model using the MAP parameter <br> combination<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]</p> <p>6) Hydro_FT_2D_MAP.txt<br> Description: Simulation results conducted with the stochastically calibrated full model using the MAP parameter combination<br> Columns: Node ID, Easting [m], Northig [m], Elevation 2005 [m asl], Elevation 2010 [m asl], Elevation 2013 [m asl]<br> ----------------------------------------------------------------------------------------------------------------------------<br> 7) dz.txt<br> Description: Riverbed Evolution for all nodes in the section of interest (n=1138) obtained with differently calibrated models for all <br> considered time periods<br> Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m], <br> Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],<br> Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],<br> Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]</p> <p>8) dz_CalibrationNodes.txt<br> Description: Riverbed Evolution for calibration nodes (n=204) obtained with differently calibrated models for all considered time<br> periods<br> Columns: Node ID, Easting [m asl], Northig [m asl], aPC_prior 2005 [m], aPC_posterior 2005 [m], aPC_MAP 2005 [m], <br> Hydro_FT-2D_MAP 2005 [m], Hydro_FT-2D_manual 2005 [m], aPC_prior 2010 [m], aPC_posterior 2010 [m], aPC_MAP 2010 [m],<br> Hydro_FT-2D_MAP 2010 [m], Hydro_FT-2D_manual 2010 [m], aPC_prior 2013 [m], aPC_posterior 2013 [m], aPC_MAP 2013 [m],<br> Hydro_FT-2D_MAP 2013 [m], Hydro_FT-2D_manual 2013 [m]</p> <p> </p>
Output data for manuscript "Tidal analysis of GNSS reflectometry applied for coastal sea level sensing in Antarctica and Greenland"
<p>We retrieve sea levels in polar regions via GNSS reflectometry (GNSS-R), using signal-to-noise ratio (SNR) observations from eight POLENET GNSS stations. Although geodetic-quality antennas are designed to boost the direct reception from GNSS satellites and to suppress indirect reflections from natural surfaces, the latter can still be used to estimate the sea level in a stable terrestrial reference frame. Here, typical GNSS-R retrieval methodology is improved in two ways, 1) constraining phase-shifts to yield more precise reflector heights and 2) employing an extended dynamic filter to account for the second-order height rate of change (vertical acceleration). We validate retrievals over a 4-year period at Palmer Station (Antarctica), where there is a co-located tide gauge (TG). Because ice contaminates the long-period tidal constituents, we focus on the main tidal species (daily and subdaily), by employing a deseasonalization filter. The difference between sub-hourly GNSS-R retrievals of the ocean surface and TG records has a root-mean-square error (RMSE) of 15.4 cm and a correlation of 0.903, while the tidal prediction has a RMSE of 1.9 cm and a correlation of 0.998. There is excellent millimetric agreement between the two sensors for most eight major tidal constituents, with the exception of luni-solar diurnal (<em>K<sub>1</sub></em>), principal solar (<em>S<sub>2</sub></em>), and luni-solar semidiurnal (<em>K</em><sub>2</sub>) components, which are biased in GNSS-R due to the leakage of the GPS orbital period. We also compare the GNSS-R tidal constituents from seven additional POLENET sites, without co-located TG, to global and local ocean tide models. We find that the root-sum-square-error (RSSE) of eight major constituents varies between 26.0 cm and 56.9 cm for different models. Given that the agreement in tidal constituents between the TG and GNSS-R was better at Palmer Station, we conclude that assimilating the GNSS-R retrievals into tidal models would improve their accuracy in Antarctica and Greenland, provided that care is exercised to avoid the orbital period overtones and also sea ice.</p>
Supplement of manuscript "microorganisms-746193"
