cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/rft94c75zb.1
title: A database for a sediment yield analysis in a Raba River basin (Carpathian Mts)

description: The database contains results of the modeling study performed for the Raba River basin (Carpathian Mts., Poland) on the sediment yields. Using the calibrated and verified basin created in the DNS/SWAT Macromodel as a baseline scenario, subsequently the climate and land use scenarios were superimposed. The climate change scenarios were prepared using temperature and precipitation projections from the EuroCORDEX regional climate [https://euro-cordex.net/060378/index.php.en] and CMIP5 general circulation models [https://esgf-node.llnl.gov/projects/cmip5/][http://climateimpact.sggw.pl/map/proj/]. The land use change projections were based on the FORECOM project [http://www.gis.geo.uj.edu.pl/forecom/]. To track the sediment yields on the sub-basin level the Raba River basin was divided into 36 units. Impacts of single (hypothetical) and combined climate change and land use scenarios on sediment yield (t/ha and %) for each of these units (sub-basins) are displayed through the selection of database filters. The database consists of six Excel sheets: ‘scenarios’ - a list of scenarios prepared for the needs of the research together with their descriptions, whose impact on sediment yield in each sub-basin can be traced in the next sheets ‘results_plot’ - results of the individual scenarios impact on the sediment yield in the selected sub-basins and/or seasons the data is presented in the form of a pivot column chart. By using the "Select sub-basin or/and season" buttons user can filter any sub-basin and season values ‘changes_plot (t/ha)’ - sediment yield change for each scenario in comparison to baseline scenario in t/ha (functionality like previous sheet) ‘changes_plot (%)’ - sediment yield change for each scenario sediment yield change for each scenario in t/ha in comparison to baseline scenario in % (functionality like previous sheet) ‘sub-basins features’ - location of individual sub-basins, their numbers in the Raba River basin and main features: land use, slopes and soils classes share in the sub-basin area ‘data’ - output from the model for all scenarios (see sheet ‘scenarios’), average values of sediment yield for each season of data from 2005-2017 for 36 sub-basins in t/month
DOI: 10.17632/vc69fx4dkx.1
title: Data for: Modification of the “Acute” method for calculating the final preferred temperatures as applied to Daphnia longispina (Crustacea: Cladocera)
DOI: 10.7281/t1/1ecvb0/3oireh
title: DustOff-monthly-average_wrfout_d01_2007-03_18_mean.nc
DOI: 10.7281/t1/1ecvb0/3rzulk
title: DustOff-monthly-average_wrfout_d01_2007-10_12_mean.nc
DOI: 10.7281/t1/1ecvb0/9udite
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-04_18_std.nc
DOI: 10.7281/t1/1ecvb0/a4d9go
title: DustOff-monthly-average_wrfout_d01_2008-03_12_mean.nc
DOI: 10.7281/t1/1ecvb0/a9j2ce
title: DustOff-monthly-average_wrfout_d01_2007-03_00_mean.nc
DOI: 10.7281/t1/1ecvb0/andyjg
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-08_00_std.nc
DOI: 10.7281/t1/1ecvb0/atek8a
title: DustOff-monthly-average_wrfout_d01_2012-04_18_mean.nc
DOI: 10.7281/t1/1ecvb0/bj9z40
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-10_06_std.nc
DOI: 10.7281/t1/1ecvb0/csxtxw
title: DustOff-monthly-average_wrfout_d01_2012-06_06_mean.nc
DOI: 10.7281/t1/1ecvb0/d5sow9
title: DustOff-monthly-average_wrfout_d01_2007-06_18_mean.nc
DOI: 10.7281/t1/1ecvb0/djhqvr
title: DustOff-monthly-average_wrfout_d01_2012-08_12_mean.nc
DOI: 10.7281/t1/1ecvb0/gwg4yv
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-04_06_std.nc
DOI: 10.7281/t1/1ecvb0/hgy1nb
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-03_12_std.nc
DOI: 10.7281/t1/1ecvb0/hqumak
title: DustOff-monthly-average_wrfout_d01_2008-08_06_mean.nc
DOI: 10.7281/t1/1ecvb0/iybgii
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-03_00_std.nc
DOI: 10.7281/t1/1ecvb0/jgt5or
title: DustOff-monthly-average_wrfout_d01_2012-07_18_mean.nc
DOI: 10.7281/t1/1ecvb0/ka4oqb
title: DustOff-monthly-average_wrfout_d01_2007-04_12_mean.nc
DOI: 10.7281/t1/1ecvb0/m4hs9u
title: DustOff-monthly-average_wrfout_d01_2012-03_06_mean.nc
DOI: 10.7281/t1/1ecvb0/mrlxf1
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-10_18_std.nc
DOI: 10.7281/t1/1ecvb0/nz5z3h
title: DustOff-monthly-average_wrfout_d01_2007-09_12_mean.nc
DOI: 10.7281/t1/1ecvb0/p8l2zl
title: DustOff-monthly-average_wrfout_d01_2012-07_00_mean.nc
DOI: 10.7281/t1/1ecvb0/pzbepo
title: DustOff-monthly-average_wrfout_d01_2008-09_00_mean.nc
DOI: 10.7281/t1/1ecvb0/uxmj9e
title: DustOff-monthly-average_wrfout_d01_2012-08_18_mean.nc
DOI: 10.7281/t1/1ecvb0/wx0rfb
title: DustOff-monthly-average_wrfout_d01_2012-05_12_mean.nc
DOI: 10.17632/fc2r9rdxbt.1
title: Dataset of an in-use tertiary building collected from a detailed 3D Mobile Monitoring System and Building Automation System for indoor and outdoor air temperature analysis

