dataesr/scientific-paragraphs-categorization huggingface dataset:
Data entry tool The data entry tool included a data documentation sheet (codebook containing the plan for data entry) and EpiData database (consisting of QES, REC, CHK triplet files), separately prepared for household and individual level questionnaires. After developing the first draft of the data entry tool at The Union, New Delhi in March 2016 (NB and HDS), it was pre-tested at Chennai, Tamil Nadu, for suggestions to reduce possible data entry errors (through data entry checks) and number of key strokes per record (April 2016). In the final tool, there were 17 fields in the household level and 160 fields in the individual level REC file. Each REC file was encrypted with a password. The key data entry checks have been summarized in Box 2.
Such folders can be shared with others for mutual access. More information at www. dropbox. com What is TeamViewer? TeamViewer is a secure software package for remote control, desktop sharing, online meetings, web conferencing and file transfer between computers. TeamViewer is a tool that makes it very easy to set up and use a Virtual Private Network connection that lets you take complete control of another computer from your own computer via internet. It enables twoway connections in which users can flip control back and forth. While TeamViewer is proprietary, it is free for noncommercial purposes. More information at www. teamviewer. com Box 2. Salient data entry checks incorporated to prevent data entry errors in Tamil Nadu Tobacco Survey (TNTS), India (2015-16).
I. Both in household and individual level CHK files i. Data entry time for each record was auto recorded as an auto generated field. This prevented the data entry operator from copying the single entered record and therefore, feigning double data entry. ii. Each household and individual under an urban and rural area of a district or slum and non-slum area of a zone of Chennai had a unique identifier. iii. Unique identifier was a derived field and was auto generated after entering the individual variables. Generation of unique identifier was made compulsory before moving to next record.
If occupation was either student/unemployed/homemaker/retired/missing, then work place related questions were auto entered as 'not applicable'. labels' in EpiData language), already available as a Microsoft Excel database, were imported as an external label block (site. rec in supplementary online material). Among the researchers, this Dropbox shared folder was accessible only to the responsible officials, respectively, from The Union (HDS) and Cancer Institute (DPS). Half-day training for data entry operators (DEOs) was conducted by the Cancer Institute (WIA) in Tamil Nadu with remote support from The Union. The Union trained DPS over Teamviewer and DPS in turn trained all the DEOs in person. Double data entry was carried out between May 2016 and August 2016 at a single site in Tamil Nadu.
We planned to enter data districtwise (n = 31) and zonewise (n = 15): thus, 46 Dropbox shared folders containing copies of the data entry tool were prepared. It was distributed among 10 DEOseach DEO was provided access only to those districts/zones' Dropbox shared folder for which s/he was allocated to enter data. Two DEOs simultaneously worked at one district/zone and completed the double data entry independently (anonymized data). We used this sectoral approach (10 DEOs completing data entry for five districts/ zones at a time) to complete data entry for all 31 districts and 15 zones. A validation report for data entry errors was simultaneously prepared and a final household and individual level REC file was prepared for each district/zone after making corrections. Data entry was near real time and therefore, was monitored remotely.
As and when required, remote trouble-shooting from The Union, New Delhi, was performed using TeamViewer. All the final 46 (31 districts and 15 zones) household level REC files were combined using the function 'append' in EpiData analysis. Similarly all the final 46 individual level REC files were appended. The appended household level REC file was combined with the appended individual level REC file using the function 'merge'. The unique identifier provided to each household was entered in both household level and individual level REC files. This helped us to link the respective households with their respective household members while merging the dataset.
Data entry errorsquality assured data entry The proportion of records with data entry errors, across districts/zones, ranged from 0% to 27% and 0% to 64% in household level and individual level REC files, respectively. The average error was 4% and 24%, respectively. The proportion of fields with data entry errors, across districts/zones, ranged from 0% to 8% for household level REC files and 0% to 6% for individual level REC files. The average error was 0. 5% and 0. 7%, respectively (Table  1 ). These are the errors that would have gone unnoticed if single entry was performed. Double data entry and validation helped identify and correct these errors.
Data inconsistency Despite checks during data entry and double data entry/validation, some inconsistency in entered data may occur, which can be explained by errors during data collection. We checked for the same for two key tobacco use indicators: current and past tobacco use. Among interviewed individuals, for current tobacco use, we identified four records with current tobacco use not available or not recorded and 138 records (0. 1%) with data inconsistency (current tobacco use status was 'no', but current tobacco use status was 'yes' as per the information collected under various types of tobacco and vice versa). For past tobacco use, among eligible interviewees (after excluding those with current tobacco use), we identified 110 records (0. 1%) with data inconsistency (past tobacco use was 'yes', but past tobacco use status was 'no' as per the information collected under various types of tobacco).
