dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors acknowledge infrastructure support from the  Imperial Experimental Cancer Medicine Centre ,  Cancer Research UK Imperial Centre , the  National Institute for Health Research Imperial Biomedical Research Center , and the  Ovarian Cancer Action Research Centre. The Generations Study is funded by  Breast Cancer Now  and the  Institute of Cancer Research. The Institute of Cancer Research  acknowledges funding to the  National Institute for Health Research Biomedical Research Centre. We thank  Breast Cancer Now  for funding this work as part of Programme Funding to the  Breast Cancer Now Toby Robins Research Centre. The authors would like to thank the study participants, study staff, and the doctors, nurses, and other healthcare staff and data providers who have contributed to all cohorts.
EPIC Italy was supported by the  Italian Association for Research on Cancer (IARC). The Melbourne Collaborative Cohort Study (MCCS) cohort recruitment was funded by  VicHealth  and  Cancer Council Victoria. The MCCS was further augmented by  Australian National Health and Medical Research Council  grants  209057 ,  396414 , and  1074383  and by infrastructure provided by  Cancer Council Victoria. Cases and their vital status were ascertained through the  Victorian Cancer Registry  and the  Australian Institute of Health and Welfare , including the  National Death Index  and the Australian Cancer Database. The MCCS methylation work was supported by the  National Health and Medical Research Council  (grant number  1011618 ) and the  Victorian Breast Cancer Research Consortium. The work performed by the  Epigenetics Group at IARC  was supported by grants from the  Institut National du Cancer (INCa, France) , the  European Commission (EC)   Seventh Framework Programme  ( FP7) Translational Cancer Research (TRANSCAN) Framework , the  Fondation ARC pour la Recherche sur le Cancer (France) ,  and la Ligue Nationale (Française) Contre le Cancer  to ZH.
17 0. 04 4. 41E-05 4. 06E-02 cg13971030 chr11 35366721 SLC1A2 0 -0. 49 0. 14 9. 19E-06 2. 41E-02 cg17567562 a chr3 47687980 SMARCC1 0 -0. 48 0. 11 1. 18E-05 2. 66E-02 cg10298859 chr13 112883993 SPACA7 146656 -0. 20 0. 04 2. 26E-07 5. 26E-03 cg19216791 chr19 5568216 TINCR 210 -0. 25 0. 07 4. 13E-05 4. 02E-02 cg25936380 chr2 120981591 TMEM185B 606 -0. 29 0. 07 2. 22E-07 5. 26E-03 cg01824466 a chr8 95959531 TP53INP1 0 -0. 23 0. 08 8. 08E-05 4. 96E-02 cg26657235 chr6 150378972 ULBP3 4367 -0. 12 0. 03 1. 32E-05 2. 76E-02 cg08551047 chr15 91473569 UNC45A 0 -0. 58 0. 14 2. 87E-06 1. 46E-02 cg20394620 a chrX 48541924 WAS 260 -0. 20 0. 05 3. 02E-06 1. 46E-02 SE standard error a Marked probes did not pass quality control in the targeted sequencing data and were not included in the analysis of the methylation index b Result from the EWAS of ELEE in EPIC-Italy (n = 216, dataset 2).
1c, d ). Once we had determined the optimum pretreatment protocol for <100 mg material, we applied this to three more archaeological samples: R-EVA 1489, R-EVA 1905 and R-EVA 1860 (two extracts per bone) (pretreatment information shown in Supplementary Dataset S1). The standard practice in our lab is to extract large bone aliquots (ca. 500 mg material) as a solid piece. Although this method requires a large time investment (demineralisation can take up to four weeks with the HCl 0. 5 M changed twice per week), we observe much higher collagen yields using this technique compared to powdered extracts of equal starting weight. Small aliquots (<100 mg) of the test bones were initially pretreated as both fine powder and as solid chunks. For solid pieces of bone, in most cases the collagen yield from small extracts (<100 mg) equalled or exceeded the collagen yields of large extracts (500-700 mg material) and no difference was observed between aliquots of 50 mg bone compared to 70 mg or 100 mg bone material (Fig.
