dataesr/scientific-paragraphs-categorization huggingface dataset:
Soil fertility One of our objectives is to understand how the heterogeneity in soil fertility within regions affects the transmission of agricultural input price shocks to conflict. As an empirical counterpart to our model of soil fertility, we use the quantity of nutrients naturally available in the soil. When soil quality is poor, fertilizers can be used as substitutes. Our baseline data source is the Harmonized World Soil Database (hwsd) built jointly by the fao (Food and Agriculture Organization) and iiasa (International Institute for Applied System Analysis). This data uses models that incorporate location-specific soil attributes (texture, organic carbon, pH, and total exchangeable bases) to compute, among other measures, an index of nutrient availability. 20 The index assigns soils to one of five categories ranging from 1 ("no or slight constraints") to 5 ("very severe constraints").
Although, the data are available by spatial units of 5 arc-minutes (approx. 9km × 9km at the equator), while our units of observation are cells of 30 arc-minutes. For each cell, we thus compute the average value, the mode, as well "land invaders", "land invasion", "land redistribution", "land battle", "over cattle and land", "invade land", "over disputed land", "over a piece of land". 20 Each soil attribute is associated with a rating. The nutrient availability index is calculated as the average of the rating of the attribute with the smallest rating and the average rating of the three other attributes. See  Fischer et al. (2008)  for a detailed description. as the standard deviation and the variance of this variable, which we use as a measure of the heterogeneity of soil fertility. We also compute the share of soil fertility in each category and define high fertility cells as those for which the share of soil with "no or slight constraints" is above 50%.
The main advantage of this data is that crop suitability is exogenous to conflicts, as it is not based on actual production. In our benchmark estimations, we use the main crop produced in the cell, which is defined as the crop with the highest suitability level. Second, we gather information about the required N-P-K mix of nutrients (measured in kg/ha) for each crop. Our benchmark data are generated by the International Plant Nutrient Institute (ipni) and include information on the nutrient requirements of 42 crops (Table  A2 , online appendix section A. 4). Finally, we obtain the real international market prices of each nutrient from the World Bank Commodities Dataset. A graph of each price series over time is shown in the online appendix (section A. 4), Figure  A1. The three price spikes that occurred between 2008 and 2009 were due to a rise in demand triggered by US biofuel programs and to the imposition of a 135% Chinese export tariff on phosphate (see  Schröder et al.
Taken together, the countries in our dataset represent only 4% of world imports and 2% of world consumption of fertilizer. 23  Second, we assume that changes in the international market prices of nutrients are transmitted to local markets. We investigate the validity of this assumption using data on a subset of countries and nutrients in section D. 2. 1 of the online appendix. We gathered data for urea and phosphate, at the market level, for about 350 markets located in 17 countries over a 4-year period (2010-22 When identifying the most suitable crop using GAEZ, we need to restrict our sample to the crops for which we observe the ideal NPK mix. For 78% of the cells, this has no incidence as the most suitable crop is one for which we have NPK data. Removing other 22% of the cells has little impact on our results (if anything these are marginally reinforced).
Other data We complement our dataset with additional cell-specific information. In particular, we add cell-level information from prio-grid v. 2  (Tollefsen et al. , 2012) : geographical characteristics, population, and weather. We also control for economic shocks such as variations in the prices of produced and consumed agricultural commodities  (Berman and Couttenier, 2015, McGuirke and Burke, 2018)  or in the price of locally produced natural resources such as oil and minerals  (Berman et al. , 2017). Finally, in the last part of the paper we make use of information on ethnic homelands boundaries from  Murdock (1959). More details about these variables and their sources are provided in the corresponding robustness section, and the full description appears in the online appendix, section A.
Final sample statistics Table  1  provides descriptive statistics for our main variables (section A. 6 of the online appendix provides additional statistics about the variables used in our sensitivity analysis). Our sample contains 6,565 cells belonging to 43 ssa countries, and covers the period from 1997 to 2013. Pr(conflict) equals 1 if at least one conflict event occurs within the cell-year. At least one conflict event occurred in 7% of the 111,605 cell-year units. Unsurprisingly, the unconditional probability of land-related conflicts is much lower at 1%. Conditional on at least one conflict event being observed, the number of conflicts is 1. 25 on average. The average level of soil fertility, computed from our baseline data on nutrient availability, is equal to 2. 11, which is close to 2, the value defined as "moderate constraints" in our dataset (1 denotes "no or slight constraints", 5 denotes "very severe constraints").
