dataesr/scientific-paragraphs-categorization huggingface dataset:
But learning can take a long time  (Nivat, 1985)  depending on the level of mastery targeted. Writing stories and tales, even with automated processes, reports requires high writing skills that can to some extent dampen as "the productions that result from it are rarely entirely new"  (Ward, 1995). We have implemented in Docyrus an autowriting module which can generate sentences from key words to help the learner write phrases of its story. Some of those sentences have words that need to be granted in gender and number to advance learners in their spelling. Docyrus detects and saves errors relying on its learning analytics module (Leijten, M. ,  Van Horenbeeck, E. , & Van Waes, L. , 2015)  and offers the student to correct them.
Results 2719 actions were recorded, each one consisting of multiple logs. We used Spearman's Rho since some variables do not follow a normal distribution. The results (table 1) analysed in SPSS show a correlation between the learner's knowledge of possible interface customization actions and the realization of the learning activity on the ILE. Correlation is significant at the 0. 05 level (2-tailed). In addition, a differentiation of the results by gender (table  2  and table 3 ) can be established as many studies based on the UTAUT model had previously established. Indeed, the significance of the correlation is greater in the male (p < 0. 01) gender than the female gender (p < 0. 05). Correlation is significant at the 0. 01 level (2-tailed). Also, text formatting actions (V3_Text_formating) can be correlated with knowledge of the possibilities (V1_Show_GUI_Form) for customizing the interface (table 4) (p < 0.
Discussion The results correlate the visualization of possible interface customization options with navigation in the software. The gender variable is a moderating variable as in the UTAUT model. But we did not find any correlation with the age variable. Likewise, a correlation could not be established between the interface customization actions and the navigation. Similarly, the passage to the act of personalizing the interface after consulting the options proposed in the personalization screen, is not established. With reference to  Sperandio's (2008)  work on systems ergonomics, we understand this refusal to personalize as the learner's fear of "losing control of his actions" on the ILE. The correlation between the text formatting actions and the consultation of the interface personalization screen which both divert the learner from his main editorial activity can be compared to the Hélices model  (Linard, 2001).
The dearth of Information Communication Technology (ICT) infrastructure in the Sub-Saharan Africa region underscores the argument that the spread of broadband infrastructure can foster internet adoption in the region. Consequently, the aim of this paper is to present results on the determinants of internet adoption in a sub-Saharan African country. Drawing on a dataset of households in Nigeria, this study presents findings on the demographic, socioeconomic and infrastructure factors that predict internet usage in Nigeria. The novelty of our analysis stems from a unique dataset constructed by matching georeferenced information from an inventory of network equipment to a nationally representative street-level survey of over 20,000 Nigerians, by far one of the largest technology adoption surveys in sub-Saharan Africa to date within the information systems literature.
While previous studies have shed light on the regional variation in internet adoption by focusing on the determinants of internet penetration in the context of the "digital divide" (e. g. Oyelaran-Oyeyinka and Lal, 2005; Chinn and Fairlie, 2010), these studies often employ infrastructure measures/proxies (such as fixed telephone lines per sq. km, main telephone lines per capita, etc. ,) thereby overlooking the peculiar nature of broadband infrastructure in the SSA region. More specifically, we note that internet use across the SSA region is underta ken mainly via wireless broadband access, rather than fixed-line broadband. Thus, in this paper, we draw on a unique dataset that combines geo-referenced (longitude and latitude) information on 3G and 4G wireless network equipment to examine factors that predict of internet usage in Nigeriaa sub-Saharan country.
Finally, technology adoption decisions are often shaped by religious reasons, as some religious beliefs may restrict the adoption of conventional technological products  (Fungáčová, et al. , 2017). Hence, we control for the religious beliefs of each sampled individual. To examine the effect of broadband infrastructure on internet adoption, used a measure that captures individual-level access to wireless broadband network. Hence, we employ a microspatial variable that captures the prevalence of wireless network infrastructure at the individual level. We calculate this variable using information from our two data sources in four steps. First, inspired by Hodler and Raschky (2017), we extract and map the 3G and 4G cell tower locations from OpenCellID (see figure B in the appendix section). Secondly, we extract and map the street-level dwelling locations from the survey data.
