dataesr/scientific-paragraphs-categorization huggingface dataset:
The Patriarche chronological assignments (in brackets) for sites within our study area, however, reflect the France-wide chronology employed in the Patriarche database, which does not precisely match an up-to-date chronology for Provence. Discussion of chronological issues, as well as additional detail on the archaeology of Holocene Provence, can be found in S1 Text. Until the 8 th millennium BCE the inhabitants of Provence were exclusively Mesolithic-i. e. , mobile foragers, with correspondingly modest population densities  [23]. Throughout the Mesolithic and into the Early Neolithic (EN; approximately 7900-6750 BP), Provence was apparently relatively sparsely populated, albeit by increasing numbers of sedentary agriculturalists occupying small hamlets. The beginning of the Neolithic in Provence, probably some combination of in-migration by agriculturalists and adoption of agricultural technologies and lifeways by indigenous inhabitants  [23] [24] [25] , is marked by the first signs of widespread anthropogenic transformation of landscapes that until then had been little modified by human activity  [26].
Data We explore potential relationships between the inhabitants of our study area in Provence and climate changes through most of the Holocene (8400-1400 BP) by integrating empirical and modeled data. These include modern geographic data, climate-sensitive agro-ecosystem modeling of PAgP with annual resolution, and archaeological site data from the Early Neolithic through the Gallo-Roman period. For the study area of approximately 1400 km 2 (Fig 1 ), we incorporate: 1. modern geographic data, primarily a 30m digital elevation model (SRTM30  [64] ) and terrain characteristics derived from it, 2. high-resolution (300m pixels and annual steps) Holocene paleoclimate data produced by downscaling a 0. 5˚centennial-step modeled dataset derived from inverse modeling of pollen data for the Mediterranean throughout the Holocene (  [65] ; see  [11]  for methodological details), 3.
annual 300m pixel results of the LPJmL agro-ecosystem model for the period 8400-1400 BP, parameterized for past agriculture through use of archaeological and ethnographic data (see  [10] ), produced using the high-resolution paleoclimate data described in (2) above, and 4. Archaeological site data spanning the period between 7250-1450 BP (the Early Neolithic period through the end of the Gallo-Roman period) from the Patriarche database, which provides coordinates, site classification, and chronological information for approximately 2250 sites (excluding those of uncertain date) in the study area. Centennial scale episodes of aridity are reflected in the subset of data from Guiot and Kaniewski's Mediterranean-wide Holocene model  [65]  that we use here (see  Fig 2). Although the accuracy and precision of the results might be improved by using more local proxy data, such data are unevenly distributed in space and time, generally lack the seasonal resolution produced by modeled data and necessary for agroecosystem modeling, and restrict the generalizability of the method.
Employing Guiot and Kaniewski's reconstructed climate dataset  [65]  makes it possible to easily replicate this method in any part of the Mediterranean Basin. Downscaling these data (cf. [11] ) produces single probabilistic realizations based on the input paleoclimate data; individually these are realistic rather than accurate, but in aggregate they are as accurate as the input data allow, and enable re-aggregation across different intervals (e. g. in archaeological periods rather than centennial steps).
Moreover, given the relative scarcity of data about past agricultural practices and their diversity in time and space, we prefer to analyze a range of possible outcomes and therefore use two parameterizations, representing a low estimate (Wheat Par 2 [W2], approximating minimally intensive agriculture) and a high estimate  (Wheat Par 1 [W1], approximating maximally intensive preindustrial agriculture, i. e. employing tilling, manuring, weeding, etc. ) (for a detailed discussion of these parameterizations, see  [10] ). We focus primarily on contrasts between periods for a given parameterization, as these are the least dependent on necessary assumptions of the modeling process; discussion below is on the basis of W1 results unless otherwise specified. After excluding sites of uncertain date and filtering the database of sites for occupational and agricultural sites-i.
The R scripts designed for the analyses allow for future improvement in data sources and/or incorporation of additional variables. In order to assess the significance of PAgP (and changes therein) for inhabitants of the region in different periods, we employ both synchronic and diachronic analyses, comparing locations of distinct types of sites within periods and settlement locations across periods. PAgP of aggregated site catchments for each period is also compared to PAgP for the landscape as a whole during that period, contextualizing estimated land use within the universe of possibilities available to inhabitants. Further methodological detail and consideration of uncertainties is given in S3 Text and S4 Text. Relating PAgP to settlement patterns echos previous archaeological attempts to relate settlement patterns to agricultural potential (e.