description: A Mobile Monitoring System (MMS) has been designed having into account the use of technology with high sensor accuracy and with the capacity to be installed easily and quickly in different cardinal locations, distribution spaces, volumes and at different heights of a tertiary in-use building located in Leioa (Bilbao).Two types of MMS have been designed with the aim of doing two types of analysis, one intended to do the indoor air temperature uncertainty analysis and another focused on doing the outdoor air temperature uncertainty analysis. Eight tripods compose the indoor MMS with twenty sensors at different heights, which have been installed in different building’s offices in order to collect indoor temperature measurements at different heights and locations. As well, eight sensors compose the outdoor MMS to collect data of outdoor temperature measurements. Both MMS have been integrated to the existing Building Automation System (BAS) of the tertiary building; some data collected by the BAS have also been taken into account for the indoor and outdoor temperature uncertainty analysis. Seven tests have been carried out, five tests for the indoor temperature analysis and two tests for outdoor analysis. In the case of indoor MMS, one test has collected temperature data from all sensors together in the same place and at the same height, and four tests were carried out in four different offices with different typologies; all tests have been performed using the MMS at different periods using the same eight tripods. With the outdoor MMS, again, one test has collected temperature data of all sensors together located at the building’s roof and the final test has been carried out installing the sensors around the envelope of the building at different heights and cardinal locations. The potential of the datasets from the indoor and outdoor MMSs is based on the rigorous data collection process, which allows making an analysis of: temperature uncertainties, temperature stratification, temperature spatial behaviour and temperature behaviour analysis due to impact of solar radiation, heating system and electricity consumption.
DOI: 10.17632/x2txw2dw4t.1
title: Suitability of different rice varieties into value addition

