dataesr/scientific-paragraphs-categorization huggingface dataset:
This study uses an intraday dataset that covers the pandemic-led economic crisis to April 24, 2020. Following stimulus packages across the globe in the mid-to late March, financial market characteristics have changed significantly; in particular, financial assets have moved concurrently since then. Hence, empirical studies are needed to examine whether gold has performed as a safe-haven asset during different phases of the pandemic. Moreover, most studies on the financial impacts of COVID-19 have used daily datasets.
Hence, Phase I spans  December 31, 2019 , to March 16, 2020 , and Phase II spans March 17, 2020 , to April 24, 2020. To confirm whether gold served as a safe haven or a hedge during the COVID-19 crisis, we examine major financial asset classes, namely, contracts for differences on equity (i. e. , S&P 500, Euro Stoxx 50, Nikkei 225, and China FTSE A50 indices), WTI crude oil, and spot exchange rates (i. e. , EURUSD, USDJPY, and USDCNY), for constructing portfolios with gold using hourly returns. Data are obtained from Dukascopy Bank SA, a Swiss forex bank and Electronic Communications Network broker. We begin with 14,494 hourly returns covering the sample period, from which we exclude 718 h with no transactions for all financial assets. The final sample comprises 13,776 hourly observations.
Fishmeal production Based on the secondary data obtained from West African (e. g. , Mauritania) and global literature  [41, [62] [63] [64] [65] [66] [67] [68] , as Gambia-based Chinese-owned fishmeal plants failed to provide data. Technical features and efficiencies of plants were considered at a level between Peruvian fair average quality and residual plants, with a fish-to-fishmeal conversion ratio of 4. 5, and featuring direct drying fuelled by R500 residual fuel. The total amount of processed fish was computed based on the fish-to-fishmeal conversion ratio and export data, as all fishmeal is exported, and all exports are authorised by the Gambian Food Safety and Quality Authority (FSQA), which provided disaggregated data.
Contribution Analyses The relative contribution of each element of the system to the estimated impacts is presented in a contribution analysis of the Gambian fish products (Figure  3 ). For most fishing activities, fuel consumption contributes to >90% of impacts (Figure  3a ). The impacts of fish processing are determined mainly by the impacts of the supplying fisheries, except for shellfish processing, for which the main driver for the impacts is the combustion of wood as fuel (Figure  3b ). In Malian fisheries, as in the Gambia fisheries, fuel use is the main contributor to the estimated impacts when canoes are motorised (Figure  4a ). Fuel is the source of 67-98% of the potential impacts, depending on the impact category. The motorisation rate is rapidly increasing. For the analysis, data from field surveys were used.
Fuel is the source of 67-98% of the potential impacts, depending on the impact category. The motorisation rate is rapidly increasing. For the analysis, data from field surveys were used. The average amount of fuel used was estimated according to the proportion of motorised canoes per type of fishing unit. The canoe fleets used by part-time and full-time fishers feature the motorisation of ~50% (one out of every two fishing units is motorised), while diversified fishers attain 10% and opportunistic fishers 0%, as they tend to use shore gear. Fuel consumption was multiplied by the motorisation rate, because the catch levels vary little with or without motorisation, as the limiting factor seems to be the equipment and the potential of the fishing areas. It was difficult to estimate a motorisation rate representative of all fishing areas, because according to the interviews, between 10% and 100% of canoes are motorised, whereas, according to the West African Economic and Monetary Union  [12] , only 10% of fishing units are motorised.
Sensitivity and Variability In the LCA and impact assessment in general, the identification of sources of uncertainty are an important step to validate the results. In artisanal fisheries, the FUI (which depends on both the average landings per vessel and its associated fuel consumption) is the main driver of uncertainty. In artisanal fish and shellfish processing, their processed volumes, yields, losses and fuel consumption are a key source of uncertainty, as actors seldom keep records, and thus, all data were obtained from recalls. Regarding industrial fisheries and fish processing, their historical and detailed data is not easily accessible, as the companies tend to be very secretive about it.