, 2017)  or to rainfall variations  (Harari and Ferrara, 2018 , Guariso and Rogall, 2017 , Adhvaryu et al. , 2017). These shocks could be correlated to our fertilizer prices. Similarly, soil quality might correlate with production and consumption patterns. We sequentially include agricultural price shocks, local rainfall variations, oil and mineral prices. The producer prices of agricultural goods are computed as the sum of the world price of all crops produced in the cell, weighted by the share of each crop in cultivated land (in the spirit of  Berman and Couttenier, 2015 and McGuirke and Burke, 2018). Consistent with these papers, we find a negative impact of such shocks (column 2). The crop consumption price index is computed as the weighted sum of the world prices of consumed crops, with weights being defined at the country-level by nutritional intake shares from FAO Food Balance Sheets data  (McGuirke and Burke, 2018).
(2017)  and  McGuirke and Burke (2018) , who study respectively the links between mineral prices and food price variations on conflict at the local-level. 32  Cervellati et al. (2016)  show that suitable conditions for malaria increase the incidence of civil violence. We employ the Malaria Ecology Index used by  Kiszewski et al. (2004)  and developed by Gordon McCord as a measure of malaria at the cell level. 33 These data were obtained from prio-grid and the Global Land Survey dataset, respectively. Demographic and economic variables are measured before the start of the period. A complete description of the variables is provided in section A. 5 of the online appendix. 34 Soil quality and its dispersion could affect ethnic diversity in particular, as found by  Michalopoulos (2012). If this is the case, then our estimates would confound the magnifying effect of heterogeneous land endowments with that of ethnic divisions.
Measurement In the online appendix section F. 3, we show that our results are not sensitive to changes: i) in the level of aggregation, ii) to alternative measure and definition of conflicts, iii) to alternative measure of fertility, and iv) to the required nutrient mix by crop. First, we show that changes in the level of spatial aggregation of our data does not influence our results qualitatively. We reproduce our baseline estimations using two alternative levels of aggregation, one smaller (0. 25×0. 25 degree cells) and the other larger (1×1 degree cells) than our baseline. Second, we use a number of alternative datasets and definition of conflicts. We start with an alternative dataset, ucdp-ged, that records only deadly events pertaining to conflicts associated with more than 25 conflict-related deaths in a given year.
Violence at the individual-level We show that our measure of fertilizer prices correlates with several measures of violence at the individual or household levels, using two different datasets (online appendix section E. 1). First, we combine 3 waves of the Afrobarometer surveys (rounds 3 to 5) which contain geo-localized information at the individual-level for 28 African countries and around 80,000 individuals over the period 2005-2013. Note that the Afrobarometer is not a panel but a repeated cross-section, hence we cannot track individuals over time. To measure violence, we use the following questions: (i) Q11. a "During the past year, have you or anyone in your family: Had something stolen from your house?" and (ii) Q11. b "During the past year, have you or anyone in your family: Been physically attacked?".
Second, we use household-level information from the World Bank lsms, which cover only six countries but with a panel structure. To measure violence, we use the fact that the surveys ask households wether they did face a number of shocks. We use information on the two following shocks: (i) theft / robbery / other violence; (ii) involuntary loss of house/land. For each type of shock, we construct dummies which equal 1 if the household did face at least once the shock in the previous year(s). As explained in more details in the online appendix, these data are less harmonized and more noisy than the one of the Afrobarometer. We find results broadly consistent with our story, especially in the case of the Afrobarometer and when using the land-related shocks from the lsms. The latter finding echoes our previous one obtained with our measure of land-related conflict from acled (Table  2 , columns (3) and (  4 )).