description: The following were the reserch questions;Does rice variety affect the proximate composition, functional properties and cooking characteristics of rice flour? and Do different varieties of rice have favorable proximate analysis, functional properties and cooking characteristics for product development?Proximate composition, functional properties and cooking characteristics of rice flour namely moisture , ash, crude protein , swelling index, hot paste and gelatinization temperature for the following rice varieties: Lifuwu, Senga, Nerica, Nunkile, Wambone, Faya, Kilombero, Mpheta, Mpatsa, Nazolo, Mtupatupa, Kayanjamalo and Katete were determined using official analytical methods. Katete was high in protein (7.47g/100g). MC (13.04g/100g) and SI (5.41g/g) were highest in Faya 14M69. Kayanjamalo formed a thick paste as compare to other varieties. Nerica 4, Faya14M69, Mtupamtupa, Kilombero, Nunkile, Nanzolo and Mpatsa had an intermediate GT (70-74oC), while Mpheta, Kayanjamalo, Katete, Senga, Wambone and Lifuwu had high GT (74.5 -80oC). Rice with intermediate GT break easily during cooking.
DOI: 10.7281/t1/1ecvb0/2ouy4m
title: DustOff-monthly-average_wrfout_d01_2008-05_06_mean.nc
DOI: 10.7281/t1/1ecvb0/3mjuln
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-08_06_std.nc
DOI: 10.7281/t1/1ecvb0/3polhw
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-04_00_std.nc
DOI: 10.7281/t1/1ecvb0/4hymnu
title: DustOff-monthly-average_wrfout_d01_2012-04_06_mean.nc
DOI: 10.7281/t1/1ecvb0/7em9j9
title: DustOff-monthly-average_wrfout_d01_2012-10_06_mean.nc
DOI: 10.7281/t1/1ecvb0/7pb9cs
title: DustOff-monthly-average_wrfout_d01_2012-07_12_mean.nc
DOI: 10.7281/t1/1ecvb0/9h6ize
title: DustOff-monthly-average_wrfout_d01_2008-10_18_mean.nc
DOI: 10.7281/t1/1ecvb0/apbikq
title: DustOff-monthly-average_wrfout_d01_2012-09_06_mean.nc
DOI: 10.7281/t1/1ecvb0/cnslt6
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-04_12_std.nc
DOI: 10.7281/t1/1ecvb0/f0js8d
title: DustOff-monthly-average_wrfout_d01_2012-05_00_mean.nc
DOI: 10.7281/t1/1ecvb0/i8pk5r
title: DustOff-monthly-average_wrfout_d01_2008-10_00_mean.nc
DOI: 10.7281/t1/1ecvb0/ksce2g
title: DustOff-monthly-average_wrfout_d01_2012-08_00_mean.nc
DOI: 10.7281/t1/1ecvb0/lkgtgr
title: DustOff-monthly-average_wrfout_d01_2008-06_12_mean.nc
DOI: 10.7281/t1/1ecvb0/mnvdko
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-09_00_std.nc
DOI: 10.7281/t1/1ecvb0/mq18os
title: DustOff-monthly-average_wrfout_d01_2008-09_18_mean.nc
DOI: 10.7281/t1/1ecvb0/mync6d
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-10_00_std.nc
DOI: 10.7281/t1/1ecvb0/ojsg8a
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-08_18_std.nc
DOI: 10.7281/t1/1ecvb0/phurvb
title: DustOff-monthly-average_wrfout_d01_2008-03_18_mean.nc
DOI: 10.7281/t1/1ecvb0/pmutsb
title: DustOff-monthly-average_wrfout_d01_2012-05_18_mean.nc
DOI: 10.7281/t1/1ecvb0/s9ntvj
title: DustOff-monthly-average_wrfout_d01_2013-08_00_mean.nc
DOI: 10.7281/t1/1ecvb0/w5njnp
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-09_12_std.nc
DOI: 10.7281/t1/1ecvb0/x9n4x8
title: DustOff-monthly-average_wrfout_d01_2008-04_00_mean.nc
DOI: 10.7281/t1/1ecvb0/xnh9wd
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-10_12_std.nc
DOI: 10.7281/t1/1ecvb0/zqg8cd
title: DustOff-monthly-average_wrfout_d01_2008-04_18_mean.nc
DOI: 10.17632/69vjpx8xyg.1
title: Data for Rheological behaviour of high concentration MWCNT/Water Nanofluids

description: In this work, 0.3, 0.5, 1 and 1.5 wt.% concentration samples of MWCNT/water nanofluids have been prepared. SDS Surfactant was used to stabilize the MWCNT particles. Rheological behaviour of MWCNT/water nanofluids have been examined at different temperatures under the controlled shear stress of 0-35 Pa. Higher viscosity results were found for higher concentration nanofluids. For all nanofluids, decrease in viscosity was observed with the increasing temperature. Non-Newtonian nature of prepared nanofluid samples was observed, to analyse this rheological characteristic of MWCNT/water nanofluid, Power law model based curve fitting was employed, which was found as the best fitted model on experimental results. From this curve fitting variation in MWCNT/water flow behaviour from Newtonian to non-Newtonian was observed over the different concentration and temperature. SDS was also responsible for this unlike rheological behaviour of MWCNT/water nanofluid at different concentration. An optimal Artificial Neural Network (ANN) was also designed to predict the dynamic viscosity of MWCNT/water nanofluid. The model accounts for the effect of particle %weight concentration, temperature, shear time and shear stress. The model was trained on a dataset of present experimental work and shows very accurate results in predicting the viscosity (for the testing data, obtained R2 and RMSE are 0.9993 and 0.0035). The model compliments the experimental results very well.
DOI: 10.17632/69vjpx8xyg.2
title: Data for Rheological behaviour of high concentration MWCNT/Water Nanofluids