Sensitivity and Variability In the LCA and impact assessment in general, the identification of sources of uncertainty are an important step to validate the results. In artisanal fisheries, the FUI (which depends on both the average landings per vessel and its associated fuel consumption) is the main driver of uncertainty. In artisanal fish and shellfish processing, their processed volumes, yields, losses and fuel consumption are a key source of uncertainty, as actors seldom keep records, and thus, all data were obtained from recalls. Regarding industrial fisheries and fish processing, their historical and detailed data is not easily accessible, as the companies tend to be very secretive about it. A large variability in fishery performances may be assumed, due to the seasonal and interannual dynamics of the targeted stocks, as well as the variations in fishing skills (including the so-called skipper effect  [48] , which influences the FUI and, thus, fuel use efficiency).
Acknowledgments: The authors acknowledge the contribution of various institutions and individuals to this work, namely theVCA4D team members involved in the  Gambia-Moustapha Dème (ISRA) ,  Adama Mbaye  (ISRA) and  Joseph Ndenn-and Mali-Ludovic Andres (IRSTOM) ,  Marc Oswald  (IRSTOM),  Gian Nicolay  (FiBL) and  Henri Zerbo -projects; the project management unit in Brussels; the  European Delegation in the Gambia and Mali  and the national stakeholders and institutions who kindly gave us their time and provided data.
Reliable and timely information on socio-economic status and divides is critical to social and economic research and policing. Novel data sources from mobile communication platforms have enabled new cost-effective approaches and models to investigate social disparity, but their lack of interpretability, accuracy or scale has limited their relevance to date. We investigate the divide in digital mobile service usage with a large dataset of 3. 7 billion time-stamped and georeferenced mobile traffic records in a major European country, and find profound geographical unevenness in mobile service usage-especially on news, e-mail, social media consumption and audio/video streaming. We relate such diversity with income, educational attainment and inequality, and reveal how low-income or low-education areas are more likely to engage in video streaming or social media and less in news consumption, information searching, e-mail or audio streaming.
Nevertheless, the limited scale and small granularity of existing studies do not allow a conclusive opinion to be formed on the magnitude of such a mobile application usage gap nor do they allow its repercussions on SES features to be understood. In this paper, we present the first large-scale, quantitative study of the relationship between mobile service adoption and socio-economic inequality. To that end, we analyse nationwide data traffic measurements collected by the leading mobile operator in a major European country (France), and find fundamental imbalances in the relative usage of specific mobile applications by different income or education groups during particular time periods. More precisely, we focus on a time frame where individuals are most likely to be in their residential areas, which favours the matching of mobile phone usage with demographic data.
Results Our data consist of around 3. 7 billion time-stamped and georeferenced records of the mobile traffic generated by different applications, such as YouTube, Facebook or Netflix-including device-specific ones such as Apple Store (run by iOS devices) or Google Play (run by Android devices). The data were collected between May and June 2017 over the whole of France, and aggregated at the base station (BS) level. Because of their volume and scattered nature, some traffic from different applications were aggregated to common categories such as mail, gaming, news consumption (mainly newspapers outlets) or audio streaming (see electronic supplementary material, text and table  S1 ). We merge the per-service traffic volume recorded in each BS coverage area with socio-economic indicators gathered from the 2014-2015 census, which include information about the income and population structure in each IRIS zone, i.
e. the French sub-municipal statistical unit (see Methods). The combination of the two datasets is performed via an areal interpolation that maps mobile traffic over BS coverage areas into IRIS zones (figure  1 ). Since our traffic data are collected by BS, they include app usage by residents of that area and users from other areas that visit that BS throughout the day. To link traffic data to the residents of a particular statistical area, we implemented a temporal consolidation of our data in which we only consider the mobile service usage recorded during the hours in which we can safely consider people to be at home, i. e. between 20. 00 and 7. 00 during weekdays (see Methods and electronic supplementary material, text). The various mobile applications inherently entail very different traffic volumes: for instance, YouTube video streaming sessions consume much more data than Twitter messages.
