dataesr/scientific-paragraphs-categorization huggingface dataset:
Participants in the route choice experiments The data on route choice behaviour in this article comes from six route choice experiments carried out between February 2018 and February 2019. The participants in the experiments were students at the University of Lyon taking part in the courses of traffic theory (66%), staff from the IFSTTAR (French Institute of Science and Technology for Transport, Development and Networks) and other universities, who received an invitation by e-mail to remotely join the experiments via a web browser (34%). All participants have signed, before the experiments begin, an informed consent form describing the objectives of the study, the data collection and processing, and the confidentially rules. Participants could opt out of the experiment at any time. No personal data were mandatory to participate to the experiments as people had the opportunity to identify themselves by a login of their choice.
Finally, all data were fully anonymised and processed as such. At the beginning of the experiments, the participants were briefed about the objective of the experiment and the interface of the experimental platform; for the participants that joined the experiments via web, a document with the instructions was shared. The participants were instructed to choose the route that they consider the best to complete a trip on time. Three of the six experiments were specifically implemented for the purpose of this work, so they were configured to obtain observations in the 9 representative OD pairs. The rest of the experiments were implemented for previous studies, so they were configured over 21 OD pairs different from the 9 representative set; data coming from these experiments was used to validate the methodology in this work.
The most likely routes joining the origins to the destinations are derived with the A � algorithm looking for the k-shortest paths in free-flow travel time. This is roughly equivalent to minimising the travel distance, but accounting for the influence of spacial limits. The total number of routes in the network, obtained with this algorithm, is 559,423, with an average number of 5. 82 routes per OD pair. Data representation. The selected route features in this study are the informed travel time, the length, directness, number of turns per kilometre and the percentage of freeway in the route composition. This features were selected as they are variables relevant in travellers' route choice behaviour  [21] [22] [23] [24] [25] , and because they are the attributes that participants can observe in the computer route choice experiments.
After removal of the very short trips and the high overlapping routes, the OD-routes are obtained by considering all the possible combinations of three routes from the set of routes joining that particular OD pair. For example, if there are 5 routes joining an OD pair, then the total number of OD-routes that are obtained is 5   3   À � ¼ 10. The total number of OD-routes in the network is 624,490. Clustering of the OD-routes. Before clustering, the data was normalised so that all the variables describing the OD-routes have the same weight in determining the dissimilarity between observations; this step is necessary when the range of the variables are not comparable, as is the case in the OD-routes where the directness of the routes takes values in the interval (0, 1), but the length of the routes takes values in the interval (0, 35).
As a result of the Bayes' theorem, the posterior distribution is proportional to the prior multiplied by the likelihood. In the general case, the posterior distribution H of the parameters is Hðb; S; b i ; 8ijY; XÞ / Y N i¼1 Y S i ; s 2 0 Þ where ϕ is the density function of the normal distribution, ϕ n of the n-variate normal distribution, f IG the density of the inverse-Gamma distribution and f IW of the inverse-Wishart distribution. The inverse-Gamma is the conjugate prior for the variance of the normal distribution, and the inverse-Wishart its generalisation for the multivariate case. The right hand side in Eq (3) has no closed form, however samples from the joint posterior distribution H can be obtained using the Gibbs sampling method  [40]. In this study, the Gibbs sampler software JAGS  [41]  and the R  [42]  package rjags were used to obtain 10000 samples of the posterior distribution H after a burn-in period of 20000 samples.
Discussion In this study, it was demonstrated that the choices of participants in a route choice experiment over a small but representative set of OD configurations can be scaled-up to the entire network. To obtain the set of representative OD configurations, a new methodology based on kmeans cluster analysis is proposed. First, the OD configurations in the network, i. e. , the OD pairs and three connecting routes, are represented in vector form according to the attributes of the OD pairs and routes. Then, these OD configurations are clustered in order to obtain a partition of the road network and the cluster centroids selected as representative of the entire network. The main hypothesis is that the choices of travellers over the entire network can be approximated with route choice models estimated using data collected for the representative set.
For this purpose, post-crisis structural banking reforms in the UK, US and Germany, and EU proposals were analysed to identify any differences with the French proposal. I then reviewed those EU reforms that concern elements of the reform, such as the AIFM directive 23  or MiFID II  24. In addition, the media database Factiva was used to obtain additional information on discussions in the context of the French reform process in general. Finally, all documents regarding the reform available on the official website of the National Assembly and the Senate were examined. These documents comprise transcriptions of the debates and hearings that took place in parliament and its affiliated committees, the reports issued during the parliamentary discussions, the impact study as well as the bill itself in its preliminary and its final version.
