dataesr/scientific-paragraphs-categorization huggingface dataset:
122    For these reasons, Wallace and Singer's 'Correlates of War' project incorporated additional data from many other sources. Yearbook data was again verified and complemented with other sources when this dataset was expanded in the early 2000s. 123  Despite the fact that, until the 2000s, scholars relied on Yearbook data to study shared membership in IGOs after 1964, 124 quantitative indicators about IGOs have usually been built from the expanded and corrected data of the 'Correlates of War' project. The situation is quite different in the literature on INGOs, where Yearbook data reigns supreme. John Boli and George Thomas's forged the crown with their original statement that the Yearbook data was 'adequate for meaningful analysis' because of semi-official status of the Yearbook, continuous contact with 13,000 organizations, and annual revision and addition of information by UIA staff.
Local perceptions of the drivers of livelihood strategies Fieldwork was conducted between February and May 2016 to identify indicators that stakeholders, experts and local residents perceive as representative and robust to examine the effects of community and household capitals on their livelihoods. A Rapid Rural Appraisal (RRA) was used for data collection to highlight the perceptions and opinions of rural dwellers (Supplementary Material S1). This method enables local people to share their knowledge and discuss their situation using their own terms  (Chambers, 1994). In total, ten communities were selected by using stratified random sampling based on their access to community capitals and on the main livelihood activities conducted by households (Fig. 2 ). A variety of additional activities were used to cross-check the data acquired from the RRA.
Developing community typologies Every community has common-pool resources (i. e. road, market, forest, lake) that can provide services for rural dwellers' livelihoods. For example, a road can provide farmers with alternative outlets for their agricultural production, while a forest can give the opportunity for households to collect and sell non-timber forest products. Such common-pool resources appear together repeatedly in the landscape, creating bundles of community capitals  (Bański & Mazur, 2016). We used cluster analysis on 18 variables derived from open source data to generate community typologies. Indicators were selected based on participatory rural appraisals conducted in ten communities located across the Mahanadi delta. Participants argued that remoteness plays an important role in their access to community capitals, and thus in their choice of livelihood strategy.
As a consequence, we used travel time to key amenities rather than amenity availability to reflect community remoteness in the cluster analysis. Euclidean distances are inappropriate for this purpose as the Mahanadi delta has several water bodies, which act as boundaries to travel. We thus estimated accessibility to key amenities by creating a least accumulative cost surface to estimate time (in hours) to travel from each community to the nearest amenity of interest, using the R package "gdistance" (van Etten, 2017).
Estimating accessibility We downloaded road data from OpenStreetMap, using the R package "osmdata"  (Padgham, Rudis, Lovelace, & Salmon, 2017). Roads were converted to a raster with 30 m spatial resolution and merged with 30 m spatial resolution land cover data from 2010 Glo-beLand30  (Chen et al. , 2014). Based on a previous study in India  (Watmough, Atkinson, Saikia, & Hutton, 2016) , average speeds were assigned to each land cover class (Table  1 ) and were based on travel by foot across land covers and footpaths and travel by motorised vehicles on other forms of road and track.
Class Estimated speed (min. km OpenStreetMap data. We used 2010 MODIS data at 250 m spatial resolution to obtain a land cover dataset detailing the different types of cropping systems found in the delta  (Gumma et al. , 2014). Travel costs to the nearest amenity of interest were computed from the least accumulative cost surface dataset mentioned earlier. In situations where multiple indicators for the same service were found (type of education or health facility), we favoured the indicator that exhibited the greatest variation among communities. Based on the results from RRA (S1), travel times to six types of amenities were chosen to reflect access to social services: public services and polling stations, secondary schools, banks and credit cooperatives, hospitals, worship temples and recreational areas, such as sports centres and playgrounds.
Three amenities were used to reflect access to productive infrastructures: travel time to communication services, agricultural outlets and industrial areas. Availability of public transport was also chosen to represent productive infrastructures, as they can be used by smallholders to access agricultural markets. Eight variables were chosen to reflect the natural resources from which most households derived their incomes, seven of which were derived from satellite sensor data and one from Open-StreetMap data (Table  2 ). The variables to reflect the natural resources included: the area of forest, the area of cropland available per household, the type of agricultural system (based on the proportion of each cropping pattern within the community) and the travel time from each community to the nearest aquaculture ponds.