1 ): step (1) the duration of the demineralisation stage; step (2) the strength of HCl during the demineralisation stage; step (3) the temperature and duration of the gelatinisation stage. Bone collagen yields along with elemental (C%, N% and C:N) and stable isotopic data (δ  13  C and δ 15 N) were used to evaluate the extracts from the different methods. In addition, Fourier Transform Infrared Spectroscopy (FTIR) was used to double check the preservation of the extracted collagen, and to detect Step 1: duration of the demineralisation stage. Step 2: strength of HCl during demineralisation. Step 3: duration and temperature of the gelatinisation stage (HCl pH3). In (a-d) the horizontal grey line shows the collagen yield from a large aliquot (>500 mg material) of the same bone. A higher number of data points are present for R-EVA 1753 (e) as an aliquot of this bone was extracted alongside each batch of samples.
The horizontal grey band in e shows the range in collagen yield of repeated large extractions from the background bone. The dashed lines at 1% show the guideline minimum requirement for reliable  14  C dating. Asterisks mark extracts which were dated using the gas ion source (see Fig. 3 ). the presence of possible carbon contaminants  31, 48, 49. Detailed pretreatment information for all extracts can be seen in Supplementary Dataset S1. For the poorly preserved bones (Fig. 1c : R-EVA 570 and Fig. 1d : R-EVA 548) the pretreatment was softened in order to minimise collagen loss during the extraction. The weaker HCl (0. 2 M) (step 2) and lower gelatinisation temperature (60 °C) (step 3) required a greater time investment and did not necessarily increase the yield of collagen compared to using stronger acid (HCl 0.
For all bone samples >30,000 BP, solubilisation occurred in <6 h (Fig. 1 ), whereas R-EVA 1489 and R-EVA 1905 required up to 27 h for full solubilisation (Supplementary Dataset S1). Of the extracts dated, two (R-EVA 548. 13 and R-EVA 548. 14) fell close to or under the minimum threshold (1%) for reliable  14  C dating (Supplementary Dataset S1). There were small variations in elemental values between pretreatments of the same bone but all values (Supplementary Dataset S1) fell within the accepted ranges of 'well-preserved' collagen  32. The stable isotopic values were in keeping with the palaeodietary expectations for each animal and were consistent between extracts. Analysis with FTIR was performed for all collagen extracts; each extract dated had a spectra characteristic of well-preserved collagen when compared to library spectra (see  ).
Dates >15,000 BP have been rounded to the nearest 10 years. Asymmetrical errors are shown where F14C ≤ 1σ*10. Grey shaded bands show the 1σ range of graphite dates measured from large extracts of the same bone. In a-h, the vertical dotted lines separate different collagen extracts of the same bone with the bone starting weight and collagen yield shown below. The number in the top left of each section is the preparation number of the bone, corresponding to Supplementary Dataset S1. Asterisks mark collagen extracts dated with the gas ion source reported in Fewlass, et al. 29. Supplementary Fig. S3 ). Considering the collagen yields and  14  C measurements, the optimum pretreatment protocol for small aliquots of bone (<100 mg) is shown in Fig. 2. 14 C dating. For each of the bones, several collagen extracts (bone weight ranging from 37-100 mg, marked with asterisks in Fig.
1 ) were dated using the EA-GIS-AixMICADAS (Fig. 3 ). Each collagen extract was split and measured multiple times. Between two and four replicates were measured containing ca. 30-40 μg C, run for the duration of one titanium (Ti) target (ca. 12 minutes) and for each bone >20,000 BP, a single aliquot containing ca. 80-90 μg C was measured over the duration of three targets to increase precision (see Supplementary Dataset S2). The gas ages obtained were compared to one or more graphite dates measured from collagen extracted from 500-700 mg bone material (Supplementary Dataset S2). Discussed here are measurements made from collagen extracted from solid pieces of bone. Details of measurements made from powdered aliquots (lower collagen yields) are included in the supplementary information. Figure  3  shows the ages obtained for each bone.
The replicate measurements are statistically indistinguishable for R-EVA 1489, R-EVA 1905, R-EVA 1860, R-EVA 123, R-EVA 570 and R-EVA 124 (output of all statistical tests are included in Supplementary Dataset S2), demonstrating the reproducibility of the measurements and consistency between different pretreatment batches across the range of the 14 C timescale. In addition, all of the measurements of R-EVA 1489, R-EVA 123 and R-EVA 124 from this study agree with the EA-GIS-AMS measurements made in 2016 reported in Fewlass, et al. 29  (Supplementary Dataset S2). The exception is the roughly 40,000 year old bone R-EVA 548, which at ca. 1% collagen preservation represents the limits of C 14 dating. The gas dates obtained from the two low yield extracts (R-EVA 548. 13 and R-EVA 548. 14) were much younger than the other extracts of this bone (Fig.