Using the qualitative data analysis software NVivo 10, content analysis comprised thematic and analytical coding. Thematic coding enabled the classification of amendments into categories reflecting the characteristics of the respective amendment, while analytical coding implied an assignment of policy-makers' justifications in support of their decisions on the amendments based on a list of semantic descriptors (as described below). This paper focuses on those amendments which concern the scope of activities because the scope represents a distinguishing feature among the different proposals. The need for legitimacy is much less pronounced for amendments dealing with the type of separation since all European proposals follow the same separation approach, i. e. the transfer of certain investment banking activities to a newly created trading entity.
Southern Cameroon serves as a case study area since it is facing a bundle of societal megatrends that are poised to have an impact on the still very high forest cover in coming years: The country's economy has recovered from the crisis of the early 2000s  [22] [23] [24] ; the local population is growing rapidly, especially in urban centers where the population tends to have different food consumption patterns; and the area is known as a bread basket for export to neighboring Gabon, Republic of the Congo, and Equatorial Guinea. These trends, combined with continued low agricultural yields, are generally expected to drive agriculture further into forest areas  [25] [26] [27] [28]. From the perspective of a stepwise approach towards the development of a national FREL, the study uses available datasets to establish an adjusted subnational FREL for Cameroon and critically analyses future steps for improving the FREL as the country aims to access performance-based finance.
CO 2 is the most relevant GHG in the forestry sector but in specific cases CH 4 and N 2 O may also be considered. The country has not formally fixed a reference period and a performance period. The establishment of an FRL is further constrained by available data. First, there is no dataset available to reliably trace the degradation or gain of forests, which restricts the scope of this article to establishing an FREL of CO 2 emissions from deforestation only, where other gases potentially emerging from smaller fires are considered negligible. Pools are restricted to above and below-ground biomass due to the uncertainty associated with more liable pools. This study uses the periods 2000-2015 and 2020-2030 as virtual reference and reporting periods, respectively, based on the availability of relevant datasets.
Remote Sensing of Activity Data Activity data was derived from a countrywide reference land cover map for a base year  (2000)  and the assessment of forest loss over the reference period  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) , clipped to the extent of the study area. The reference maps for 2000 and 2015 were developed using a hierarchical supervised decision tree-based wall-to-wall mapping methodology implemented in PCI Geomatica Software. The assessment was run on a composite of 10,517 terrain corrected (L1T) Landsat images (bands 3,4,5,7) with low cloud coverage sourced from the United States Geological Survey (USGS) Earth Explorer and resulted in seven thematic classes: (1) Primary and (2) secondary terra firma and (3) primary and (4) secondary swamp forests, (5) mangroves, (6) agro-industrial plantations of perennial crops,  (7)  non-forest land, and (8) forest loss since the year 2000.
Emission Factors from the National Forest Inventory Emission factors were developed on the basis of the country's first, and thus far only, national forest inventory (NFI) performed during the years 2003-2004  [35]. Tree biomass in Cameroon was inventoried using 204 valid census tracts distributed according to a systematic stratified sample design across the country -45 of which are located inside the study region. Tree biomass in both forest and non-forest (such as agroforests, fallows, tree plantations, etc. ) areas was considered, although non-forest classes are only sparsely present in the final sample. Re-analyzed for the purpose of REDD+, the NFI data allows for distinguishing carbon stocks in five land cover types in the study area: (1) Forest, (2) settlements, (3) grassland, (4) fallow land, (5) annual crops, and (6) perennial crops.
Carbon stocks were calculated by combining data and information on tree diameter and tree height from the NFI with pan-tropical allometric equations  [36]  and proxy shoot-to-root ratios for moist tropical forests  [37]. Emission factors were computed as the difference between initial forest carbon stocks of the living biomass and the carbon stocks left in the vegetation after forest conversion and presented in the results section. Further details on NFI data and assessments can be found in the background report  [38].
Trade The model considers flows of agricultural goods within Cameroon, as well as to and from neighboring countries and the international market. Trade of crops and foodstuff with third countries is documented by national publications  [48]  and the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT). Statistics on internal trade within Cameroon are not available and therefore estimated as the complement of local agricultural production needed to feed the local population. The share of the consumption in each of the five agro-ecological zones (AEZ) and rural/urban area that is satisfied by each source, that is local, each other AEZ or the rest of the world. This estimation is made according to three guiding principles: (1) The (over) supply status of a certain crop; (2) the proximity of the other regions with the importing region; and (3) it is assumed that imported goods from other countries mostly go to the urban areas.