Managing uncertainties The varying spatial and chronological resolutions of these datasets, as well as the quality of the data themselves, pose analytical challenges. Mismatches in spatial and temporal resolution in the geographic and paleoclimate datasets has been addressed through a downscaling approach  [11]  in order to produce the annual 300m pixel LPJmL results used here  [10]. The resulting dataset is idea for analysis, but the accuracy of the downscaled data is limited by the accuracy of the regional-scale paleoclimate reconstruction that serves as the basis for the downscaling. In principle this could be improved by integrating local paleoclimate data for the study area with the regional reconstruction, but this presents another set of methodological challenges. The Patriarche data present a different set of challenges, some endemic to archaeological survey data and others characteristic of data aggregated from diverse sources.
Also in contrast with the raw data, the adjusted data (see rightmost columns in Table  2 ) suggest that heightened taphonomic effects on the EBA relative to the FN mask a significant increase in settlement in the EBA. At the same time, the effects of the time-averaging of sites across periods should somewhat counterbalance the emphasis that the adjusted data put on the Neolithic Period, as longer periods are likely to have the effect of overemphasizing numbers of sites (as is evident in the time-adjusted counts in Table  2 ). In short, the patterns we consider below appear to be robust even in face of a worst-case assumption about biases in the data. Drawbacks of the dataset are offset by the remarkable density and time depth of the data. Moreover, the most salient bias in the data-i. e. , the absence of data from the Durance floodplain-is consistent over time, suggesting that diachronic analyses should be minimally affected.
Diachronic settlement patterns Interpreting changes in the number of sites over time is complicated by landscape taphonomy and time-averaging (see Section 4. 3), and as data about site size is inconsistently reported we exclude that also. Nevertheless some patterning is strong enough that it likely transcends problems of site preservation or chronological limitations, and the presence of sites from all periods in most areas suggests that taphonomic problems are at least not effacing particular periods from the landscape. Within the study area, the most salient diachronic pattern is a marked MBA abandonment; resettlement of the area began in the LBA and a florescence of sites followed in the LIA and continued into the Gallo-Roman Period. Table  2. Site counts by period (sites may persist for multiple periods so column totals may be higher than the total number of sites recorded in the region).
The regional population decline in the EIA is reflected locally in the slower pace of new settlement relative to the LBA. Local chronology (i. e. the periodization used in the Patriarche dataset) is not sufficient to resolve regionally-apparent fluctuations during the LIA and Gallo-Roman periods  [44] ; as a result the apparent burst of settlement in the study area at the beginning of the LIA is likely the result of overall growth throughout that period. At the same time, the rearrangement in settlement patterns during the LIA-i. e. , abandonment by the LIA of some EIA sites even as the total number of settlements increased dramatically-is consistent with the regional political upheaval of the time.
Acknowledgments All spatial analyses have been carried out in R [91], using primarily the raster [92] and spatstat [93] packages and plotting data primarily with the lattice package [94]. The authors would particularly like to thank for their input two other project contributors who have not been directly involved in this paper:  Nobuyuki Hanaki  and  Sylvie Thoron. This paper is a contribution to the  AMEDEE  project, which has received partial funding from  Laboratory of Excellence OT-Med  (Project  ANR-11-LABEX-0061 ,  AMidex  project  11-IDEX-0001-02 ) and the  Research Network ECCOREV (Ecosystemes Continentaux et Risques Environnementaux ). The Patriarche data that we use have been kindly provided by the  Ministère de la Culture et de la Communication  ( DRAC PACA/SRA ) and managed in ArkeoGIS (arkeogis.
Urban data-London The data from the Transport for London contains information on road traffic collisions that involve personal injury occurring on public highways which have been reported to the police. Data is collected by the police at the scene of an accident or, in some cases, reported by a member of the public at a police station, then processed and passed on to Transport for London. The data, taken between 2005 and 2014, includes 242,782 unique collisions, with x, y space coordinates available. Accidents are subdivided into three categories: Fatal injury, where death occurs in less than 30 days as a result of the collision, Serious injury, if there are fractures or injuries requiring hospital treatment, and Slight injury, where the accidents do not require medical treatment. Table  1  contains the reported frequencies between 2005 and 2014.
Motorway data-Mexico The motorway data considered here contains road traffic collisions registered on motorways in Mexico. The data is divided for each motorway and considers, for each accident registered by the police, the distance from the starting point of the highway. Unfortunately, the data does not include in which direction of the road the accident occurred. The motorways analysed have Mexico City as their starting point, connecting the capital of Mexico with five large cities: Cuernavaca, Toluca, Pachuca, Puebla and Queretaro (Fig  2 ). There are two types of motorways, Federal Roads (free of charge) and Toll Roads and each city has both, a Federal Road and a Toll Road connecting them to Mexico City, except for the case of Queretaro for which the Federal Road first passes through another city (Toluca) and so it is not considered.