3g ), showing they had been affected by contamination from modern carbon. Due to the low yield, under normal circumstances R-EVA 548. 13 would not have been passed for dating following pretreatment. Excluding these two extracts, the replicates from R-EVA 548. 3 and R-EVA 548. 8 are consistent with the graphite date for this bone. For background bone R-EVA 1753 (>50,000 BP), the dates from the collagen extracts (Supplementary Dataset S3) were on par with the blank standards (IAEA-C1/phthalic anhydride) of equal size (Supplementary Dataset S4). As expected, the blank level in the EA-GIS system was affected by the reduction in sample size from 90 μg C to 30 μg C (Fig. 3a ). The ages of the seven <50,000 BP samples were corrected with background collagen measurements of the same size (ca. 30 μg C or ca.
30 µg C or ca. 90 µg C) prepared alongside every batch of samples and measured during the same measurement session to account for any C added during sample preparation and measurement. Figure  3a  shows the ages obtained for the background bone containing ca. 25-40 μg C (small) and ca. 80-100 μg C (large). The large measurements (mean F 14 C = 0. 0024, SD = 0. 0006, n = 9, equivalent to 48,600 BP) are on par with the system blank (either IAEA-C1 or phthalic anhydride) measurements of equal size (mean F 14 C = 0. 0026, SD = 0. 0006, n = 7, equivalent to 48,000 BP) (Supplementary Datasets S3 and S4), indicating that no carbon contamination was introduced during sample preparation. An increased sensitivity to modern  14  C is to be expected at lower levels of carbon and it is clear that the smaller background collagen measurements are generally younger.
The 25-40 μg C background collagen samples (mean F 14 C = 0. 0039, SD = 0. 0007, n = 22, equivalent to 44,530 BP) are likewise equal to the system blank measurements of equal size (mean F 14 C = 0. 0036, SD = 0. 0006, n = 5, equivalent to 45,180 BP) (Supplementary Datasets S3 and S4). These values are lower than previously published values for blank IAEA-C1 samples measured at CEREGE reported in Bard, et al. 30  (F 14 C = 0. 02 for sample sizes around 30 µg C and F 14 C = 0. 005 for samples of 80-100 µg C) and to phthalic anhydride blanks measured at ETH Zurich reported in McIntyre, et al. 24  (mean F 14 C = 0. 0046 ± 0. 0012, n = 6, size range 84-100 μg C). The results indicate the lower limit of  14  C detection with the gas ion source to be around F 14 C = 0. 004. As demonstrated by R-EVA 124, beyond this limit the minute levels of  14  C can be measured but the uncertainty of the background correction dominates accuracy and precision.
Although the measurement of gas samples requires more supervision than graphite targets, the direct coupling of the EA with the GIS significantly reduces sample preparation time by cutting out the graphitisation step which poses a large risk of contamination at such small sample sizes. Therefore in situations where sample size is limited the gas ion source offers an attractive solution for archaeological, as well as environmental, applications. Even working with the assumption of 1% collagen preservation, in theory sufficient collagen could be extracted from less than 10 mg bone material to obtain a 14 C date using the EA-GIS-AMS. However in order to assess the quality of the extract prior to dating and obtain high-resolution stable isotopic data for palaeodietary reconstruction, collagen should also be analysed with an EA-IRMS.
55  ). Low energy ion currents for the gas analyses were in the range of 10-15 μA. BATS 58 was used for data reduction. The uncorrected collagen background (R-EVA 1753) measurements of the corresponding type (piece/powder) and equal size were used to correct the archaeological samples measured in the same session (i. e. 'small' sample aliquots were corrected only with 'small' background collagen samples). For all samples, the long term standard deviation of blanks (F 14 C = 0. 001) was used as the absolute blank error and an external error of 3. 5‰ was added to take into account the long-term variability of standards ('error 2' described in Fewlass, et al. 29  ).