Data Collection To investigate the effect of wireless connectivity on internet usage, we rely on a unique nationally representative market survey of Nigeria carried out by Africa's largest mobile operator, MTN 2 during April-July 2018. The MTN survey, which covers 730 localities (i. e. , villages or towns), is by far and away one of the largest and most comprehensive technology surveys in sub-Saharan Africa to date. The data was collected via the use of paper questionnaire distributed to respondents in all 36 states of the country. Figure A in the appendix section plots the centroid GPS coordinates of surveyed areas at the municipality level. The wide geographical spread of the survey areas confirms the nationally representative nature of the survey. A total number of 21,844 observations were obtained for the final analysis.
Our second data resource is the OpenCellID database which contains information on the micro-spatial independent variable (towers). The database contains raw information on the geo-location (longitude and latitude) of around seven million unique cell sites across the world (Hodler and Raschky, 2017). One key benefit of the OpenCellID database is the possibility to identify the technology (radio) type for each telecommunications tower (i. e. GSM, UMTS, LTE, etc. ). This allowed us to identify the two wireless network classes: "UMTS" (third -generation technology, 3G) and the more advanced "LTE" (fourth -generation technology, 4G) types. Specifically, we identified a total of 13246 u nique tower locations from the OpenCellID data consisting of 11470 3G and 1776 4G sites. These sites are geo-coded at the GPS (longitude and latitude) -level (see figure B in the appendix).
Measures Our main dependent variable is represented by a broadband usage variable for the use of broadband services. To construct this indicator variable, we convert survey responses on broadband subscription using the question: "Which of the following telecommunication services 3 do you use nowadays?". We then calculate the dependent indicator variable as a dummy that takes the value "1" if "Data service (Accessing internet)" was selected in response to the question. Otherwise, a dummy value of "0" was assigned to the observation. Table  1  provides the summary statistics of the variables employed in this study. The internet adoption rate within our dataset is 12%, falling firmly within the same ballpark as the 13. 7% and 15. 2% broadband penetration rates reported by 1 See https://www. stata.
Hence, we would argue that the failure to control for this network infrastructure effect in the study of broadband adoption across developing countries could well result in significant omitted variable bia s. Furthermore, the network infrastructure effect may also explain the nuances embodied in the varied adoption of broadband services across different regions, given that the quality of service (QoS) and user experience may well depend on the diffusion and reliability of th e underlying network infrastructure. Although the first-stage results presented in this paper revealed preliminary drivers of internet adoption, they also provide interesting avenues for further study which we aim to explore in subsequent analysis of the dataset. In the first place, the significant urban variable shows that there are likely to be regional differences with regards to the factors that influence internet adoption in Nigeria.
Volcanic activity is always accompanied by the transfer of heat from the Earth's crust to the atmosphere. This heat can be measured from space and its measurement is a very useful tool for detecting volcanic activity on a global scale. MIROVA (Middle Infrared Observation of Volcanic Activity) is an automatic volcano hot spot detection system, based on the analysis of MODIS data (Moderate Resolution Imaging Spectroradiometer). The system is able to detect, locate and quantify thermal anomalies in near real-time, by providing, on a dedicated website (www. mirovaweb. it), infrared images and thermal flux time-series on over 200 volcanoes worldwide. Thanks to its simple interface and intuitive representation of the data, MIROVA is currently used by several volcano observatories for daily monitoring activities and reporting.
