cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.34808/jp15-4707
title: Long-term hindcast simulation of sea level in the Baltic Sea

description: The dataset contains the results of numerical modelling of sea level fluctuations over a period of 50 years (1958-2007) in the Baltic Sea (Jędrasik and Kowalewski, 2019). A long-term hindcast simulation was performed using a three-dimensional hydrodynamic model PM3D (Kowalewski and Kowalewska-Kalkowska, 2017), a new version of the M3D model (Kowalewski, 1997). A numerical dynamic-thermodynamic model of sea ice (Herman et al. 2011) was coupled to the analysis of ice conditions variability.
The model is based on the Princeton Ocean Model (POM) developed at Princeton University (Blumberg and Mellor, 1987). Like POM, the M3D/PM3D model uses the Mellor-Yamada turbulence scheme and sigma vertical coordinates. The simulation was carried out in a rectangular grid of 3' (latitude) and 6' (longitude) resolution. The spatial resolution was 3 nautical miles, i.e. about 5.5 km. An open boundary was located between the Skagerrak and the Kattegat along the parallel connecting Skagen and Göteborg, where the water exchange with the North Sea takes place. A radiation condition based on Somerfield’s concept for velocities  vertically averaged and normal to the boarder  plane was applied. Atmospheric forcing for the period 1958-2007 was performed using the regional at amospheric climate model REMO (REgional MOdel) which was based on the numerical weather prediction model EM (Europa-Model) of the German Weather Forecast Service (DWD).
The sea level spatial distributions were recorded in the model's computational grid in unprojected form (LatLong, WGS 84, EPSG: 4326) with an hourly time interval. Sea level values are expressed in metres relative to the mean level of the Baltic Sea in the PM3D model. The validation of the sea level modeling results for the period 2010-2015 showed (Kowalewski and Kowalewska-Kalkowska, 2017) a root mean square error (RMSE) of 8 to 13 cm for various stations in the Baltic Sea.
Kowalewski M.;1997; A three-dimensional, hydrodynamic model of the Gulf of Gdańsk; Oceanol. Stud., 26 (4); 77–98
Kowalewski M., Kowalewska-Kalkowska H., 2017, Sensitivity of the Baltic Sea level prediction to spatial model resolution, Journal of Marine Systems 173, 101–113, http://dx.doi.org/10.1016/j.jmarsys.2017.05.001
Jędrasik J., Kowalewski M., 2019, Mean annual and seasonal circulation patterns and long-term variability of currents in the Baltic Sea, J. Mar. Syst., 193, 1-26, doi:10.1016/j.jmarsys.2018.12.011
Herman, A., Jedrasik, J., Kowalewski, M., Herman, Agnieszka, Kowalewski, Marek. (2011). Numerical modelling of thermodynamics and dynamics of sea ice in the Baltic Sea. Ocean Science, 7(2), 257-276. https://doi.org/10.5194/os-7-257-2011
DOI: 10.5281/zenodo.3939510
title: IODP Expedition 318, Hole U1361A - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 318: Wilkes Land Glacial History
DOI: 10.17632/zz4mmbbmp9.1
title: FB2Droid：A Novel Malware Family Based Bagging Algorithm for Android Malware Detection

description: This dataset contains 8000 malware and 8000 benign Android applications
DOI: 10.5281/zenodo.3939509
title: IODP Expedition 318, Hole U1361A - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 318: Wilkes Land Glacial History
DOI: 10.5281/zenodo.3939506
title: IODP Expedition 318, Hole U1359D - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 318: Wilkes Land Glacial History
DOI: 10.17632/y7n82jcj8d.1
title: MARTY - Modern ARtificial Theoretical phYsicist A C++ framework automating theoretical calculations Beyond the Standard Model

description: Studies Beyond the Standard Model (BSM) will become more and more important in the near future with the rapidly increasing amount of data from different experiments around the world. The full study of BSM models is in general an extremely time-consuming task involving long and difficult calculations. It is in practice not possible to do exhaustive predictions in these models by hand. Here we present MARTY, a new C++ framework that fully automates calculations from the Lagrangian to physical quantities such as amplitudes or cross-sections. It can fully simplify, automatically and symbolically, physical quantities in a very large variety of models and compute Wilson coefficients in effective theories. This will considerably facilitate BSM studies in flavour physics. Contrary to the existing public codes in this field MARTY aims at providing a unique, free, open-source, powerful and user-friendly tool for high-energy physicists studying predictive BSM models, in effective or full theories up to the one-loop level, which does not rely on any external package. With a few lines of code one can gather final expressions that may be evaluated numerically for statistical analysis.
DOI: 10.5281/zenodo.3939505
title: IODP Expedition 318, Hole U1359D - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: IODP Expedition 318: Wilkes Land Glacial History
DOI: 10.17632/b9ndfn4f48.1
title: biochemical