For all gas measurements in graphs b-h: the absolute error of the blank has been set to 0. 001 and an external error of 3. 5‰ has been added to all measurements based on the long term standard deviation of standards. Dates >15,000 BP have been rounded to the nearest 10 years. Asymmetrical errors are shown where F14C ≤ 1σ*10. Grey shaded bands show the 1σ range of graphite dates measured from large extracts of the same bone. In a-h, the vertical dotted lines separate different collagen extracts of the same bone with the bone starting weight and collagen yield shown below. The number in the top left of each section is the preparation number of the bone, corresponding to Supplementary Dataset S1. Asterisks mark collagen extracts dated with the gas ion source reported in Fewlass, et al. 29.
Acknowledgements We would like to thank the  Max Planck Society  for funding this research. The AixMICADAS was acquired and is operated in the framework of the  EQUIPEX  project  ASTER-CEREGE  ( PI E. Bard ) with additional matching funds from the  Collège de France , which also supports the salaries of the authors from CEREGE. We are indebted to  Dr. Raquel Silva Maria  and  Sven Steinbrenner  of the  Department of Human Evolution  at the  MPI-EVA  for technical assistance. We give kind thanks to  Caterina Pangrazzi ,  Jordi Rosell ,  Ruth Blasco ,  Marcel Weiss  and  Andrzej Wisniewski  for access to samples during the course of the study.
When facing such organizational practices and policies, it is difficult to choose when the two goals are not fully congruent. For example, some scholars have contended that organizations engaged in CSR initiatives (such as volunteering programs), may be characterized by vague goals and competing interests, which may lead to employee confusion about which goals they should prioritize first  [39, 40]. Relatedly, based on data from women's clinics, Merrell  [41]  found that volunteers within formal organizations are likely to experience ambiguity, as roles and expectations may not normally be clearly differentiated. Second, SRHRM may confuse employees on how to work. SRHRM not only adds to new work content, but also calls for a new way of finishing tasks, such as improving job efficiency and producing environmental products.
Second, human resource managers connected with line managers and asked them, with a list of names, to randomly select one-four subordinates who could participate. Line managers were also asked to evaluate the selected employees' performance in the supervisor questionnaire. After that, we obtained a list of all employees who could participate in the survey. Third, with the help of human resource managers, all participants convened in a conference room. They were informed of the purpose of the survey and assured of the confidentiality of their responses. Employees provided data for their demographic variables, SRHRM, role ambiguity, and prosocial motivation, while their supervisors were asked to rate their subordinates' task performance and OCBs. We assigned an identification number to each questionnaire in order to match subordinates with their supervisors' evaluations.
Estimation Method We first conducted the confirmatory factor analysis (CFA), a method used in data analysis to examine the expected causal connections between variables  [73] , to evaluate the discriminant validity of the key variables. Then, we tested our hypotheses by structural equation modeling (SEM) analysis with the Mplus 7 software package, with all latent variables indicated by their observed items. SEM analysis allowed us to simultaneously test the effects of the independent variables, mediator, and moderator in the context of comprehensive models, as suggested by Edwards and Lambert  [74]. In particular, the direct effects of SRHRM on the two performance outcomes and the mediator were tested by structural equation analysis. For testing the indirect effects, a Monte Carlo method simulation computed with 95% confidence intervals was applied.
In other words, spikes in fertilizer prices increase the risk of conflict through their effect on the level of rents and on the dispersion of rents across groups (i. e. rent inequality). We also show that the positive link between fertilizer price variations and conflict is magnified when natural soil quality is more heterogeneous. In sum, our model generates two testable predictions: a) variations in fertilizer prices are positively associated with the likelihood of conflict, b) this association is stronger when natural soil fertility is more heterogeneous. We test these predictions using a dataset in which the units of analysis are cells of 0. 5 × 0. 5 degree latitude and longitude (approx. 55km × 55km at the equator) covering all of Sub-Saharan Africa (ssa) from 1997 to 2013. Testing our theory requires data on (i) conflict events, (ii) soil fertility, and (iii) local variations in fertilizer prices.