Harvested Area and Arable Land Once the production level is computed, the harvested area results from dividing production by agricultural yields. Yields in the model are specified per administrative region based on data from the ministry of Agriculture's (MINADER), the AGRISTAT statistics report series, and counter-checked with country-level production data from FAOSTAT. Yields are kept constant at reported levels for future projections. In order to compute the impact on arable land, two other parameters are used:  (1)  The average number of harvests per year and (2) the share of fallow land in total arable land. The average number of harvests per year is crop and region specific and is taken from the AGRISTAT reports over 2000-2004. The area that lies fallow is mainly driven by the fallow period, which varies with the population density, that is to say, fallow periods are shorter in densely populated areas  [52].
The authors however also note that on a local scale, differences across global maps are significantly higher. The land use modeling approach to adjusting the FRL certainly increases the transparency of the adjustment term as compared to other approaches. It is an approach that requires a wide range of input data, which raises questions about data reliability beyond the scale of the sensitivity analysis performed in Section 2. 6. The literature does notably point out potential problems with national datasets relating to population  [78, 79]  and international trade  [80]. Further, challenges relate to data on domestic trade, consumption rates and the characterization of complex multi-crop and multi cropping cycle systems, and the general political situation and stability that could evolve in the next 15 years.
Pertinent datasets and information are not available at present. The UNFCCC reporting guidelines stipulate that all significant gases, pools, and activities shall be covered  [16]  where the threshold for "significant" is often defined as 10% of total emissions  [81]. Forest degradation is a significant source of emissions, as suggested by both qualitative national analyses  [25, 31]  and global studies  [25, 82, 83] , the latter placing forest degradation in the range of 25% of total land-based emissions. The main drivers of forest degradation are-listed in decreasing order of available documentation-industrial logging aimed at the international timber market  [57, 84, 85] , cocoa encroaching into forests  [86] [87] [88] , informal logging for the domestic and regional timber market  [89] [90] [91] [92] [93] [94] [95] [96] , and fuel wood collection  [97, 98].
Funding: This work was supported by the  RESTORE+  Project (www. restoreplus. org)which is part of the  International Climate Initiative (IKI) , supported by the  Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMU)  based on a decision adopted by the  German Bundestag. JP and AM received funding under service contracts of MINEPDED and the FCPF. The re-analysis of the national forest inventory data performed by MD was funded by the  SilvaCarbon Program  (egsc. usgs. gov/silvacarbon). Acknowledgments: This work was supported by the  RESTORE+  Project (www. restoreplus. org)which is part of the  International Climate Initiative (IKI) , supported by the  Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMU)  based on a decision adopted by the  German Bundestag.
We perform the analysis in two ways. First, a quantitative analysis of the six documents utilises text mining tools to draw out a summary of the themes, lexicon, and word-associations in the strategic documents of selected authorities, using the tm package of the R programming software (to remove common fill words, punctuation, and so on). This part of the analysis provides a data-driven, numerical view on the language chosen to write the SEP documents, ranking lexemes by scaled frequency of occurrence and measuring word co-occurrences. Second, a qualitative analysis explores the content of the policy documents and compares their understanding of main regional development drivers such as sectoral specialisation, skills, and transport policy, in order to highlight the similarities and differences between strategies taken by cities, and how they relate to expectations from regional theory.
While there are a number of initiatives underway to try and address these issues, such as the Offices for Data Analytics (ODA)  [84]  and Open Data Institute (ODI) nodes, this has been a slow and city regionally disparate process with only one established ODA (Avon and Somerset), and challenges identified in the areas of human resources, technological infrastructure, data, funding, and legal. Furthermore, many existing resources are focused on transport analytics (i. e. , Transport Data Initiative, Connected Places Catapult, Transport for West Midlands, Transport for Greater Manchester, Transport for the North). Finally, 'devolved' authorities should aim to collect data not only on aggregate output and global productivity, but also on the distribution of income (spatially and by groups), skills, and accessibility if they are to inform inclusive targets rather than productive performance goals only.
Data and methodology Our sample consists of all common stocks (share code is 10 or 11) with available monthly short interest data listed on NYSE and AMEX over the period of January 1988 to December 2016. 4 Monthly short interest data are from Compustat. Closed-end funds, REITs, trusts, and ADRs are excluded from the sample. Stock information such as price, trading volume and the number of outstanding shares is from the Center for Research in Security Prices (CRSP). Institutional ownership data is from Thomson Reuters. Our main variable of interest, the short interest ratio (SIR t ), is defined as the ratio of total number of uncovered shares shorted (SI) over the total number of shares outstanding in month t. Following the method in Lee and Swaminathan (20 0 0) , we form 30 (10 × 3) portfolios based on independent sorts of past J-month cumulative returns and the recent level of short interest at month t-2.