Considering the policy implications mentioned above, such data have to be of high quality. The variation in average percentage error was expected depending on the unit of analysis (record or field) and on the type of REC file, whether individual or household level. When we compared the percentage errors during validation (either with fields or with records as the unit of analysis) across household level and individual level REC files, it was expected that the average error would be higher for individual level REC files. The reason for this was the large number of fields in individual level REC file (n = 160) compared to household level REC file (n = 17). Average percentage errors were higher if the unit of analysis was records (compared to fields) because even when a single field in the record had an error it would be counted as that record having a data error.
Smartphones are relatively inexpensive (basic tablets can cost 60 USD): this may be considered in future, subject to availability of budget. Considering the small size of EpiData files, this model of data entry can be replicated in other resource-constrained settings where internet connectivity is poor. 'Automatic Forms Processing' is a possible alternative to double entry, a method by which data collected can be 'automatically' entered by scanning, and converting it into an electronic format through techniques such as 'Optical Mark Recognition' or 'Intelligent Character Recognition'  [9]. This would also require relatively expensive equipment and computer expertise that are often not available in resource-limited settings.
Acknowledgments We would like to acknowledge  R. Swaminathan  for his assistance in designing the study and estimating the sample size. We acknowledge the  Training Orientation and Research Centre  for providing the list of educational institutions and helping us in identifying the suitable institution. We would like to acknowledge all the 31 educational institutions and three NGOs who constituted the study group and the field investigators who assisted in data collection. We thank the  Department for International Development (DFID), UK , for funding the  Global Operational Research Fellowship Programme  at the  International Union Against Tuberculosis and Lung Disease (The Union ), Paris, France in which HDS works as a senior operational research fellow. We thank the study coordinators  R.
Acknowledgements The authors thank everyone who participated in and funded these deliberative dialogues as well as the reviewers. The authors also thank  Isobel Hié , who translated the manuscript and  Donna Riley  for editing the last version. contributors VR and CD analysed the process and wrote the manuscript. Both authors read and approved the final manuscript. funding Part of this work was supported by the  Canadian Institutes of Health Research (CIHR) , which funded the programme ( ROH-115213 ). VR holds a CIHRfunded Research Chair in Applied Public Health ( CPP-137901 ). disclaimer The sponsors had no role in the writing of the paper or in the decision to submit the article for publication.
, 2012). The impact of CMUC eligibility is then estimated based on the total number of visits to the doctor, the number of general practitioner (GP) visits and the number of specialist visits. Our sample consists of 2232 low-income individuals enrolled in the Health Insurance Fund (Caisse Primaire d'Assurance Maladie, CPAM) and recipients of social benefits from the Family Allowance Fund (Caisse d'Allocations Familiales, CAF) in an urban area in Northern France. These individuals have an income per consumption unit (CU) of up to 30% above or below the CMUC eligibility cut-off point. Individuals below this cut-off point can benefit from the CMUC plan, and individuals above this cut-off point are eligible for ACS. Our data set includes claim data from all doctor visits for the years 2008 and 2009 as well as information on 2007 and 2008 resources.
Data and sample selection Our sample of individuals consists of poor recipients of family benefits from the CAF and the CPAM of Lille, an urban area in Northern France. These individuals were identified at the end of 2008 on the basis of their 2007 tax-declared incomes as potentially eligible either for the CMUC plan or for the ACS voucher programme 6   (Guthmuller et al. , 2014). This programme is open to poor households whose income were up to 20% above the CMUC cut-off in 2009. Since January 2013, the eligible population has been extended to individuals with an income up to 35% above the CMUC cut-off. Our sample consists of individuals eligible for ACS and individuals eligible for CMUC, often with an income near the threshold. Based on this sample and on updated information on reported income taxes and any monthly social benefits in 2007 and 2008, we accurately retrieved the annual income per CU used to assess eligibility for CMUC.
7 We then retained for the years 2008 and 2009 those individuals whose household income per CU one year earlier was within a range of + or -30% around the CMUC eligibility threshold. We excluded from our sample individuals who benefit from a disability allowance or are recorded by the CPAM as being fully covered for a chronic disease, 8  in addition to individuals over the age of 60; as previously indicated, these people are generally not eligible for CMUC. 9 or each individual belonging to this sample and for each year, we collected data from the CAF and the CPAM. The Family Allowances Fund provided information on family characteristics for the years 2007 and 2008, the number of members in the household and the family composition (couple, single, a couple with children, single with children), as well as age, gender and employment status.