We make use of information on ethnic homeland boundaries from  Murdock (1959). An alternative source for ethnic boundaries is the data from greg. Given our level of spatial disaggregation, our preferred source however is  Murdock (1959) , which contains many more groups than greg (835 versus 250 over the geographical area we consider), which is key to be able to identify sufficient variation. Denote by s ni the share of nutrient n for given crop i from IPNI. We first compute: HET i = n=N,P,K m =n (s ni -s mi ) 2 (25) HET i represents the sum of the squares of the differences in nutrient shares for a given crop, i. e. an indicator of nutrient heterogeneity. We then sum across the five most suitable crops of spatial unit c (which can be a cell, an ethnic-group × cell, or an ethnic group): HET c = 5 i=1 HET i (26) We interpret HET c as an indicator of crop production heterogeneity, i.
vided outstanding research assistance. The project leading to this publication has received funding from  Excellence Initiative of Aix-Marseille University  -A*MIDEX, a  French "Investissements d'Avenir" programme and  from the  French National Research Agency  Grant  ANR-17-EURE-0020. Mathieu Couttenier  acknowledges financial support from the  IDEXLYON  from the  University of Lyon (French National Research Agency , " Programme Investissements d'Avenir "  ANR-16-IDEX-0005 ) and the  Swiss National Foundation  (grant  Ambizione-PZ00P1 161451 ). Raphael Soubeyran  acknowledges financial support from the  ANR GREEN-Econ   research project  ( ANR-16-CE03-0005 ).
Acknowledgement I would like to thank the Editor ( Mrs. Agnieszka Żołądkiewicz-Kuzioła ) and the two anonymous reviewers for the comments provided in relation to the earlier version of the paper. Finally, I would like to express my deepest gratitude to  Prof. Garrett Epp  for the proofreading and to  Prof. Ewa Miklaszewska  for her comments on the first version of the paper. Any remaining errors are solely the author's responsibility.
A dataset of 41 bathymetric profiles detected by satelline imagery and echo-sounding to a maximum depth of 38 m along the fore-reef slopes of Takapoto Atoll (Tuamotu Plateau, French Polynesia, central Pacific) were qualitatively and quantitatively analyzed in order to identify the main geomorphological features. In addition, scuba-diving investigations from a selected site resulted in defining coral composition and cover rate. Six distinct fore-reef sectors were morphologically differentiated on the basis of seaward extension and slope gradient. Only the north end, and a restricted area of the mid-south-east side extend seawards over an average distance of 150 m, with a relatively low gradient (23°-38°). Most of the outer-slope areas are narrow (less than 100 m on average) and steep (average slope equals to 45° to up to 70°).
From satellitederived near-shore bathymetry,  Collin et al. (2018)  provided the first high-resolution outer-slope morphometry of the eastern side of Takapoto. In addition to geomorphology, the characterisation of coral communities living along the upper fore-reef slopes is an important step in terms of paleo-water depth reconstructions. The earlier works on the compositional and distributional patterns of coral communities at Takapoto were carried out by  Chevalier and Denizot (1979) ,  Kühlmann (1982) ,  Bouchon (1983)  and  Kühlmann and Chevalier (1986). In this study, are presented both morphological and biological datasets from the foreslopes of Takapoto Atoll in the Tuamotu Archipelago  (French Polynesia, south-central Pacific). Analysis of satellite-based datasets gains insight into the detailed spatial distribution of submerged geomorphic features.
Acquisition and analysis of geomorphic data Pleiades-1 satellite imagery was collected over Takapoto Atoll on September, 22 2016 at 20 h 01 min 03 sec (UTC). Orbiting at 694 km altitude, Pleiades-1 sensor provides four spectral wavebands (blue: 430 nm-550 nm, green: 490 nm-610 nm, red: 600 nm-720 nm, and infrared: 750 nm-950 nm) at 2 m pixel size. The processed imagery totaled 72 294 672 pixels in the form of 9759 lines and 7408 columns. Given the sunsensor-scene trigonometry, a geometric correction was necessary to project multispectral imagery in UTM 6 South within RGPF local datum. For the sake of standardization and further comparison, a radiometric correction was carried out. It firstly converted top-of-atmosphere (TOA) digital number into TOA radiance using calibration factors, secondly TOA radiance into down-of-atmosphere (DOA) radiance by compensating for atmosphere absorption, and thirdly DOA radiance into DOA reflectance by sun irradiance normalization.