, 2019) , demonstrated the potential of integrating these space-based data for forecasting eruptions  (Furtney et al. , 2018) , stressing the need to develop a volcanic monitoring system to support volcano observatories  (Pritchard et al. , unpublished). The Monitoring Unrest From Space (MOUNTS) project 1  , although in an embryonic stage, can be considered a first prototype of such integrated system, since it includes near-real time multi-parametric analysis (UV, IR and microwaves) derived from the ESA Sentinel constellation, at several volcanoes  (Valade et al. , 2019). However, apart from this example, a comprehensive integration of space-based datasets into an operational system for global volcano monitoring is at this time only envisioned, with several distinct groups working on a single RSO (deformation, degassing, ash, thermal;  Reath et al.
Architecture of the System Middle Infrared Observations of Volcanic Activity is an automatic system for detecting thermal anomalies at high temperatures (>500K), based on the analysis of MODIS (Moderate Resolution Imaging Spectroradiometer) data. MODIS is a sensor mounted on board two NASA satellites, called Terra and Aqua, in sun-synchronous polar orbit since March 2000 and May 2002, respectively. The main features of MODIS, useful for volcano thermal monitoring, consist of its global coverage with spatial resolution of 1 km, temporal resolution of about four images/day (at the equator) and the presence of a dual channel in the mid-infrared (centered at 3. 959 microm) with low/high gain settings (providing an extended range of unsaturated data). The general architecture of the MIROVA system can be divided into two main parts: (a) data download and processing (developed on a local computer) and (b) data dissemination (developed on a website: www.
These targets were gradually added to the MIROVA list based on a scientific interest, or following specific requests from the observatories. They now cover the most active volcanoes on the globe  (Global Volcanism Program, 2013)  with the current number actually limited by the hardware resources of the system and by the amount of data to be downloaded and processed daily (about 25 Gb per day). Despite this limitation, this architecture makes it possible to add other specific targets in case of need (i. e. , unrest of a new volcano) without compromising the total system efficiency in terms of processing time, memory, storage capacity, etc. Access to archived MODIS data (available since 2000 and 2002 for Terra and Aqua, respectively) also allows analysis and reconstruction of 20-year time series for any volcano.
Download, Data Processing, and Sensibility The original MODIS data (Level 1b -calibrated radiances) are downloaded from the LANCE system, which provides them with a latency of less than 3 h 2. The continuous screening of the LANCE daily remote folders (every 5 min), allows timely downloading in the local computer of any newly acquired MODIS granule imaging at least one target volcano. The downloaded granules are then processed following several steps fully described in  Coppola et al. (2016a). For each analyzed volcano, the original spectral radiance data (recorded by MODIS in the Middle Infrared [MIR] at 3. 959 µm and Thermal Infrared [TIR] at 12. 02 µm) are resampled in regular grids of 50 × 50 km (in UTM coordinates) and processed in order to identify the pixels containing thermal anomalies.
Website and Data Dissemination In order to easily and quickly share the real-time observations of the system, a dedicated website 3 summarizes, and continuously updates, a series of tables and graphical outputs for any monitored volcano. The home page (Figure  1 ) of this website is constituted by a world map showing all the monitored volcanoes, and by a table summarizing the latest thermal anomalies detected by the system (in chronological order). In order to quickly identify the "thermal magnitude" of the hotspot listed in this table, we represent each detection through a color scale, proportional to the logarithm of the detected VRP. This scale is divided into five distinct levels and each thermal detection is classified into these subdivisions, based on the recorded VRP (Figure  1 ). The interactive map and table take the user to the page dedicated to each individual volcano, where specific graphical outputs are updated approximately four times per day (according to the number of MODIS overpasses) and available online from 1 to 4 h after image acquisition.
The original images (i. e. , 50 × 50 km maps) and time series (numerical tables) are not archived on a web database and cannot be query and downloaded. Although this limitation prevents any direct data download, the management of the complete MIROVA database (that currently include a total of more than 2 million images) would require a large bandwidth and greatly slow down the use of the website. Data sharing is currently done by request through email by the observatories. This practice, although slow and impractical from an operational point of view, allows a continuous exchange of information and discussions between the system developers and the end users, not necessarily experts in the interpretation of remote-sensed thermal data. In recent years, and thanks to its easy application on diverse volcanic environments, the MIROVA system has gradually turned into a small pilot project testing the capabilities for global volcano monitoring, with the observations now covering the full range of volcanic activities in near real time at 216 volcanoes  (Coppola et al.
