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Transcription of interviews with the participants of the study, all merged together, arranged by pseudonyms of participants.
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The chemical, structural and optical properties of each fraction obtained from black mineral sand was characterized with thermal gravimetric analysis (TGA), Fourier transform infrared (FT-IR) and UV-Vis spectroscopies techniques. The thermogravimetric analysis (TGA) was carried out using TGA /DSC Star System Mettler Toledo analyzer, for observing the thermal behavior. Previously to TGA/DSC scans, the samples were degasified at 100°C during 60 min at 20 cm3 min-1 in nitrogen purge stream and the analysis were performed since 25°C to 1000°C at a heating rate of 5°C min-1 under 100 cm3 min-1 in flowing air (the TGA/DSC curves were normalized regarding the sample weight). The functional groups of the substrates adsorbed to the catalyst were identified with a FT-IR 8400S spectrophotometer (Shimadzu) at room temperature within the range of 4000-400 cm-1. The UV reflectance of the solid were analyzed with an UV/Vis spectrophotometer (Shimadzu UV-2600) with an integrating sphere for acquisition of the diffuse reflectance spectra (BaSO4 was used as reference). With this information, the Tauc plots were used to estimate the material band-gap energy (Eg), by extrapolation of the straight line from the absorption curve to the abscissa.
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The paper presents data used to calculate the effect of Bitcoin’s price volatility on altcoin pricing for the period from December 2018 to December 2019. Particularly, the analysis includes data on the price for the following cryptocurrencies: Bitcoin, Litecoin, Ethereum, Bitcoin Cash, Dash and Monero. The data covers a total of one year and includes results of the coefficient of averages indicator, which is calculated for each altcoin individually with the use of data on the average growth rates and average decline rates of cryptocurrencies in relation to Bitcoin prices. Table 1 presents data on the correlation values of the selected cryptocurrencies to each other. Figure 2 is based on the data from Table 2, with the exception of altcoins’ correlation values. Based on the calculated average growth rates and average rates of decline of Litecoin, Table 2 presents statistical values of the relationship between Litecoin prices and Bitcoin prices. The diagram presented if Figure 3 is a visual representation of the data obtained from Table 2. Table 3 offers the end-result of the conducted analysis, which includes the values of the average positive and average negative indicators, the maximum and minimum indicators, and coefficient of averages indicators for the sampled cryptocurrencies. Figure 4 is a visualization of the end-result data.
Data Types:
  • Tabular Data
  • Dataset
The development of railways in Italy (1839-1913). The geodatabase has been built by Carlo Ciccarelli (University of Rome Tor Vergata) and Peter Groote (University of Groningen). GIS dataset available from ArcGIS online; please click the link. Different formats available for download. https://www.arcgis.com/home/item.html?id=d4fe94faf2e54518b3f421f19a137d4c Use the legend key to see when railway lines were constructed, or to differentiate between standard gauge and narrow gauge or between primary and secondary railways. For more information, see our articles on the construction of the geodatabase: Carlo Ciccarelli & Peter Groote (2017), Railway Endowment in Italy’s Provinces, 1839-1913, Rivista di Storia Economica (ISSN 0393-3415), Fascicolo 1, april 2017; (https://www.rivisteweb.it/doi/10.1410/86763) Carlo Ciccarelli & Peter Groote (2017), The spread of railroads in Italian provinces: a GIS approach, Scienze Regionali - Italian Journal of Regional Science The layers 'lines conecting capitals' and 'junctions' are of no use in the map viewer of ArcGIS online. We advise to keep these switched off. They are added because they are necessary for the use of the network analyst in ArcGIS desktop. Consequently, they are also included in the downloadable versions of the geodatabase. If you are interested in using the underlying geodatabase in ArcGIS desktop 10.X, then download the map package that is available here, the file geodatabase here, or the shapefile (lacks topological characteristics!) here. If you find the data of use, please let us know at carlo.ciccarelli@uniroma2.it and p.d.groote@rug.nl. If you use the data in an article, please quote our articles in Rivista di Storia Economica and Rivista di Scienze Regionali.
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We present data concerning the clinical and sociodemographic characteristics of a sample of drug-naïve patients with a first episode of non-affective psychosis. The dataset was obtained from a 3-year longitudinal intervention program as part of an ongoing 10-year epidemiological study.
Data Types:
  • Software/Code
  • Dataset
The data is the patients who underwent chest wall perforator flaps for partial breast reconstruction after breast conservation surgery at Tata Medical Center between January 2017 and November 2019. There were total of forty patients. Data on clinicopathological characteristics, surgical details, adjuvant treatment and follow-up were retrieved from the institutional hospital management system and a prospectively maintained REDCap database. An acquired - informal questionnaire was developed and used to assess patient satisfaction at least six months after completion of radiotherapy, using a 4 point Likert’s scale. The questionnaire was administered either telephonically or during a routine follow up visit.
Data Types:
  • Tabular Data
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Olefin yield from from different hydrocarbon feed stock mostly naphtha.
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Analysis of the HR policies in MNCs (impact of culture, startegies to implement effcetive strategies, etc
Data Types:
  • Image
  • Dataset
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CEPII makes available a "square" gravity dataset, for the period 1948 to 2015, allowing the estimation of international flows as a function of GDP, population and trade costs.
Data Types:
  • Software/Code
  • Dataset
The original data mainly included environmental data of tomato seedling and florescence stage and plants images. The original code mainly includes the stochastic forest algorithm model building process.
Data Types:
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  • Image
  • Tabular Data
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