Each cluster is modelled by the Gaussian distribution and is characterised by its mean vector, covariance matrix and the probability of each point belonging to this cluster. These parameters are estimated using the Expectation-Maximisation algorithm, which is initialised by hierarchical model-based clustering. The covariance matrix determines the geometric shape of each cluster, the latter being centred at the mean, around which there is an increased density of points. The model with the greatest integrated likelihood, or Bayesian Information Criterion (BIC), is considered as the best fitting model. We used the R package "mclust"  (Fraley, Raftery, Murphy, & Scrucca, 2012)  to implement the model-based clustering algorithm, which estimated the best finite mixture model according to different covariance structures and different numbers of clusters.
Proxying climate shocks Extreme events, such as heat waves, droughts, floods and cyclones are becoming more frequent and both their frequency and intensity are likely to increase in the future  (Baker et al. , 2018). Extreme weather events can result in agricultural losses, which can lead to shifts from transient to chronic poverty  (Krishnan & Dercon, 2000). Decreases in agricultural production can be identified by remotely sensed satellite sensor data in the form of abrupt changes in vegetation greening  (Liu,  Table  2  Variables used for community typologies. Indicators for social services are based on travel times to the closest service found by using a least accumulative cost surface dataset, computed from road networks and land cover data. Natural services are derived from agricultural-relevant metrics from land cover data.
For each community type, a two-level random intercept model was fitted using the R package "R2MLwiN" (Charlton, Rasbash, Browne, Healy, & Cameron, 2017): = - = + + + + + + + + + π π π β β β β β β β β β β logit ( ) log( 1 ) District PopDensity SCST Breaks Natural Physical Human Financial Social ij ij ij j ij ij ij ij ij ij ij ij ij Cluster 0 1 2 3 4 WDRVI 5 6 7 8 9 where π ij refers to: the probability of being engaged in precarious livelihoods (unemployment and agricultural labour) for the village i in the Tehsil j. Each level 1 unit (village) had an associated denominator n i , which was the total number of people of working age (every person aged 15-59. Two sets of explanatory variables were considered: livelihood capitals and the number of breaks in the WDRVI time-series, as a proxy of the number of crop failures.
Acknowledgement This work was carried out under the  Deltas, vulnerability and Climate Change: Migration and Adaptation (DECCMA)  project under the  Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA)  programme, with financial support from the  Department for International Development (DFID)  ( United Kingdom) , the  International Development Research Centre (IDRC) (Canada)  [grant number  107642 ] and the  Economic and Social Research Council (ESRC)  ( United Kingdom)  [grant number  1501613 ]. The views expressed in this work are those of the creators and do not necessarily represent those of  DFID ,  IDRC ,  ESRC  or its Boards of  Governors. Prior  to commencing the study, ethical clearance was obtained from the  University of Southampton  [ERGO number 15234]. Data used in this research come from the  Census of India  provided by the  Office of the Registrar General & Census Commissioner of India.
2d ). Hence, to improve statistical power and expand on Nanog targets we combined the SunTag and 44iN datasets to test for those genes with coherent Nanog response across both systems (Fig. 2e  and Supplementary Fig. 2D ). Combining with those genes already identified, this resulted in 457 genes, which generally display extremes of expression differences between Dox-treated SunTag (high Nanog) and untreated 44iN cells (low/absent Nanog; Fig. 2e  and Supplementary Fig. 2D ); they globally behave in a concordant way when long-term Nanog-null cells are compared to wild-type cells (Supplementary Fig. 2D ), or when their expression is analysed in published data sets (Supplementary Fig. 2E ). Genes activated by Nanog are enriched in regulators of stem cell maintenance (FDR < 2. 99e-16), while repressed genes are enriched in differentiation processes such as nervous system development (FDR < 6.