State of the Art of Operational Thermal Remote Sensing Systems Currently Used by Volcano Observatories None of the participating observatories has its own automatic detection/quantification hot spot system, but they rely on systems developed and maintained by external groups. Although there are many thermal remote sensing data available online (see Supplementary Table  S1 ), most of them are not operational in near real time and in the area of interest. The most used remote sensing thermal monitoring systems are those based on moderate resolution sensors, such as MODIS data (MIROVA, MODVOLC, REALVOLC) or VIIRS (FIRMS), which provide approximately 2/4 images daily, at a resolution of 1 km. Some observatories also use automatic systems based on higher sampling rate data (e. g. , HOTVOLC, HOTSAT based on SEVIRI data), which are particularly useful for tracking volcanic plumes or for detecting high-radiating effusive eruptions.
Others use the available ASTER data (i. e. , ASTER Image database for Volcanoes), based on TIR observations, which enable to detect low-level thermal activity (such as fumarole fields) but with longer revisit time (e. g. , >15 days;  Reath et al. , 2019a). The lack of a near-real time data dissemination system makes this resource less used for monitoring purposes despite the fact that since 2011 the ASTER Urgent Request Protocol (URP) system has allowed the continuous increase of acquisitions of new volcanic activity as quickly as possible  (Ramsey, 2016). Due to the lack of operational monitoring systems to analyze high-spatial resolution Short-Wave Infrared (SWIR) images, no observatory uses automatic systems focused on Sentinel 2 or Landsat 8, which have a typical revisit time of 5 to 16 days (see Supplementary Table  S1 ).
Frequency of Use of MIROVA Website and Volcano Alert Levels The experiences reported by the observatories during the past 5 years allow us to state that the frequency of use of a satellite thermal monitoring system such as MIROVA strongly depends on the specific alert level of each monitored volcano. Many alert level systems exist in the world, with the majority based on four levels, e. g. , the US system developed by the USGS (Normal, Advisory, Watch, and Warning). Each country (or volcano) has a specific scale which is adapted to the particular volcanological, environmental, social, political and economic context  (Fearnley et al. , 2012). For each level there is therefore a different need/urgency to have up-to-date data on the thermal state of the volcano which can be summarized as follows:.
Warning Hazardous eruption is imminent, underway, or suspected. During imminent eruptions or ongoing eruptions, the thermal data are checked daily or hourly during crisis (e. g. , Bardarbunga, Iceland [August 2014]; Agung, Indonesia [August 2017]; Kilauea, United States [May 2018]). In these warning conditions, each dataset must be more frequently updated. A screen grab of the MIROVA website is often displayed in the monitoring acquisition rooms during eruptive crisis (e. g. , Nevado del Ruiz, Colombia; Manam, Papua New Guinea).
Intensity The intensity of the thermal anomaly, referred to as VRP, is the fundamental parameter of MIROVA and constitutes the main added value compared to other systems able to detect the presence/absence of hotspots, without quantifying their intensity in terms of radiated energy. Besides being a direct measurement of radiated thermal energy, the VRP can be used to calculate the area or the temperature of the lava body using Stephan-Boltzmann law (see details in section "Download, Data Processing, and Sensibility"), if at least one of the two variables can be estimated (or assumed) independently. The area or temperature of a lava dome is a parameter that can be easily understandable even by non-experts, and provides a clear idea of the size of the phenomenon underway. Moreover, during effusive eruptions, the VRP can be used to calculate the Time Averaged lava Discharge Rate (TADR) and the volume of erupted lava, subject to a calibration of the conversion factors or a basic knowledge of the type of erupted lava  (Coppola et al.