Panel C and D present equal-weighted average monthly raw returns and Fama-French 3-factor adjusted returns in percentages for heavily and lightly shorted stock portfolios, respectively. All NYSE/AMEX common stocks with monthly short interest data are sorted based on their short interest ratios at month t and then equally assigned into ten portfolios. S1 represents lightly shorted stock portfolio and S3 represents heavily shorted stock portfolio. S1-S3 represents short selling strategy portfolio. The portfolios are rebalanced monthly and held for K months where K = 1, 3, 6, or 12 months. Stocks with prices less than $5 at the end of formation period are excluded. We skip one month between formation and holding periods. Newey and West (1987)  t-statistics are in parentheses. The sample period is from January 1988 to December 2016.
Controlling for size Many studies show that momentum profits are negative related to firm size  ( Jegadeesh and Titman, 1993; Hong et al. , 20 0 0 ). Asquith et al. (2005)  find that heavily shorted stocks are mainly small-and medium-sized stocks. To examine whether the size effect subsumes the return predictability of the past return and short interest, we examine the performance of the joint approach in three different size subsamples. First, we equally divide all sample stocks into three size groups (small, Table  2  Monthly returns of portfolios sorted on past returns and short interest. Panel A: Raw Returns  This table presents average monthly returns of portfolios based on two-way independent sorts on past J-month returns and short interest ratio (SIR) at month t. At the beginning of each month, all NYSE/AMEX common stocks with monthly short interest data are sorted in ascending order based on their past J-month returns and then equally assigned into ten portfolios.
The role of short-sale constraint Short-sale constraints tend to increase the cost of short selling. Therefore, such constraints could help weed out relatively uninformed short sellers. Consequently, the short interest variable should be more informative when short-sale constraints are severe. Given the important role of short-sale constraints, 5  in this subsection we study whether our approach performs best among stocks with the most binding short-sale constraints. Following  Nagel (2005)  and  Daniel et al. (2017)  , we rely on institutional ownership (IO) as a proxy for the short-sale constraint. Institutional ownership data are from Thomson-Reuters 13-F filings (S34). Since IO data are reported quarterly, we use reported IO in month t for the subsequent months t + 1 to t + 3. We delete sample stocks without IO data.
Cost Estimation Cost analysis was performed in euros for the year 2012 from the perspective of the payers of breast cancer screening, which are the French State and the French Health Insurance System. Costs considered in the analysis were medical and nonmedical direct costs and were grouped into one of the following categories: MM unit, renting the screening management premises, staff, mailing of invitation letters and reminders, equipment and operating costs (including maintenance), mammograms performed at the RO, and other costs. Table  1  shows the unit cost for the main cost components. Cost data were estimated retrospectively from the 2012 accounting file of the screening management department and from.
05. We obtained a 95% CI for the ICER and a cost-effectiveness acceptability curve (CEAC) using a nonparametric percentile bootstrap technique, a method that requires no assumptions about the sampling distribution of the ICER. Bootstrapped cost and effectiveness were obtained and plotted in the cost-effectiveness plane by sampling 10 000 samples of the same size as the study sample with replacement and by determining the cost and effectiveness of each one. 39 Analyses were conducted with the use of R statistical software, version 3. 4. 4, and Microsoft Excel 2010. The bcea package was used for the CEAC.
Background: Health state utility (HSU) is a core component of QALYs and cost-effectiveness analysis, although HSU is rarely estimated among a representative sample of patients. We explored the feasibility of assessing HSU in head and neck cancer from the French National Hospital Discharge database. Methods: An exhaustive sample of 53,258 incident adult patients with a first diagnosis of head and neck cancer was identified in 2010-2012. We used a cross-sectional approach to define five health states over two periods: three "cancer stages at initial treatment" (early, locally advanced or metastatic stage); a "relapse state" and otherwise a "relapse-free state" in the follow-up of patients initially treated at early or locally advanced stage. In patients admitted in post-acute care, a two-parameter graded response model (Item Response Theory) was estimated from all 144,012 records of six Activities of Daily Living (ADLs) and the latent health state scale underlying ADLs was calibrated with the French EQ-5D-3 L social value set.
Otherwise, EQ-5D-3L data are increasingly collected along clinical trials  [3]. However, HSU estimates lack representativeness due to the exclusion criteria applied to the patient population such as an older age or the presence of comorbidities  [10] [11] [12]. Altogether, none of the patient surveys were conducted in France  [8]  and few French patients were recruited in international clinical trials (e. g. , less than 20 patients in  [12] ). By default, a cost-effectiveness analysis conducted in the French healthcare context should further assume that patient surveys from other countries are representative of French patients  [13]. In this study, we explored another route than patient surveys to estimate consistent HSU at the country level. More specifically, the French National Hospital Discharge database allows identifying all patients cared with a severe condition such as cancer as well as health states typically used in a cost-effectiveness analysis such as cancer stage at initial treatment and relapse in the follow-up.