description: biochemical profile in covid-19
DOI: 10.17632/pwkdwprv3j.2
title: Algorithm for constrained multi-objective land use allocation optimization under uncertainty

description: This dataset describes the software and data to quantify uncertainty in Pareto fronts arised from spatial data. This dataset contains the Python code for a multi-objective land use allocation optimization under uncertainty. The program is an extension to CoMOLA from Strauch et al. 2019 (https://doi.org/10.1016/j.envsoft.2019.05.003). For a detailed description of CoMOLA, we refer to the article. Before executing CoMOLA under uncertainty, extreme lower and upper bound samples need to be generated from the land use and soil fertility map with quantified uncertainty. That preprocessing is described reproducable with the following Mendeley Dataset: Hildemann, Moritz Jan; Verstegen, Judith (2021), “Sampling procedure of land use and soil fertility map under uncertainty”, Mendeley Data, V2, doi: 10.17632/6x6cccfc4x.1. For every produced extreme sample, CoMOLA needs to be performed with the corresponding land use and soil fertility map. As the computational effort and computation time are high (15-20 hours) and ten optimizations were performed for every extreme sample, the runs were performed in parallel on a high-performance Linux cluster (MEGWARE cluster with 15.120 cores, 412 nodes and Intel Xeon Gold 6140 18C 2.30GHz processors). The program is executable for Python 3.7 and 3.8 in a Linux environment. The changes compared to CoMOLA include: an update to Python 3.8, removal of R components in objecting the objective values, and the implementation of a seeding procedure to inject single-objective optima into the first generation of the Genetic Algorithm. The seeding procedure allowed faster and better convergence. The generated Pareto fronts can be used postprocessing to quantify the uncertainty in objective and solution space. Pseudo-random states are used to assure reproducibility despite the stochastic processes.
DOI: 10.5281/zenodo.3945472
title: ODP Leg 165, Hole 1001A - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 165: Caribbean Ocean History and the Cretaceous-Tertiary Boundary Event
DOI: 10.5281/zenodo.3945471
title: ODP Leg 165, Hole 1001A - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 165: Caribbean Ocean History and the Cretaceous-Tertiary Boundary Event
DOI: 10.17632/rk74fhjhzc.1
title: Study data

description: Examination of dam surface suitability for floating photovoltaic system installation with RS and GIS science technologies
DOI: 10.17632/6x6cccfc4x.2
title: Sampling procedure of land use and soil fertility map under uncertainty

description: The dataset is composed of input data, the code to process the data and the output data from a sampling procedure for spatial data. The sampling procedure is used to derive samples, that result in extreme lower and upper bound single-objective values. The extreme lower and upper bound samples are used to approximate the extreme Pareto fronts when performing the multi-objective optimization with the samples as input data. To the input data belong a land use map, a patch ID map, a soil fertility map and an uncertainty map, which all have 10*10 cells. In addition, two matrices are included. One matrix defines transition constraints for the land uses of the land use map, the second matrix defines related classification accuracies per land use. The land use map, patch ID map, transition matrix and soil fertility map were reused from the CoMOLA project, produced for research from Strauch et al. 2019 (https://doi.org/10.1016/j.envsoft.2019.05.003). The outputs are divided into three categories. One category is about creating samples from the uncertain land use map with the original soil fertility map. The second category is about creating samples from the uncertain soil fertility map with the original soil fertility map. The third category is about creating samples from both uncertain maps combined. For the first category, the land use maps and the corresponding patch ID maps were stored that resulted in extremely low or high objective values of habitat heterogeneity, forest species richness, crop yield and water yield from 1000 samples (computation time: &lt;1 minute). For category 2, the soil fertility maps are stored that resulted in extremely low and high crop yield objective values with the original land use map were stored from 1000 samples, because the soil fertility map only influences the objective crop yield (computation time: &lt;10 seconds). For category 3, two different soil fertility maps and two different land use map with the corresponding patch ID maps that lead to the extreme lower and upper bound crop yield values are stored. Here, the 1000 samples of category 1 and 2 are reproduced and each sampled land use map is evaluated against every sampled soil fertility map (computation time: ~2 hours). For convenience, additional pickle files were saved containing extreme solutions, extreme objective values and extreme objective values per iteration of the sampling procedure to plot the progression. The execution of the Python script SpatialSampling.py (Python 3.8) needs to be executed to reproduce the sampling procedure. After the simulations, plots are generated in case that the boolean parameter "show_visualizations" (beginning of script) is set to True. Pseudo-random states are used to assure reproducibility despite the stochastic processes.
DOI: 10.17632/tkmxw33y5c.1
title: Data on the implementation of distance learning during the COVID-19 pandemic in Yogyakarta City, Indonesia