The large size effects identified for traffic variables suggest a deep usage royalsocietypublishing. org/journal/rsif J. R. Soc. Interface 18: 20210350 gap of mobile phone data between different areas of income and educational attainment. On the other hand, traffic variables tend to have a different role when local inequality is concerned: as an example, audio and video streaming have reverse correlations (i. e. negative and positive, respectively) with this SES response. Especially good predictors of inequality are the adoption of iOS (i. e. Apple Store) devices in areas where income disparity is higher, and Android (i. e. Google Play) devices where the economic status of the population is instead more homogeneous. Putting together our results in figure  4 , we can generally say that high-income and high-education areas have relatively more traffic on information-seeking tools (news, Google, mail), e-commerce and audio streaming, while areas with low SES indicators have higher relative traffic on social media activity and streaming (Facebook, YouTube, Snapchat).
Mobile service traffic data The network traffic dataset employed by our study comprises usage statistics of popular mobile applications. Data entries are recorded as the uplink (data transmitted by the user device) and downlink (data flowing to the user device) byte counts per service, at a temporal granularity of 5 min and aggregated by BS. The data were collected by Orange France within its own infrastructure during 1. 5 months in May and June 2017. They describe the mobile behaviour of the whole Orange subscriber base in France, i. e. approximately 15 million individuals distributed over more than 550 000 km 2 and served by over 25 000 BS. Usage statistics were collected by passive probes monitoring user sessions; the specific mobile service associated with each session was detected using deep packet inspection (DPI) and fingerprinting techniques tailored to specific traffic types (see electronic supplementary material, SI appendix for further details).
Geographical data and socio-economic indicators We used geographical information and census data from the French Institut national de l'information géographique et forestière (IGN), which are publicly available in their web pages. For the geographical description, we downloaded the Contours IRIS édition 2016 dataset, which defines a polygon in a Lambert-93 projection for each IRIS zone (i. e. aggregated unit for statistical information) in France, as well as an associated record containing the IRIS code, name and type among other information. For the population structure, we downloaded the Population en 2015 dataset, which contains a description of the population structure by age group and other factors, such as socio-professional category and immigration. For the economic indicators, we downloaded the Revenus, pauvreté et niveau de vie en 2014 (IRIS) dataset, which contains a complete description of the income distribution deciles for residential IRIS zones.
Areal consolidation The coverage area of each BS in the Orange mobile network is modelled via a Voronoi tessellation that uses the BS location as the object positions on the geographical space. Such BS coverage areas have a different geometry from the IRIS zones for which income and population data are available; generally, coverage areas are much smaller than IRIS zones in urban centres, but the opposite occurs in the countryside and less populated regions of the country. To spatially consolidate the data, we adopted an areal-weighted interpolation procedure to transfer BS-level traffic counts into IRIS zones. As exemplified in figure  1 , the principle is computing the intersection between the two spatial bases, and then creating a many-to-one mapping of BS coverage subareas to IRIS zones (i.
e. determining which IRIS zones each BS coverage area intersects with) plus a set of associated areal weights (i. e. the surface fraction of original BS coverage area that falls into each BS sub-area). By assuming that mobile service traffic is evenly distributed within the BS coverage area, traffic counts for each BS sub-area are calculated as the areal weight multiplied by the total traffic recorded for the BS, for each service. Finally, the traffic counts for all relevant BS sub-areas are aggregated for each IRIS zone. After filtering out IRIS zones without economic indicators, we have mobile service traffic data for 11 806 IRIS zones (out of 49 404), which encompass all the main urban areas of France as depicted in electronic supplementary material, figure S2. Classified by their degree of urbanization (according to Eurostat), we find that 78% of the IRIS zones in the final dataset correspond to urban areas, 19% are peri-urban areas and 3% are rural areas.