Eruptive Evolution, Trends, and Patterns In conjunction with seismic, gas geochemistry, visual change and deformation data, MIROVA provides an initial check for level and type of activity at volcanoes, e. g. , the occurrence of lava flows, domes or simply hot, degassing open vents. Being able to track relative changes in thermal output (i. e. , VRP) as a time series, provides a useful assessment of any changes in activity, although a lack of thermal data does not necessarily means a volcano is cooling off (it can be due to cloud or thick ash cloud cover). Stationary, waning or intensifying trends obviously have different implications on the evaluation of the hazards and can be used (modeled) to define future eruptive scenarios, and possibly to make forecasts. At Piton de la Fournaise (La Réunion Island), the knowledge of the effusion rate trend (derived from thermal data), in combination with previously generated models of the likely lava flow path, has been useful to predict whether the flow will tend to lengthen or shorten, and whether the lava flows will reach the sea, cutting the national road that goes around La Réunion Island  (Harris et al.
, 2017 (Harris et al. , , 2019)). Moreover, by comparing this variable with the gas flux and deformation rate, it is possible to analyze the balance of the erupted/degassed material and eventually to quantify magmatic intrusions or endogenous growth  (Coppola et al. , 2017 (Coppola et al. , , 2019a)). During the Holuhraun (Iceland) eruption 2014-2015, MIROVA was regularly checked to monitor the level of activity at the eruption site  (Barsotti et al. , 2019) , to estimate of lava discharge rate and, eventually, to declare the end of the eruption based on the observed eruptive trend  (Coppola et al. , 2017). In the case of Nevado del Ruiz (Colombia), MIROVA's thermal anomaly data matched very well with shallow seismicity increments, ash emissions (seismically associated to volcanic tremor), large SO 2 fluxes, tiltmeters inflationary trends, etc.
How Thermal Data Are Used The use of the observations/images/data obtained from MIROVA is highly varied and depends fundamentally on four factors: (i) the presence and (quality) of a ground-based monitoring network; (ii) the accessibility of the volcano; (iii) the alert level; and (iv) the type of activity in progress. Remote-sensed thermal observations clearly provide invaluable information for volcanoes not monitored in other ways, and may represent the only evidence of ongoing volcanic activity  (Coppola et al. , 2016a). These data may provide fundamental indications on the evolution of the unrest/eruption (e. g. , stationary, increasing, decreasing), although incomplete. When an eruption begins, remote sensing methods allow the surveillance of volcanic phenomena, and help minimizing the risks associated with direct exposure of observers to the activity.
Often, it is very difficult, impossible, or too expensive to make frequent observations of the volcanoes' summit, as well as to measure directly lava flows, domes or fumarole temperatures. It is usually impossible to conduct frequent airborne observations within the crater, so MIROVA data are useful to get a longer time series for routine monitoring, to verify reports, rumors, or provide observations for internal and wider discussions before making a decision to assess the level of activity at the volcano. At basaltic volcanoes equipped with a large ground-based monitoring network, such as Stromboli, Etna, Piton de la Fournaise, etc. , MIROVA is commonly used to estimate timeaveraged lava discharge rates (TADR) and erupted volumes in near real time  (Coppola et al. , 2019a; Laiolo et al. , 2019) , to define imminent eruptive scenarios  (Ripepe et al.
, 2017)  and to model the path and length of lava flows  (Harris et al. , 2017 (Harris et al. , , 2019)). In summary, the processed data provided by MIROVA are used by the observatories in different ways, but essential for monitoring activities, internal discussions on the volcano's activity level and for real-time calculations of the erupted volume and eruptive trend. This information actually converges to activity reports (daily, weekly, monthly or extraordinary reports), in which the thermal data are integrated with other monitoring parameters in order to evaluate the state of activity and to maintain situational awareness. The Volcano Disaster Assistance Program (VDAP) also includes the use of the website at trainings offered at volcano observatories where they are just starting to use remote sensing as part of their volcano monitoring.