In parallel, a sonar campaign, aiming at calibrating-validating a depth model, was conducted, from September, 25 to 26, 2016, to acquire water depth soundings over both lagoon and ocean parts (Fig. 2a ). Fieldwork week was close enough to imagery date to be confident with an absence of bathymetric disturbances between two periods. An array of 276 082 geolocated measurements was compiled across 104. 4 km over 16 h 44 min recordings. The dataset ranged from 0. 54 m to 180. 85 m with an average of 9. 90 m ± 10. 02 m. The sonar device, installed on a fishboat, was a 200 kHz echosounder Lowrance HD7 leveraging a 10 Hz GPS unit. Water level driven by tide was corrected using the nearest French Marine gauge in Rangiroa atoll. The bathymetry has been extracted based on the optical law, showing that electro-magnetic radiation is absorbed by water in respect to the wavelength.
Acquisition and analysis of biologic data The study site is located at the south end of the atoll, close to the village of Fakatopatere and profile T23. Digital images of the quadrats were acquired using a SLR Nikon D300s body camera with aquatica housing and equipped with a Nikkor 15 mm (AF 15 mm f / 2. 8) lens and two SB 105 Nikonos strobes. Pictures were taken at -5 m, -10 m, -15 m, -20 m, -25 m, and -30 m depth running downslope. At each depth site, 4 pictures were taken perpendicular to the bottom surface with a frequency of 1 picture every 30 seconds by a scuba diver swimming at low speed (1 m per 5 sec). Reef bottom to camera lens distance was approximately 5 m. Digital pictures were processed on computer screen using the point intercept method (50 points-picture). Frequency of coral/non-coral substrate was assessed on each picture by identifying the benthic category present under each point.
Acknowledgments The present work was supported by the  French national research agency (CNRS)  under the  STORISK research project  (N o  ANR-15-CE03-0003 ) coordinated by  Virginie Duvat. Field  work has benefited from logistical support and help from the research station of the Office of Marine Resources in Takapoto, Gaby  Maiti Haumani. Bathymetric , satellite and echo-sounder-based records were provided by  AC  and treated by DJ. Data on the composition and distribution of coral communities were provided by  GS  and  MT , and exploited by YC. LM, the corresponding author, analysed.
9-1 Ma and formation I is dated to the Late Saalian-Eemian glacial-interglacial cycle). The rich large mammal faunal assemblage recovered from the White marl at Carpentier quarry clearly characterizes a temperate interglacial period and can be biostratigraphically correlated to a Late Cromerian interglacial  25. Combined with the geochronological data obtained by both ESR-quartz (n = 6) from the sandy lenses included in the fluvial deposit of the White marl and ESR/U-series on cervid teeth (n = 2) (mean age of the whole set of data: 584 ± 48 ka), this interglacial stage can be allocated to the Cromerian III Interglacial of the formal north-western European stratigraphy and correlated with MIS 15  30. The White marl from Carpentier is thus one of the best-dated series from the Cromerian III interglacial in Western European river systems and represents a fundamental cornerstone for the interpretation and dating of the Moulin Quignon sequence and more generally for the Quaternary terrace system of the Somme River.
electron spin resonance (eSR) dating. Three samples of sandy sediments were extracted during the 2017 archaeological excavation from the main stratigraphic units and in situ gamma-ray measurements were performed in each sampling hole (Fig. 4 ). Details of the Electron Spin Resonance (ESR) dating method applied on sedimentary quartz are given in the Method part. The results obtained for the three samples from Moulin Quignon, using the aluminium (Al) and lithium titanium (Ti-Li) centres of quartz, are consistent with each other (overlapping of uncertainty domains at 2 σ) (Supplementary Table  S1 ). This consistency makes it possible to combine the results and calculate a weighted mean age for each sample using the Isoplot 3. 0 software  32. For samples MQ17-01 and MQ17-03, from the fluvial sand layer (Fs) overlying the main gravel body (Grs-j), the results are respectively 686 ± 58 ka and 650 ± 37 ka (Table  1 ).