Temporal consolidation A mismatch between traffic and socio-economic datasets exists also in the temporal dimension, because of the inherent mobile nature of the consumption of applications on portable devices as opposed to the static character of census indicators. We resolve the discrepancy by only considering the mobile service usage that is most likely to be produced by users when they are at their locations of residence-which their socio-economic indicators also refer to. More precisely, we filter out weekends and French holidays (25 May and 5 June in the period considered), royalsocietypublishing. org/journal/rsif J. R. Soc. Interface 18: 20210350 and we keep observations during home hours (from 20. 00 to 7. 00) on weekdays. There is evidence that app usage peaks from 20. 00  [34] , and that online consumption is more or less homogeneous throughout the day  [35].
Regression models We consider two socio-economic indicators in IRIS zones that are available in the public datasets, i. e. the median income and the ratio of people with a professional activity that requires higher education, or higher education ratio for short; in addition, we consider a third inequality indicator in the form of the Gini index computed from the income data (see the electronic supplementary material, SI appendix for further details). We model the dependency of the indicators on mobile service usage via a generalized linear model g(E½y i ) ¼ a 0 þ X j b j Á RCA ij þ X k g k Á POP k þ d Á SP err , ð4: 2Þ where y i ; fincome, education, inequalityg for zone i is modelled after the RCA ij values for each application j. POP k are control variables from the population structure, i.
Ethics. The data from the Orange network probes used in this work were collected as part of the ABCD-Adaptive Behavior and Cloud Distribution collaborative research project founded by the French National Research Agency (ANR). The collection of these personal data was authorized by the Data Protection Officer (DPO) of Orange according to Article 89 of the European Union's General Data Protection Regulation (GDPR), which provides an exemption for research, in particular for scientific and research purposes. The data were collected and processed exclusively on the Orange Labs secure Big Data platform. The data were processed in a server located in the operator premises, and accessible only to authorized researchers, and aggregated at the BS level so as to remove all privacy risk for individuals.
Funding. This work has been supported by the research project  CANCAN (Content and Context based Adaptation in Mobile Networks ), grant no. ANR-18-CE25-0011 , funded by the  French National Research Agency (ANR). The work of M. F. was partially supported by the  Atracción de Talento Investigador  grant no. 2019-T1/TIC-16037  NetSense, funded by  Comunidad de Madrid. E. M. and I. U. acknowledge partial support by  Ministerio de Economía, Industria y Competitividad, Gobierno de España , grant nos. FIS2016-78904-C3-3-P  and  PID2019-106811GB-C32.
Data collection We designed a multidimensional self-completion questionnaire (piloted with a group of 60 older individuals) for this study. There were two versions: one for individuals who agreed to participate in the MAPT trial, and one for those who declined. Both versions contained identical questions about socio-demographics, personal and family history of memory problems/AD, and psychosocial elements: perceived social support (4 items), emotional stability (6 anxiety items from the NEO Personality Inventory-Revised), health locus of control (18 items), and perceived risk of memory disorders/AD (4 items developed for this questionnaire, based on the literature). References used to construct the different elements of the questionnaire can be found in Additional file 1: Appendix 1. Reasons for participation or non-participation (depending on whether or not the respondent had agreed to participate in the MAPT trial) were also explored using items based on the literature (see Additional file 1: Appendix 1 for references), eight individual semistructured interviews conducted with older people, half of whom were participating in a dementia prevention trial at the time, and a focus group.
b Reasons for refusing: percentage of nonparticipants who declared that they completely agreed that this was a reason for not participating in the MAPT trial (N = 317) individuals recruited by a doctor. Indeed, a preliminary analysis suggested that MAPT participants recruited by a doctor were older and had poorer cognitive, physical, and functional status than those who were recruited via other methods (data not shown). Nonetheless, individuals who responded to media advertisements about the trial or attended conferences where the trial was presented, for example, had to be more proactive about participating, and probably only did so if they had a sufficiently high enough level of interest in participating in the first place. A better understanding of the characteristics of prevention trial participants recruited via different methods could provide insight into how best to reach specific subpopulations of individuals, which may be of particular use for more tailored approaches to dementia prevention.