Establishment of Parental SunTag ES cells: Subconfluent E14Tg2a cells were transfected with 5 μL of Lipofectamine 2000 (ThermoFisher), 0. 8 μg of PB-CAG-rtTA, 0. 8 μg of dual SunTag PiggyBac vector and 0. 4 μg of the PBase vector. Upon Doxycycline treatment (1 μg/mL) the cells were selected with Hygromycin B for 10 days. Single clones were manually picked and expanded in absence of Doxycycline and Hygromycin B. After expansion and stock freezing, induction of GFP/BFP expression was analysed for 6 clones on a LSR II Fortessa (Becton-Dickinson). Data were analysed using the FlowJo software suite (Tree Star). The 2 clones showing the best percentages of GFP/BFP positive cells under Doxycycline treatment and an absence of fluorescent signals in absence of Doxycycline were kept for further experiment (C1 and C2).
The karyotype of C1 and C2 cells were established using colcemid arrest (4 h; 100 ng/ml -1 ; Gibco, 15212-012), hypotonic shock (NaCitrate 0. 017 M, KCl 0. 03 M) and cold acetic acid-methanol (1:3) fixation at 4 °C. Fixed cells were spread by dropping on pre-heated glass slides, mounted (Vectashield; VectorLab, H1200) and imaged using a Nikon Eclipse X microscope equipped with: ×63 oil immersion objective (N. A1. 4); LUMENCOR excitation diodes; Hamamatsu ORCA-Flash 4. 0LT camera; NIS Elements 4. 3 software. Chromosomes number was then counted manually with NIS Elements 4. 3 software for a minimum of 20 randomly chosen cells per clone. gRNA design and selection: To design gRNAs targeting the SunTag system to the Nanog and Otx2 promoters, we first identified all the potential targets of 20 nucleotides preceding a 'NGG' protospacer adjacent motif (PAM) on both strands.
Coli RNAs at 1 μg/mL, 1 μL of Nanog probe at 20 pmol/μL). The slides were washed 3 times in 2× SSC 10% Formamide for 30 min at 37 °C and mounted in Vectashield medium with DAPI (Vector-abcys H-1200). Nanog pre-messenger probe was designed using Stellaris Probe Designer version 4. 2 on Biosearch Technologies website with the maximum masking level (5) and was synthetised by the same company. Image stacks (0. 5 μm gap) were acquired using a Nikon Eclipse X microscope equipped with: ×63 oil immersion objective (N. A1. 4); LUMENCOR excitation diodes; Hamamatsu ORCA-Flash 4. 0LT camera; NIS Elements 4. 3 software. The number of active transcription sites per cell was automatically counted with FISH-quant program  49. We used ImageJ software to measure the intensity of the signal at active transcription sites.
5 μL of LightCycler 480 SYBR Green I Master (Roche, 04707516001), 5 μL of sample and 0. 25 μL of each primer at 20 μM in a final reaction volume of 10 μL. Standard and melting curves were generated to verify the amplification efficiency (>85%) and the production of single DNA species. PCR primer sequences are listed in Supplementary Data 1. The 2dCt method was used both for ChIP and RT-PCR analysis. For the former, all values were corrected to the input; for the latter, Tbp was used to normalise the data. Chromatin preparation: Nanog SunTag cells (3. 10 7 ) used for H3K27me3 analysis, were resuspended in 3 ml PBS and crosslinked for 10 min at room temperature with 1% formaldehyde (Sigma F8775). Crosslinking was stopped with 0. 125 M glycine for 5 min at room temperature. 44iN cells (3. 10 7 ) used for TF binding profiling were crosslinked in 3 ml of freshly prepared PBS-DSG 2 mM at pH 7.
Adapters ligation was performed in a total volume of 25 μL (sample 19. 25 μL, NEB 10× T4 ligase buffer 2. 5 μL, 0. 2 μM adapter 1. 25 μL, NEB T4 ligase 2 μL) at 16 °C overnight. After DNA purification, DNA was amplified in a total volume of 50 μL (sample 19. 5 μL, Pico green 1 μL-1:10 in water; Life Technologies P7589-, 25 μL of KAPA HiFi HOTSTART Ready mix-NC0295239-, PCR 1. 0 10 μM 1 μL, PCR 2. 0, 10 μM 1 μL) on a LightCycler 480 (Roche). PCR are listed in Supplementary Data 1. sample reaching an absolute fluorescence value of 6 was taken out from the plate at the end of the last extension. Any library requiring more than 16 cycles of amplification to reach this level was discarded and reprepared. DNA was finally purified and the concentration was measured with a Qubit 4 Fluorometer (Thermo Q33226).