description: The data were obtained by distributing survey instruments to elementary school teachers in Yogyakarta City for one week, starting from March 1, 2021, to March 8, 2021. The instrument was developed using Google Form so that the link could be spread easily by researchers to teachers. The data results were then sorted at the clearance step using Microsoft Excel, and only data from elementary school teachers in the city of Yogyakarta were used.
DOI: 10.17632/wd27d4tg77.1
title: COVID impact on drug market and user behaviour_GE

description: The aim of the study was to examine potential impact of COVID-19 containment measures on illicit drug supply and drug use behaviour among people who use drugs (PWUD) regularly in Tbilisi, Georgia. A prospective cohort of 50 Georgian PWUD participated in a bi-weekly online survey in April – September, 2020. They also took part in qualitative interviews at 3- and 6-month follow-up time points. In addition, cohort of 4 key informants were interviewed every month to assess their perceptions of changes in illicit drug supply and drug use behaviours. Availability of drugs was reduced during the lockdown and many PWUD switched to use alternative substances when preferable drugs were not available. The variety of substances used by study participants at the end of the study was smaller compared to the baseline use. Findings regarding changes in price and quality of drugs were mixed. When stable contacts with dealers were affected PWUD searched for new contacts and supply options. When access to sterile injection equipment was limited, PWUD exercised risk-containing injection behaviours. Despite some interruptions in service delivery during the first weeks of lockdown, providers of treatment and harm reduction services showed remarkable flexibility and were able to implement effective strategies to deliver services.
DOI: 10.17632/jk3wr7crj7.2
title: MEcGANs

description: This repository contains data, results, and saved models for the manuscript titled "Cloud Removal from Satellite Imagery using Multispectral Edge-filtered Conditional Generative Adversarial Networks". The implementation of the MEcGANs method presented in the manuscript is available at https://github.com/andrzejmizera/MEcGANs. The repository contains data that are based on freely available satellite images from the WorldView-2 European Cities dataset (https://earth.esa.int/eogateway/catalog/worldview-2-european-cities). Data provided by the European Space Agency. © ESA (2020). © DigitalGlobe, Inc. (2020), provided by European Space Imaging. Data.zip: - The 'images' folder contains target RGB, co-registered NIR, cloud-mask images, a real cloud RGB image and a co-registered real cloud NIR image used to synthesise clouded RGB and NIR images in accordance with the description provided in the manuscript. - The 'images/RGB' and 'images/NIR' images were obtained with the use of freely available satellite images from the WorldView-2 European Cities dataset. Images of various cities were selected and cropped into a total of 2759 images of size 256x256. - The 'images/real_cloud_rgb_image.png' and 'images/real_cloud_nir_image.png' files contain an RGB image and a co-registered NIR image of a real cloud, respectively. They were obtained by extracting a part from one of large images available in the 'Landsat 8 Cloud Cover Assessment Validation Data' dataset (https://landsat.usgs.gov/landsat-8-cloud-cover-assessment-validation-data). - The 'datasets' folder contains lists of image filenames used to generate the train and test subsets of the three datasets used in the experiments, i.e. the edge-intensive, large, and small dataset. Results.zip: - Contains the complete sets of outputs generated by MEcGANs and McGANs for the three datasets used in the experiments for different values of NIR cloud penetrability, i.e. 1% and 0.5%, as described in the manuscript. A Jupyter notebooks for visual comparison of the generated images by the two methods and computation of four quality metrics, i.e. peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity index (SSIM), and spatial correlation coefficient (SCC), is provided. Checkpoints: Contains saved model checkpoints of the experiments described in the manuscript. The checkpoints can be used to resume model training or to run saved generators on new datasets as explained in the README.md document available at https://github.com/andrzejmizera/MEcGANs.
DOI: 10.5281/zenodo.3945480
title: ODP Leg 166, Hole 1003D - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 166: Bahamas Transect
DOI: 10.17632/3g89tzfbrv.2
title: Data for: arc magmatism developed in the Antarctic Peninsula and Patagonia during the Late Triassic – Jurassic