description: In this work, 0.3, 0.5, 1 and 1.5 wt.% concentration samples of MWCNT/water nanofluids have been prepared. Rheological behaviour of MWCNT/water nanofluids have been examined at different temperatures under the controlled shear stress of 0-35 Pa. Higher viscosity results were found for higher concentration nanofluids.
DOI: 10.17632/gsdsb9m5w4.1
title: Draft Genome Sequence of Cercospora brassicicola Henn., causing White Leaf Spot on Brassica species
DOI: 10.17632/pgdb3k9yv8.1
title: Consumer Confidence data

description: The uploaded dataset contains monthly time-series of the index numbers (base 2010) of the Italian current confidence indicator and the Italian future confidence indicator from January 2014 to March 2020. Each time series is composed of 75 values (72 valid data and 3 missing data for the time interval July 2017 – December 2017). The current confidence index measures the judgment of the present economic situation of Italy and families, current opportunity for saving and purchasing durable goods and family financial budget, on the other hand the future confidence index measures the expectation regarding the above-mentioned variables including unemployment. A joint assessment of both indexes can offer a very interesting interpretation referred to the optimistic (current index lower than the future one) or pessimistic (current index greater than the future one) state of the population. The source of these datasets is the official website of Italian Institute of Statistics (http://dati-congiuntura.istat.it/?lang=en&SubSessionId=c419585c-c3e5-4408-9423-14ce999a6dcd).
DOI: 10.7281/t1/1ecvb0/15nj2h
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-05_18_std.nc
DOI: 10.7281/t1/1ecvb0/1hqhs3
title: DustOff-monthly-average_wrfout_d01_2008-09_12_mean.nc
DOI: 10.7281/t1/1ecvb0/3jfc7b
title: DustOff-monthly-average_wrfout_d01_2008-07_06_mean.nc
DOI: 10.7281/t1/1ecvb0/9gvfvx
title: DustOff-monthly-average_wrfout_d01_2008-06_00_mean.nc
DOI: 10.7281/t1/1ecvb0/bjpm6c
title: DustOff-monthly-average_wrfout_d01_2008-03_00_mean.nc
DOI: 10.7281/t1/1ecvb0/cj90pa
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-07_00_std.nc
DOI: 10.7281/t1/1ecvb0/dcnjef
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-07_12_std.nc
DOI: 10.7281/t1/1ecvb0/dgmv7d
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-06_18_std.nc
DOI: 10.7281/t1/1ecvb0/fevnil
title: DustOff-monthly-average_wrfout_d01_2008-08_18_mean.nc
DOI: 10.7281/t1/1ecvb0/fo6tsy
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-05_06_std.nc
DOI: 10.7281/t1/1ecvb0/fqijvn
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-06_12_std.nc
DOI: 10.7281/t1/1ecvb0/g8molc
title: DustOff-monthly-average_wrfout_d01_2013-05_00_mean.nc
DOI: 10.7281/t1/1ecvb0/glvkpb
title: DustOff-monthly-average_wrfout_d01_2013-09_06_mean.nc
DOI: 10.7281/t1/1ecvb0/ljgnww
title: DustOff-monthly-average_wrfout_d01_2013-10_06_mean.nc
DOI: 10.7281/t1/1ecvb0/meotmy
title: DustOff-monthly-average_wrfout_d01_2013-07_12_mean.nc
DOI: 10.7281/t1/1ecvb0/osqk0o
title: DustOff-monthly-average_wrfout_d01_2008-06_18_mean.nc
DOI: 10.7281/t1/1ecvb0/otq0mx
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-03_18_std.nc
DOI: 10.7281/t1/1ecvb0/vah31u
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-03_06_std.nc
DOI: 10.7281/t1/1ecvb0/vnwhb3
title: DustOff-monthly-average_wrfout_d01_2008-10_12_mean.nc
DOI: 10.7281/t1/1ecvb0/vtzmwf
title: DustOff-monthly-average_wrfout_d01_2013-05_18_mean.nc
DOI: 10.7281/t1/1ecvb0/wkklsg
title: DustOff-monthly-average_wrfout_d01_2008-04_12_mean.nc
DOI: 10.7281/t1/1ecvb0/xkvhwm
title: DustOff-monthly-average_wrfout_d01_2013-08_18_mean.nc
DOI: 10.7281/t1/1ecvb0/yicxbq
title: DustOff-monthly-average_wrfout_d01_2013-04_06_mean.nc
DOI: 10.7281/t1/1ecvb0/z6fei4
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-06_00_std.nc
DOI: 10.7281/t1/1ecvb0/zzzhx1
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-06_06_std.nc
DOI: 10.17632/b842728hnj.1
title: A number in a product name: anchoring, anchor fluency, and willingness to pay

description: .sav file with the data collected in the point-of-sale field experiment described in the study.
DOI: 10.17632/fjn8sx57d9.3
title: Cornea donor in Anatomy Department

description: Corneae from Body Donors in Anatomy Department: Valuable Use for Clinical Transplantation and Experimental Research
DOI: 10.17632/k5y4g5nfwy.2
title: Data and R-code used for the analysis of the multidimensionality and magnitude of host manipulation by acanthocephalan parasites.