Discretisation of the data Urban environment. In the case of the urban space, we tessellate the region of analysis, that is, the city is divided into nearly 30,000 non-overlapping, regular hexagons, and the number of accidents within each hexagon is counted. A hexagonal tessellation is frequently used in cartography since it offers advantages in terms of the visualisation  [28]  and it offers equal-area units and minimal correlation with regularly spaced features, as opposed to a square grid  [29]. Hexagons of side length 40 metres provide a useful level of refinement for our analysis dividing the region of central London into N = 29,600 tiles. Under this partition, Waterloo Bridge, for example, sits within four hexagons from its extremes on either side of the River Thames. Hexagon tiles are small enough that the region they represent are clearly identifiable and, although they do not match exactly with road junctions, they clearly represent parts of streets.
The results of the mixture model (the number of groups, the accident rate and relative size) can be computed using the statistical package CAMAN (Computer Assisted Analysis of Mixtures) by considering the observed number of road accidents suffered in each of the tiles or segments and a test can help us accept or reject the distribution obtained  [37]. Rare Event Concentration Coefficient RECC. The distribution of the rates ðq 1 ; q2 ;. ; qk ; l1 ; l2 ;. ; lk Þ obtained from the data is useful since we could, for example, simulate accidents within each unit to understand the expected departures that simply a natural variability of the number of accidents would yield. In the case of highways, for instance, being aware of the rate of accidents from its origin to its destination gives a full description of the occurred accidents.
RPC acknowledges the support the government of Mexico via a  Conacyt Scholar. HGR acknowledges the support of the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme , grant agreement number  646592   MAGnUM  project. SRB acknowledges the support by the  Cimplex  project funded by the  European Commission  in the area FET Proactive: Global Systems Science (GSS), as a Research and  Innovation Action , under the  H2020 Framework programme , grant agreement number  641191. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Which typologies can be proposed to capture the different forms of co-production witnessed in FRG? The article takes co-production types and typologies in literature as a starting point, examines to which e te t the fit the t pes of o-production that are observed in FRG and further develops them in specific relation to this domain. Hereto, it confronts insights from literature with empirical data on FRG co-production in England (UK), Flanders (Belgium), France, the Netherlands and Poland. Based on the literature review and the empirical evidence, the article presents 3 complementary typologies of citizen co-production in FRG, which are critically reviewed in turn.
Indeed, in a large part of the world, dealing with flood risks constitutes a primary aspect of CCA and disaster management. Consequently, the ties between these separate literature strands are growing stronger (see  Wamsler, 2014). The relevant papers have been retrieved from the Web of Knowledge and Google Scholar databases by searching for combinations of the following terms: flood (management/governance), citizen, community, public, private, engagement, co-production, co-delivery, co-financing, typology, climate change (adaptation), disaster and water management. In addition, a snowball technique was used, whereby references in screened literature led to new sources. The papers have been collected during the time frame of the empirical research, instead of resulting from a structured literature search at a single point in time.
Empirical analysis supporting analytical steps ii-iv The empirical analysis compares FRG co-production in England (UK), Flanders (Belgium), France, the Netherlands and Poland. Although not a country in itself, Flanders is chosen as the unit for analysis instead of Belgium because FRG competencies in this country are located at the regional level. Similarly, devolved regions exist within the United Kingdom. Here, England is selected as the analytical unit. The empirical analysis for this research refers to the period 2000-2016. This period has been selected for two reasons. First, it contains a time frame that is comprehensible to look back to in interviews, and of which sufficient accessible data records exists. Second, this period signifies the movement towards citizen co-production in all countries of analysis (see  Meijerink & Dicke, 2008; Johnson & Priest, 2008; Mees et al.
, 2016). Considering the fact that inland water systems are strongly entangled, the analysis takes both fluvial and pluvial flood risks into account. The data for the analysis were collected under the auspices of the EU-FP7 project STAR-FLOOD 2  and EU-JPI Climate TRANS-ADAPT 3. In the framework of the STAR-FLOOD project, between 40 and 70 semi-structured interviews were conducted in each country with key actors in FRG from December 2013 to March 2015. These included policymakers, practitioners and a limited number of NGOs/citizen groups operating at national and local scales in the sectors of water management, emergency planning and spatial planning. The interviews did not exclusively focus on citizen involvement but assessed developments in FRG in general. In TRANS-ADAPT, in-depth analysis has been carried out on 3 local cases in both the Netherlands and France.
Each case analysed and evaluated local community-based initiatives in relation to flood alleviation schemes. 30 semi-structured interviews have been conducted in France and 27 in the Netherlands from January 2015 to July 2016. Interviewees represented a range of local actors, including politicians, public officials and citizen groups involved in bottom-up community-based initiatives. The interview protocols of both research projects are provided in Annex 1. In both projects, the information gathered from the interviews has been complemented with in-depth policy analysis of purposively selected documents. This entailed both local and national data (legislation, policy documents, websites, reports, etc. ). A mix of inductive and deductive coding was used to analyse the collected data. Citizen involvement in decision-making and in implementation were two codes used in both projects.