description: Table 1. Zircon raw U-Pb isotopic data obtained from the Jurassic magmatism of the Antarctic Peninsula. Concordant data is presented along with discordant analyses. Table 2. Raw U-Pb isotopic data collected from the zircons used as standards. While GJ-1 was used as primary standard, with a date of 600.5 ± 0.4 Ma (Boekhout et al., 2021; Ulianov et al., 2012), Plešovice was used as secondary standard with a date of 337.13 ± 0.37 Ma (Slama et al., 2008). Table 3. Raw data of Sr and Nd isotopic compositions obtained in whole-rock analyses presented in (Bastias et al., 2021). This data is also accompanied with relevant information such as lithology, U-Pb LA-ICP-MS zircon geochronology and geographical location. Table 4. Raw Lu-Hf isotopic data obtained from the zircons extracted from the Jurassic magmatism of the Antarctic Peninsula. Bastias, J. et al. 2021. Lithos 386-387, 106013. DOI: 10.1016/j.lithos.2021.106013. Boekhout, F., et al. 2012. Lithos 146–147, 48–64. Slama, J., et al. 2008. Chem. Geol. 249 (1–2), 1–35. Ulianov, A., et al. 2012. J. Anal. At. Spectrom. 27, 663–676.
DOI: 10.5281/zenodo.3945479
title: ODP Leg 166, Hole 1003D - Well Logging Data

description: Logging data are measurements of physical properties of the formation surrounding a borehole, acquired in situ after completion of coring (wireline logging) or during drilling (Logging-While-Drilling, LWD). The range of data (resistivity, gamma radiation, velocity, density, borehole images,…) in any hole depends on the scientific objectives and operational constraints.
description: ODP Leg 166: Bahamas Transect
DOI: 10.17632/65cp5dk4r9.1
title: Benjamin E Farrant PhD Thesis Electronic Appendix

description: This Electronic Appendix includes supplementary data to that reported within the thesis titled "Mobilisation and Loss of Noble Gas and Halogen Elements During the Impact Melting of Ordinary Chondrites" submitted for a PhD degree at the University of Manchester by Benjamin E Farrant. The spreadsheets contain quantitative electron probe microanalyzer (EPMA) point analysis data from various mineral phases. Data for five meteorites (Chelyabinsk, Chergach, Chico, Gao-Guenie and Kilabo) are provided and the names of the meteorites are named in the spreadsheet filename to indicate which meteorite the data within that spreadsheet relate to. Data for the minerals olivine, low and high-Ca pyroxene, chromite, ilmenite, feldspar, apatite and merrillite as well as data for feldspathic glass are included. Separate tabs within each spreadsheet indicate which mineral the data of that tab are from. These data are used to determine the average mineral chemistries reported within the text of the thesis.
DOI: 10.17632/99w85gxgrg.1
title: Datasets For risk factors on building construction projects in Aceh Province

description: This data is the result of collecting questionnaire data which was distributed to 68 contractor companies in Aceh province,
DOI: 10.17632/nh924v9j4s.1
title: ASSESMENT OF LITTER IN THE MANGROVE FOREST OF THE CIÉNAGA DE MALLORQUÍN, COLOMBIAN CARIBBEAN REGION

description: This dataset contains one excel file, is about the condition of anthropic litter in the mangrove ecosystem of the Ciénaga de Mallorquín, Atlántico - Colombia. Data obtained between July 30 and August 1, 2019. Abstract: The Ciénaga de Mallorquín (CM) is a unique coastal lagoon close to Barranquilla city. This study evaluated the state of pollution based on anthropic litter inside the mangrove forest in the CM. A total of 860 items of litter were collected, equivalent to an average density of 23.89 items/m^2. Plastic was the main litter item (43.5%), followed by polystyrene, rubber, and processed wood, most of which comes from inland sources such as dumping, recreational and urban activities, and is transported mainly by local streams. The CM needs an urgent, integral, and proactive management plan to reduce litter at its sources, adopting and improving measures such as education and public awareness, good management practices, and recycling and reuse.
DOI: 10.7284/139835
title: ADCP (Acoustic Doppler Current Profiler) data collected during research cruise AT42-24 using a Hawaii UHDAS instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139846
title: Fluorometer data collected during research cruise AT42-24 using a WET Labs WETStar instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139828
title: GNSS (Global Navigation Satellite System) data collected during research cruise AT42-24 using a C&C C-Nav 3050 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139831
title: GNSS (Global Navigation Satellite System) data collected during research cruise AT42-24 using a Furuno GP-90D instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139854
title: Gyrocompass data collected during research cruise AT42-24 using a Sperry MK-37 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139847
title: Meteorological Station data collected during research cruise AT42-24 using a Vaisala WXT520 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139848
title: Meteorological Station data collected during research cruise AT42-24 using a Vaisala WXT520 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139845
title: Multibeam Echosounder data collected during research cruise AT42-24 using a Kongsberg EM122 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139849
title: Multibeam Echosounder data collected during research cruise AT42-24 using a Kongsberg EM122 instrument system onboard the platform RV Atlantis

description: Original (unprocessed) data collected during a Field_expedition