<p>This folder contains analysis data from the study submitted as manuscript (Manuscript ID microorganisms-746193):</p> <p>TITLE:</p> <p>Functional genomics differentiate inherent and environmentally influenced traits in dinoflagellate and diatom communities</p> <p>AUTHORS:</p> <p>Stephanie Elferink, <a href="mailto:Stephanie.westphal@awi.de"> Stephanie.westphal@awi.de</a>, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research<sup> </sup> </p> <p>Uwe John, <a href="mailto:Uwe.John@awi.de"> Uwe.John@awi.de</a>, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, and Helmholtz Institute for Functional Marine Biodiversity</p> <p>Stefan Neuhaus, <a href="mailto:Stephan.neuhaus@awi.de">Stephan.neuhaus@awi.de</a>, Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research<sup> </sup> </p> <p>Sylke Wohlrab, <a href="mailto:Sylke.wohlrab@awi.de"> Sylke.wohlrab@awi.de</a>, Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, and Helmholtz Institute for Functional Marine Biodiversity</p> <p>JOURNAL:</p> <p>MDPI - microorganisms</p> <p>MS-ID:</p> <p>microorganisms-746193</p> <p>HOWTO:</p> <p>Sequences identified as Alveloates or Stramenopiles (description in manuscript) had been were classified more accurately by PhyloAssigner version 6.166 (https://github.com/jungbluth/phyloassigner, Vergin et al., 2013, DOI:10.1038/ismej.2013.32) with a phylogenetic placement onto reference trees based on 18S/28S concatenated alignments, according to Elferink et al. 2017 (DOI: 10.1016/j.dsr.2016.11.002).</p> <p>CONTENT:</p> <p>reference databases:</p> <p>- Alveolata_SSU-LSU-concat_310715_636.phyloassignerdb</p> <p>- Stramenopiles_SSU_LSU_concat_030815_1777.phyloassignerdb</p> <p>query sequence files:</p> <p>- Alveolata_seqtab_SIGN_dada2.fasta</p> <p>- Alveolata_seqtab_SIGN_dada2.fasta</p> <p>created output folder including the taxonomic annotation:</p> <p>- Alveolata_seqtab_SIGN_dada2.place.out</p> <p>- Stramenopiles_seqtab_SIGN_dada2.place.out</p> <p>text file containing the used commands:</p> <p>- commands</p>
Hubei STEC Data through CORS stations for DOY 059 and 061 of the year 2018 which used in (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City, manuscript submitted to Earth and Space Science Journal AGU)
<p>Manuscript submitted to Earth and Space Science AGU entitled with <br> (Using Real GNSS Data for Ionospheric Disturbance Remote Sensing Associated with Strong Thunderstorm over Wuhan City)<br> by: Mohamed Freeshah, Xiaohong Zhang, Xiaodong Ren, Jun Chen, and Zhibo Zhao</p> <p>The STEC data inside two compressed folders named as stec059 and stec061, respectively.<br> The STEC file name has the CORS station name for the first forth letters and next three numbers epresent the Day of the year.<br> For example:<br> ES010590.18STEC<br> ES01 is the station name<br> 059 is the day of year (DOY), 2018</p>
Configuration files for model stations presented in the manuscript "Sensitivity of shelf sea marine ecosystems to temporal resolution meteorological forcing"
<p>This repository contains configuration files for running GOTM-FABM-ERSEM at stations L4 and CCS to produce results presented in the manuscript "Sensitivity of shelf sea marine ecosystems to meteorological forcing" in addition to meteorology files for running the sensitivity analysis presented in the manuscript. Ncfiles containing model results for all scenarios presented in the manuscript are also included within the zip files for both stations</p> <p><br> GOTM code is freely available from: <br> https://github.com/gotm-model/code</p> <p><br> FABM code is freely available from:<br> https://github.com/fabm-model/fabm.git</p> <p><br> ERSEM code is freely available from:</p> <p><a href="https://www.pml.ac.uk/Modelling_at_PML/Access_Code">https://www.pml.ac.uk/Modelling_at_PML/Access_Code</a><br> </p> <p>Instructions for compiling GOTM-FABM-ERSEM can be found in the ERSEM git repository after registering for the code using the link above. </p> <p>Versions/commits for the model code used to create results presented in this manuscript are:</p> <p>GOTM: commit 38e5d5b77adc7b3b5364aed7d7e4921b04b1781f </p> <p>FABM: commit 69da88c87ec59a51d1e2143c1f76111526ed6498 </p> <p>ERSEM: Version 19.04</p> <p> </p> <p> </p>
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Annotated Behaviour and Observability Dataset (ABODe)
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