The following ESR acquisition parameters were used: 5 mW microwave power, 1024 points resolution, 20 mT sweep width, 100 kHz modulation frequency, 0. 1 mT modulation amplitude, 40 ms conversion time, 20 ms time constant and 1 scan. The signal intensity of Al-centre was measured between the top of the first peak at g = 2. 018 and the bottom of the 16th peak at g = 2. 002 of the Aluminium hyperfine structure  49. Equivalent doses (D e ) were determined from the obtained ESR intensities vs dose curve using an exponential + linear function  50  using Microcal OriginPro 8 software with 1/I² weighting. In the age calculation, the dose rates d a were calculated from the radionuclide contents U, Th, K) of the sediments taking into account in situ gamma-ray measurements performed with an Inspector 1000 (Canberra) gamma spectrometer and the location of the samples in the stratigraphic sequence.
Acknowledgements This study received financial support from the  French ANR Program "PremAcheuSept"  for the research at  Carrière Carpentier  ( 2010-1014 ). The authors also warmly thank: (1) The municipality of Abbeville for material assistance during the test-pit campaign and the archaeological excavation, and especially the Mayor,  Nicolas Dumont , who granted us access to the site. (2) The  Regional Archaeological Service of the "Région Hauts de France " for administrative authorization and co-funding of the archaeological excavation. (3) The  MNHN-Paris , department of collections for access to the old collections. (4)  Dr. Amélie. Vialet  ( MNHN ) and all the students of the MNHN for their participation in the excavation, (5)  Prof. Ariane Burke  ( Univ. de Montréal ) for her comments on the first version of the manuscript, and (6) the four anonymous reviewers who helped to improve the quality of the manuscript.
Materials and Methods From February to April 2017, a cross-sectional survey was performed in Soc Son, Hanoi-a rural district where the population is about 316,000, with 523 older people who (1) were aged from 60 years old or older, (2) were able to understand and answer the questions, and (3) were willing to enroll in the study. These individuals were randomly selected from a list of older individuals residing in the study setting. After selecting the participants, with the local health professionals' support, we contacted and invited them to go to the commune health centers near their living locations for the face-to-face interview. Collecting data via interviews was meant to ensure a high response rate and better quality of data, especially since the participants were older people from rural areas.
Each interview was conducted in 20-25 min by well-trained undergraduate medical and nursing students at the Hanoi Medical University. We developed a structured questionnaire to collect the data about: Socioeconomic information: Socio-demographic and behavior characteristics included age, sex, occupation (retired/freelancers/farmers/others), having caregiver or not, who the caregiver was, poor status (based on Government classification  [11] ), and having health insurance or not (the current coverage of health insurance in Vietnam is about 80%). Risk for depression: The Geriatric Depression Scale-4 items (GDS-4), which has been used widely among Vietnamese population but was not published, was used to assess the risk for depression among our participants: (1) Are you basically satisfied with your life? (Yes/No), (2) do you feel that your life is empty? (Yes/No), (3) are you afraid that something bad is going to happen to you? (Yes/No), and (4) do you feel happy in most of your life? (Yes/No).
Data analysis: p-value less than 0. 05 was used to define statistical significance. Chi-squared test and T-test were employed to measure the differences in socio-economic status, health status, behaviors, and health service utilization between people at risk for depression and their counterparts. Multivariable Logistic regression was used for determining factors that were associated with the risk for depression. The regression model was combined with stepwise forward selection strategies, using the p-value of log likelihood of less than 0. 2 for selecting variables, to build the final reduced model. Data were analyzed using STATA 12. 0 software (Stata Corp. LP, College Station, TX, USA). Ethical approval: The study protocol was received approval from the IRB of National Geriatric Hospital (Reference: No 35; 16 January 2017).
According to grounded theory, the empirical database was obtained from multiple sources. This included the collection and analysis of documentary material found on the luxury brands' online presence and in the business press (see also  Fionda and Moore, 2008; He and Balmer, 2013). Based on the principle of purposeful sampling  (Corbin and Strauss, 2008) , we also conducted 15 semistructured interviews with sales associates in stores of the selected brands in Shanghai (ten to 20 minutes each; 12 respondents were female). We decided to target sales associates because they gain first hand insights into CoO preferences of Chinese consumers and may use CoO cues in their selling techniques. The interviews focused on i) how the CoB was perceived by the brand representatives and consumers and ii) which foreign CoO cues sales associates use to improve the brand image.