Only in rare cases (e. g. Bovaird et al. , 2015) , multi-dimensional typologies were established. In this article, these described types were confronted with the examples of co-production that were identified in the empirical analysis and in literature on FRG and CCA. The types described in literature were then refined and assembled into 3 complementary typologies, which host and structure all the examples observed in the analysis: 1. the type of interaction between governments and citizens: In the sections below, each typology is discussed in detail. Per typology, first its origins and application outside the remit of FRG is explained. Following, it is assessed against the data derived from the empirical analysis and literature related to FRG. Based on this, a typology is proposed adapted to the context of FRG.
Acknowledgement This article incorporates resea h f o t o esea h p oje ts, i ludi g "TA'-FLOOD , o issio ed ithi the  EU 7 th Framework Programme for Research, Technological Development and Demonstration  (grant agreement no. 308364 ); and TRANS-ADAPT, commissioned under the  EU-JPI Climate programme. We also want to thank  Dr Sally Priest  and our anonymous reviewers for their review and advice.
The dynamics of global inequality have attracted growing attention in recent years. However, we still know relatively little about how the distribution of global income is evolving. Income inequality is increasing in many countries, but large emerging countries like India and China are catching up and might drive global inequality down. Recent studies of global inequality combine household surveys and provide valuable estimates  (Lakner and Milanovic 2016 , Liberati 2015 , Ortiz and Cummins 2011). Surveys, however, are not uniform across countries, they cannot capture top incomes well, and are not consistent with macroeconomic totals. In this paper, we report on new estimates of global inequality presented in the World Inequality Report 2018  (Alvaredo et al. , 2018). These estimates are based on recent, homogeneous inequality statistics produced for a number of countries in the World and Wealth Income Database (WID.
Global income inequality dynamics (1980-2016) We estimate income per adult with equal splitting for married couples, before taxes and before government transfers, but after the operation of private and public retirement systems. The best way to make estimates comparable across countries is to distribute total national income, as recorded in the internationally-harmonized national accounts of each country. To do so we combine survey, tax, and national accounts data in a systematic manner. This general methodology is presented in detail in  Alvaredo et al. (2016)  and has already been applied to a number of countries: the United States in North America; France in Europe; 1 China and India in Asia; Brazil in South America; Russia; and the Middle East. Inequality estimates for these countries are homogenous, distribute 100% of national income, and capture the top of the distribution well, overcoming weaknesses from previous studies.
2 For example, we know the average income level in Malaysia, but not (yet) how national income is distributed to all individuals in this country. We assume that the distribution of income in Malaysia is the same, and has followed the same trends, as in the region formed by China and India. For Sub-Saharan Africa, we have fiscal income series only for South Africa and Ivory Coast. Therefore, we relied on household surveys available from the World Bank (these estimates cover 70% of Sub-Saharan Africa's population and yet a higher proportion of the region's income). These surveys were matched with fiscal data available for Ivory Coast from WID. world so as to provide a better representation of inequality at the top of the distribution. Russia, the shift to a deregulated economy in India. Policies and institutions matter: rising inequality cannot be viewed as a mechanical, deterministic consequence of globalization or technological change, as most economic models assume.
Notes: The figure displays the average real income of the global bottom 50% with actual data from 1980 to 2016 and as projections from 2016 to 2050 under three scenarios for inequality: 1. Business as usual, 2. European scenario, 3. US scenario. If all countries follow the inequality trajectory of Europe between 1980 and 2016 from 2017 to 2050, the average income of the bottom 50% of the world population will be €9,100 by 2050. Income estimates are calculated using Purchasing Power Parity (PPP) euros. For comparison, €1=$1. 3. Source: WID. world.
Global bottom 50% average income, 1980-2050 Notes: The figure displays the average real income of the global bottom 50% with actual data from 1980 to 2016 and as projections from 2016 to 2050 under three scenarios for inequality: 1. Business as usual, 2. European scenario, 3. US scenario. If all countries follow the inequality trajectory of Europe between 1980 and 2016 from 2017 to 2050, the average income of the bottom 50% of the world population will be €9,100 by 2050. Income estimates are calculated using Purchasing Power Parity (PPP) euros. For comparison, €1=$1. 3. Source: WID. world. 10,000 €9,100 9,000 Global top 1% €) 8,000 income share Annual real income per adult (PPP 1,000 2,000 3,000 4,000 5,000 6,000 7,000 Scenario 1: All countries follow their own inequality trend Scenario 2: All countries follow Europe's inequality Scenario 3: All countries follow US inequality trend €1,600 trend Global bottom 50% income share Bottom 50% average annual income €3,100 , , , Scenario 3: All countries follow US Europe's inequality trend Scenario 1: All countries follow their own inequality trend Scenario 2: All countries follow Average annual income assuming.