description: Data and R-script corresponding to the article of Fayard et al. "Magnitude and direction of parasite-induced phenotypic alterations: a meta-analysis in acanthocephalans"
DOI: 10.17632/pgdb3k9yv8.2
title: Consumer confidence time series 2014-2020

description: The uploaded dataset contains monthly time-series of the index numbers (base 2010) of the Italian current confidence indicator and the Italian future confidence indicator from January 2014 to March 2020. Each time series is composed of 75 values (72 valid data and 3 missing data for the time interval July 2017 – December 2017). The current confidence index measures the judgment of the present economic situation of Italy and families, current opportunity for saving and purchasing durable goods and family financial budget, on the other hand the future confidence index measures the expectation regarding the above-mentioned variables including unemployment. A joint assessment of both indexes can offer a very interesting interpretation referred to the optimistic (current index lower than the future one) or pessimistic (current index greater than the future one) state of the population. The source of these datasets is the official website of Italian Institute of Statistics (http://dati-congiuntura.istat.it/?lang=en&SubSessionId=c419585c-c3e5-4408-9423-14ce999a6dcd).
DOI: 10.17632/rnpj8ngn5z.1
title: The Tunisian exchange rate regime: Is it really floating?

description: Financial statistics on exchange rates et foreign reserves
DOI: 10.17632/v9gmmkyjsc.1
title: 5-Fluorouracil Induced Dysregulation of the Microbiome-Gut-Brain Axis Manifesting as Depressive Like Behaviors in Rats

description: supplementary data of PFC metabolis and gut microbiota
DOI: 10.7281/t1/1ecvb0/3szgvn
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-09_06_std.nc
DOI: 10.7281/t1/1ecvb0/4s8dvq
title: DustOff-monthly-average_wrfout_d01_2013-03_06_mean.nc
DOI: 10.7281/t1/1ecvb0/5vspcw
title: DustOff-monthly-average_wrfout_d01_2008-05_18_mean.nc
DOI: 10.7281/t1/1ecvb0/8g4x2c
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-07_18_std.nc
DOI: 10.7281/t1/1ecvb0/a6n8dv
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-03_12_std.nc
DOI: 10.7281/t1/1ecvb0/dp1ceo
title: DustOff-monthly-average_wrfout_d01_2008-10_06_mean.nc
DOI: 10.7281/t1/1ecvb0/fafa3m
title: DustOff-monthly-average_wrfout_d01_2013-07_00_mean.nc
DOI: 10.7281/t1/1ecvb0/fhduby
title: DustOff-monthly-average_wrfout_d01_2008-08_00_mean.nc
DOI: 10.7281/t1/1ecvb0/hyluon
title: DustOff-monthly-average_wrfout_d01_2008-04_06_mean.nc
DOI: 10.7281/t1/1ecvb0/jkajf4
title: DustOff-monthly-standard-deviation_wrfout_d01_2006-07_06_std.nc
DOI: 10.7281/t1/1ecvb0/jwt15i
title: DustOff-monthly-average_wrfout_d01_2013-06_06_mean.nc
DOI: 10.7281/t1/1ecvb0/mec5cf
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-03_00_std.nc
DOI: 10.7281/t1/1ecvb0/o29icr
title: DustOff-monthly-average_wrfout_d01_2008-09_06_mean.nc
DOI: 10.7281/t1/1ecvb0/rl7hda
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-05_12_std.nc
DOI: 10.7281/t1/1ecvb0/rthrbc
title: DustOff-monthly-average_wrfout_d01_2008-07_12_mean.nc
DOI: 10.7281/t1/1ecvb0/t8lpqf
title: DustOff-monthly-average_wrfout_d01_2008-08_12_mean.nc
DOI: 10.7281/t1/1ecvb0/ugvvl3
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-09_18_std.nc
DOI: 10.7281/t1/1ecvb0/ulb5ll
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-05_00_std.nc
DOI: 10.7281/t1/1ecvb0/ycj3cc
title: DustOff-monthly-average_wrfout_d01_2008-05_00_mean.nc
DOI: 10.7281/t1/1ecvb0/z1cxji
title: DustOff-monthly-average_wrfout_d01_2008-03_06_mean.nc
DOI: 10.17632/4vcjrj9mdm.2
title: UKIP Support in Local Elections: Which Factors Play a Role in Determining Electoral Fortunes?