DNA sample and beads solution were mixed (0. 8 to 2:1 ratio) in the P96 well and left for 5 min and washed twice with 200 μL 70% EtOH. DNase-free water was added to the beads for DNA elution, with successive rounds of pipetting. After 5 min, the samples were repositioned on the magnetic plate for 5 min and the supernatant was collected. Informatic analyses. Data and availability: A summary of data collected and sequenced for RNA-seq, ChIP-seq along with public data used to identify Nanog binding regions is available in Supplementary Data 2. All data collected is available from GEO (GSE118898). Briefly, for Nanog induction RNA-seq between 2-4 replicates were sequenced per condition; for 44iN ChIP-sequencing we collected two replicates per factor for plus and minus Dox, and correspondingly 2 replicates per +/-Dox for ATAC-seq.
For H3K27me3 ChIP-seq we collected and sequenced 4 replicates per Lif/Dox condition, some of which were excluded due to quality issues (see Identification of H3K27me3 Domains). To identify Nanog binding regions we combined Nanog ChIP-seq from four independent studies: GSE56312 51 ; GSE11724 52 ; GSE44288 53 ; GSE55404  54. To compare to previously published Nanog targets, we used published microarray data  15, 24. Phf19 ChIP-seq data was obtained from GSE41589  48. ChIP-seq Data Processing: For all ChIP-seq samples (Nanog public datasets, 44iN+/-Dox, H3K27me3) reads were aligned with Bowtie2 55 in the mm9 genome, with options "-k 10" for all samples and additionally "-I 0 -X 1000-nodiscordant-no-mixed" for paired-end samples. Reads were additionally filtered for those with a single alignment (mapping quality: 255) and an edit distance less than 4 (mean edit distance for paired reads).
For Nanog and 44iN datasets distinct reads aligning with identical coordinates were treated as duplicates and collapsed to one. RNA-seq Data Processing: Stranded RNA-seq reads were aligned to the mm9 genome using STAR  56  and quantified by RSEM  57  using the RSEM-STAR pipeline, with additional options "-seed 1618-calc-pme-calc-ci-estimate-rspd-forwardprob 0. 0". ATAC-seq Data Processing: Paired end 65 bp ATAC-seq reads were trimmed by aligning read pairs to discover regions of reverse complementarity surrounded by Nextera sequencing adapters. Similarly to ChIP-seq processing, reads were aligned to mm9 genome using Bowtie2 55 with options "-k 10 -I 0 -X 1000-nodiscordant-no-mixed", and filtered for reads with a single alignment mean edit distance less than 4 between read pairs. Heatmaps and meta plots were generated by marking ATAC-seq cut sites, left-most and right-most coordinates of each read with shifted inwards by 4 bp, as recommended  50  , and base pair of the cut site and the two surrounding were marked.
Total cut-site signal was normalised for sequencing depth and averaged over replicates. Identifying Nanog binding regions from Public datasets: Six independent samples from four studies were combined, as specified above. Peaks were called against relevant inputs/controls for all samples using MACS2  58  with "callpeak -q 0. 2 -g mm", with the exception of ref. 54  in which controls were unavailable, in this case MACS2 was run without controls and peaks are called against a local background model. Peaks intersecting with the mm9 blacklist (ENCODE Project Consortium 2012) were excluded along with those on chrM and chrY. This resulted in 69,088 and 25,047 peaks for ref. 51  , 17,950 and 10,347 peaks for ref. 52  , 31,062 peaks for ref. 53  , and 27,888 peaks for ref. 54. Combined, this resulted in 85,697 candidate Nanog binding regions, which were further filtered to those occurring in at least two independent samples resulting in 39,164 peaks.
Acknowledgements We thank  Dr. Pentao Liu  and  Dr. Di Croce  for kindly providing PiggyBack plasmids and Phf19 knock-down vectors, respectively;  Dr. Domingos Henrique  for fruitful discussions regarding the involvement of Polycomb activity in Nanog function;  Carla Mulas  for critical reading of the manuscript. This work was supported by recurrent funding from the  Institut Pasteur , the  CNRS, and Revive  ( Investissement d'Avenir ;  ANR-10-LABX-73 ). P. N. acknowledges financial support from the  Fondation Schlumberger  ( FRM FSER 2017 ), the  Fondation ARC pour la recherche sur le cancer  ( ARC NAVARRO CA 14/12/ 2016 ), the  Agence Nationale de la Recherche  ( ANR 16 CE12 0004 01 MITMAT ), and the  Ligue contre le Cancer  ( LNCC EL2018  NAVARRO). V. H. is supported by the  Fondation ARC and Revive.