3 First, we combine original geo-localized datasets on soil characteristics with information on conflict occurrence. The Armed Conflict Location Events Data (acled) provides detailed information on the date, location and type of conflict events, and can be used to identify subsets of events that are more likely to be land-related. For each cell, we complement this conflict data with measures of the natural fertility of soils, defined as their inherent nutrient content. These measures come from the Harmonized World Soil Database and are available at a highly disaggregated level; this allows us to calculate our measure of land inequality, as the variance of natural soil fertility within each cell. The final variable we construct is the local price of fertilizers. Direct information on timevarying prices at a spatially disaggregated level is not readily available.
Concerning the implication (a), existing papers find at most weak support for this relationship  (Sheahan and Barrett, 2014, 2017)  but they use measures of self-assessed, perceived soil quality. Combining geo-coded data from the World Bank lsms surveys with our measure of nutrient availability (see section 3. 3), we find stronger support for our assumption, though the results are sensitive to the specification (online appendix section D. 1. 1). In the case of the implication (b), we find that our cell-specific measure of fertilizer prices correlates significantly with agricultural yields at the aggregate and local levels (online appendix section D. 1. 2). We use several measures of yields or agricultural output: a sub-country measure from fao-agromaps, a cell-level proxy based on a standard vegetation index (Normalized Difference Vegetation Index, ndvi) and household-plot level information from the World Bank lsms surveys.
Unit of analysis Our units of analysis are cells of size 0. 5×0. 5 degrees latitude and longitude (around 55×55 kilometers at the equator), covering the entire set of ssa countries. Most of the data we use throughout the paper are available at a more disaggregated level. For this reason, we aggregate the data in order to generate a dataset at the cell-year level. We use this level of aggregation rather than administrative boundaries in order to ensure that our unit of analysis is not endogenous to conflict events. We assign a country to each cell based on the end-of-period boundaries. Note that we check that our main results are not sensitive to our choice of spatial scale, by reproducing our analysis using 0. 25×0. 25 and 1×1 degree cells.
Conflict data We use conflict event data from the Armed Conflict Location and Event dataset (acled) which contains information on the geo-location of conflict events in all African countries over the period from 1997 to 2013  (Raleigh and Dowd, 2014). These data, that have been widely used in recent conflict literature, contain information about the date, the location (longitude/latitude) of conflict events within each country, and the nature of the actors on both sides of the conflicts. Events are compiled from various sources, including press accounts from regional and local news, humanitarian agencies, and research publications. Geographic precision specifies at least the municipality level in more than 95% of cases, and is even finer (i. e. village level) for more than 80% of observations.
For each data source, we aggregate the data by year and by 0. 5×0. 5 degree cell. 18 We focus on Sub-Saharan African countries only; North African countries possess significant reserves of phosphate (in particular Morocco and Western Sahara), which would affect our identification strategy. We construct a dummy variable equal to one if at least one conflict occurs in the cell during the year; we interpret this variable as cell-specific conflict incidence. As an alternative measure of conflict, we compute a variable containing the number of events observed in the cell during the year. Note that our results are also robust to modeling the onset and ending of cell-specific conflicts separately. Figure  1. A provides a visual representation of our data across cells (a larger version appears in the online appendix section A.
7, figure  A2 ). Events are observed over the entire Sub-Saharan African continent, with some clusters appearing in the Great Lakes Regions, Nigeria, and West Africa. A unique feature of the acled dataset is that it includes information about the characteristics of the actors on both sides of the conflicts as well as -for a large subset of observations -a description of the event. As an alternative measure of conflict incidence, we make use of the richness of the acled dataset in order to identify conflicts that are likely to be land-related as well as those that are likely to have occurred between local actors. We thus define "land conflicts" as events (i) involving communal militias (i. e. local armed groups, fighting for a local objective) on both sides and/or (ii) whose description include specific keywords related to land.
19     in the online appendix (section A. 7) shows the spatial distribution of our measure of land-related conflicts. These conflicts tend to be geographically concentrated, and mostly occur in East Africa, which illustrates a limitation of our measure of land-related conflicts. Specifically, the measure relies on the event description provided in the acled data, for which the methods and quality of coding vary widely across countries. In some countries, the description may be absent or very short, or may use a different terminology, which may have prevented us from identifying potential land-related events. For this reason, we use the broader measure of violent conflicts described above in our baseline, and focus on land-related events for comparison purposes. Note that the acled dataset does not include information on who attacked in the first place.