Acknowledgements We thank  Sophie Gillette  (PhD) and  Isabelle Carrié  (PhD) coordinators of the MAPT study for help with data collection for the ACCEPT study. The members of the Accept Study Group are as follows: principle investigator:  Sandrine Andrieu (MD, PhD ); epidemiology and public health team:  Nicola Coley  (PhD);  Virginie Gardette (MD, PhD ),  Alain Grand (MD, PhD ); psychology team:  Valérie Igier  (PhD),  Delphine Coniasse Brioude  (PhD),  Maria Teresa Munoz Sastre  (PhD); sociology team:  Jean Pierre Poulain  (PhD),  Tristan Fournier  (PhD),  Christelle Arandjelovic  (MSc); geriatrics team:  Bruno Vellas (MD, PhD ); primary care team:  Stephane Oustric (MD, PhD ),  Julie Subra  (MD).
Funding The ACCEPT study was funded by a grant from the  French Institute for Public Health Research (IRESP)  and the  French Institute for Health Promotion and Health Education (INPES). The MAPT study was supported by grants from the  Gérontopôle of Toulouse , the  French Ministry of Health  ( PHRC 2008 (PHRC , 2009)),  Pierre Fabre Research Institute  (manufacturer of the omega-3 supplement),  ExonHit Therapeutics SA , and  Avid Radiopharmaceuticals Inc. The promotion of this study was supported by the  University Hospital Centre of Toulouse. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript.
Consequently, the impact categories GWP (kg CO 2 -eq), AP (kg SO 2 eq), EP (kg P eq), and LO (m 2 a crop eq) were used to assess the environmental impacts in our study ReCiPe Midpoint 2016 (H) V1. 02  (Huijbregts et al. , 2017) , was used together with Ecoalim  (Wilfart et al. 2016)  and Ecoinvent  (Wernet et al. 2016 ) inventory databases for the impact assessment. Individual environmental assessments for each pig were implemented in SimaPro V8. 5. 4. 0 on the MEANS (MulticritEria AssessmeNt of Sustainability) platform (http:// www. inra. fr/ means), following the approach we proposed in a previous study  (Soleimani and Gilbert 2020).
The LCA model The LCA model was developed in six separate modules: feeding plan, animal profile, emissions, excretion, water expenditure and energy expenditure  (Soleimani and Gilbert 2020). Briefly, the model was developed based on pig net energy (NE) requirements, with a focus on the fattening period to allow prediction of the different performance profiles resulting from the composition of the tested diet. InraPorc® software, designed to simulate the performance pigs' response to different nutritional strategies  (van Milgen et al. 2008; Dourmad et al. 2008) , was incorporated in the LCA model to obtain sow-litter profiles (identical in all scenarios), feeding plans and corresponding simulated growth performance during fattening. Water and energy expenditures were calculated based on the IFIP report on typical French farms (IFIP-Institut de la Filière porcine 2014).
The individual fattening performance traits were used as input parameters in the life cycle inventory (LCI) in SimaPro to perform individual LCAs. The components of excreta (dry matter (DM), organic matter (OM), nitrogen (N), phosphorus (P) and potassium (K)) were calculated using the mass-balance approach, as the difference between nutrient intake in the feed and the nutrients retained in the body (Supplementary Material S1). A typical French slatted floor type of housing and slurry storage was adopted, along with system expansion approach considering that the manure produced replaced a certain percentage of mineral fertilisers  (Garcia-Launay et al. 2014). Average performance data were used for the sow-litter stage, and individual performance data were used for the postweaning and fattening stages.
2011 ) and 75% for N  (Nguyen et al. 2010). To ensure the results were comparable, the inventories, methods and calculations were kept the same in all the LCA runs. The environmental impacts of the diet ingredients were obtained from the Ecoalim dataset  (Wilfart et al. 2016 ) of the AGRIBALYSE® database using the Recipe method 2016, applying the attributional approach. Values of 500 km for cereals and 100 km for meals were used for the transport of ingredients from the farm to the feed factory  (Garcia-Launay et al. 2018) , and a value of 30 km  (Cadéro et al. 2018)  was used for the distance from the feed factory to the pig farm, taken from the attributional life cycle inventories of the ecoinvent version 3. 1 database.