description: UKIP Local Elections
DOI: 10.17632/hc793bs7m5.1
title: Mandla Subprovince late Cretaceous-early Paleocene paleosol geochemistry

description: Bulk geochemical data for paleosol profiles sampled in the Mandla Subprovince of central India, surrounding Nagpur. Collected in February-March 2017.
DOI: 10.17632/pcj44fyswg.1
title: Data on Mepraia spinolai individuals captured in Las Chinchillas National Reserve, Chile, using a human as a bait.
DOI: 10.5285/77d63a69e3554412a40c9a9ba564e3f9
title: SWIGS: Gorgon Magnetohydrodyamic Code Simulation Data: Magnetospheric and Ionospheric Conditions under Southward IMF for 0-90 degree Dipole Tilts

description: This dataset contains data outputs generated using the Gorgon Magnetohydrodynamic (MHD) code, for simulations of the steady-state magnetosphere-ionosphere system during southward interplanetary magnetic field (IMF) with dipole tilt angles from 0-90 degrees. This data were collected as part of the NERC project Space Weather Impacts on Ground-based Systems (SWIGS). The MHD equations were solved in the magnetosphere on a regular 3-D cartesian grid of resolution 0.5 RE (Earth radii), covering a domain of dimensions (-30,90) RE in X, (-40,40) RE in Y and (-40,40) RE in Z with an inner boundary at 4 RE. In this coordinate system the Sun lies in the negative X-direction, the Z axis is aligned to the dipole in the 0 degree tilt case (where positive tilt points the north magnetic pole towards the Sun), and Y completes the right-handed set. The ionospheric variables were calculated on a separate 2-D spherical grid of dimensions 66x128 in latitude and longitude (with the north pole at 90 degrees latitude and the sun at 180 degrees longitude), coupled to the magnetospheric domain at the inner boundary. Output data is timestamped in seconds and is defined at the centre of the grid cells. The simulation data corresponding to each dipole tilt are stored in separate directories 'XXdeg', e.g. in '00deg' for a 0 degree tilt angle. The data are stored in hdf5 format. The magnetospheric variables are stored in the files 'Gorgon_[YYYYMMDD]_[XX]deg_MS_params_[XXXXX]s.hdf5' where XX is the tilt angle in degrees and XXXXX is the simulation time in seconds. The magnetospheric data includes the magnetic field ('Bvec_c'), plasma bulk velocity ('vvec') and electric current density ('jvec') after 4h of simulation, as well as the magnetic field and velocity in 5 minute intervals for the preceding 30 minutes. The dataset for each magnetospheric variable is of shape (240,160,160,3) where the first 3 dimensions are the grid indices in (X,Y,Z) indexed from negative to positive, and the final dimension is the cartesian vector components in (i,j,k). Similarly, the ionospheric data are stored as 'Gorgon_[YYYYMMDD]_[XX]deg_IS_params_[XXXXX]s.hdf5', containing the field-aligned current ('FAC') and electric potential ('phi') after 4h of simulation, as well as the potential in 5 minute intervals for the preceding 30 minutes. The dataset for each ionospheric variable is of shape (66, 128) where the first dimension is the grid index in colatitude, indexed from the north towards the south (i.e. 0 to 180 degrees), and the second dimension is the grid index in longitude, indexed from midnight towards noon via dawn (i.e. 0 to 360 degrees).
DOI: 10.7281/t1/1ecvb0/2tw1lu
title: DustOff-monthly-average_wrfout_d01_2009-07_00_mean.nc
DOI: 10.7281/t1/1ecvb0/5hzfoi
title: DustOff-monthly-standard-deviation_wrfout_d01_2007-03_06_std.nc
DOI: 10.7281/t1/1ecvb0/5u5tx6
title: DustOff-monthly-average_wrfout_d01_2009-03_06_mean.nc
DOI: 10.7281/t1/1ecvb0/6yedyo
title: DustOff-monthly-average_wrfout_d01_2008-05_12_mean.nc
DOI: 10.7281/t1/1ecvb0/8xzwrk
title: DustOff-monthly-average_wrfout_d01_2009-06_06_mean.nc