Data availability All genome-wide datasets are available through the NCBI Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo), under the accession number GSE118898. The data underlying our ChIP-seq, ATAC and RNA-seq analyses, the raw data underlying graphs, and uncropped versions of blots are provided as Source Data. All other relevant data supporting the key findings of this study are available within the article and its Supplementary Information files or from the corresponding author upon reasonable request. A reporting summary for this article is available as a Supplementary Information file.
For each boxplot, the central line represents the median, the limits the lower/upper quartiles, and the whiskers the most extreme data-point within 1. 5 times the interquartile range in excess of the lower and upper quartile. e Heat map representing gene expression z-scores of all transcripts identified by combining SunTag (FDR < 0. 05), 44iN (FDR < 0. 05) and SunTag/44iN likelihood ratio test (FDR < 0. 05) datasets.
Data collection and variables The SurveyMonkey Gold® (San Mateo, CA: SurveyMonkey Inc. ) software was used to launch the study questionnaire online and collect data. An email invitation to complete the questionnaire was sent to all students actively enrolled in the OSH certificate program. The email invitation contained the survey hyperlink redirecting potential participants to an online introduction page providing enough information to insure informed consent. Only participants who checked "I consent to participate in this study" were redirected to the survey. The questionnaire focused on whether or not participants had ever intervened in pain cases, past pain education, self-perceived pain knowledge, and the level of comfort in responding to pain cases. Perceptions toward people suffering from chronic pain were measured using the validated Chronic Pain Myth Scale (CPMS).
Statistical analysis Data related to the characteristics, knowledge and beliefs of the study population was converted into means, standard deviations (SDs) and frequency tables (n and %). CPMS scores were compared to published data about the Quebec population  31  using one sample t-tests. Bivariate comparisons of scores between men and women, and between students that were already employed within OSH or human resources and students that were not, were performed using independent group t-tests. All analyses were conducted with SPSS Statistics® version 22 (Armonk, NY: IBM Corp) and SAS® version 9. 4 (Cary, NC: SAS Institute).
Results Eighty-eight responses were received from active students enrolled in the OSH certificate program. Thirteen out of the seventeen administrative regions of the province of Quebec were represented. According to statistical data from the Registrar's Office, the participation rate was 30. 4% (88/289). The characteristics of our study sample are shown in Table  1. More women responded than men, the former representing 69% of the sample. Three quarters of respondents were currently employed on a full-time basis. Just under 30% were already employed within OSH or human resources. Furthermore, 46% reported having a close friend or family member suffering from chronic pain. Only 13. 6% had completed the optional pain course as part of their program (SCL1416 Phénomène de la douleur).
Strengths and limitations The strengths of this study include the careful development and pretesting of the questionnaire and the minimization of errors in computerizing data through the use of a survey web platform. However, we cannot exclude the possibility of a social desirability bias in participants' responses, and our sample size did not allow the use of a multivariable analysis for the identification of the predictors of knowledge, beliefs and attitudes toward people suffering from chronic pain. A response rate of only 30% was reached which increases the chances of a section bias. However, we should mention that many student surveys report response rates below 20%. 34 As for external validity, this study questions whether or not it is necessary to educate OSH professionals on chronic pain through the evaluation of a sample of students enrolled in a certificate program.
Conclusion Given the under-diagnosed and under-recognized status of chronic pain amongst trained healthcare professionals, and the prejudices held by the general public, the results of this study are not surprising. However, such data collected from OSH professionals and students is essential to reach and persuade decision makers (such as employers and training program managers) to invest time and resources in finding solutions that match educational needs. According to our study, pain education should become a mandatory requirement for OSH students to enable them to better handle their professional responsibilities, and continuing education in chronic pain should be made available for professionals already in the field. Future research should explore the biopsychosocial determinants of knowledge and attitudes amongst OSH professionals, the role of OSH professionals in chronic pain prevention and management, and the improvement of training programs.