The CPAM provided administrative data on the number of doctor visits, the number of GP visits and the number of specialist visits. This database also includes monthly information on the CHI coverage status and whether the individual is 100% covered for a chronic disease. Our sample consists of 2232 observations  (1221 in 2008 and 1011 in 2009). We define individuals as CMUC recipients if they are covered by the CMUC plan for at least the first 6 months of the year. As eligibility is assessed on income in the 12 months prior to application, it is possible that individuals who appear to be ineligible for CMUC on 31 December are still considered CMUC recipients the following year, because they could be eligible 6 months earlier or 6 months later. Battistin et al. (2009)  showed that if we assume this measurement error of eligibility orthogonal to treatment and outcome variables and under the assumption that we observe the true eligible income for part of the population, the estimation strategy we follow in this paper remains valid.
w it is the unobserved error component with E(W it |X it , eliCMUC it , Z it ) = 0. 12 Formally and as shown by  Hahn et al (2001) ;  Imbens and Lemieux (2008) ; and  Lee and Lemieux (2010) , the effect of CMUC eligibility δ can be estimated without bias on outcome variable y as: δ ¼ lim z→c À E Y=Z ¼ z ½ Àlim z→c þ E Y=Z ¼ z ½. Because the number of doctor visits is over-dispersed, we use a negative binomial II model to estimate D. This is also the model that best fits our data. 13  We observe the assignment variable to CMUC (household income per CU) in 2007 (  2008 ) and estimate the effect on outcome variables in 2008  (2009). Estimates are run on the pooled dataset 14 and with different polynomial functions of income to check the robustness of our results. The baseline specification includes a linear function of income per CU and a year dummy, individual and family characteristics.
ACKNOWLEDGEMENTS The authors are grateful to  Eve Caroli ,  Brigitte Dormont ,  Michel Grignon ,  Tor Iversen ,  Florence Jusot ,  Christine Le Clainche  and  Dominique Polton  for their helpful comments on earlier versions of this paper. This study was financially supported by Health Chair, a joint initiative by  PSL, Université Paris-Dauphine, ENSAE  and  MGEN  under the aegis of the  Fondation du Risque (FDR). Sophie Guthmuller  thanks the  Caisse Nationale d'Assurance Maladie des Travailleurs Salariés (CNAMTS)  for the PhD scholarship. The final revisions of both text and empirical strategy of the article have been conducted when Sophie Guthmuller took service at the  European Commission, Joint Research Centre. The scientific output expressed does not imply a policy position of the  European Commission.
Highly radioactive cesium-rich microparticles (CsMPs) released from the Fukushima Daiichi Nuclear Power Plant (FDNPP) provide nano-scale chemical fingerprints of the 2011 tragedy. U, Cs, Ba, Rb, K, and Ca isotopic ratios were determined on three CsMPs (3. 79-780 Bq) collected within ~10 km from the FDNPP to determine the CsMPs' origin and mechanism of formation. Apart from crystalline Fepollucite, CsFeSi 2 O 6 • nH 2 O, CsMPs are comprised mainly of Zn-Fe-oxide nanoparticles in a SiO 2 glass matrix (up to ~30 wt% of Cs and ~1 wt% of U mainly associated with Zn-Fe-oxide). The 235 U/ 238 U values in two CsMPs: 0. 030 (±0. 005) and 0. 029 (±0. 003), are consistent with that of enriched nuclear fuel. The values are higher than the average burnup estimated by the ORIGEN code and lower than nonirradiated fuel, suggesting non-uniform volatilization of U from melted fuels with different levels of burnup, followed by sorption onto Zn-Fe-oxides.
The presence of 138 Ba indicates either a fissionogenic or natural origin. Natural 136 Ba/ 138 Ba, 0. 1095, is an order of magnitude greater than 136 Ba/ 138 Ba of fissionogenic origin, ~0. 0126, estimated for Units #1-3 by calculation using the ORIGEN code  19  , which largely varies depending on the burnup: 0. 004 for 10 MWd/kgU, 0. 011 for 20 MWd/kgU, and 0. 014 for 30 MWd/kgU. 136 Ba/ 138 Ba is 0. 1523, 0. 1638, and 0. 4293 for OTZ3, KOI2, and OMR1, respectively. These values are much higher than those of fissionogenic Ba. The 136 Ba/ 138 Ba ratio may indicate the contribution of a low-yield fission product; the amount of 136 Cs (half-life of 13. 16 days) can be remarkable in some cases depending on the amount of volatilized Ba. Although the amount of fissionogenic Ba isotopes is greater than Cs isotopes, the Cs can volatilize to greater extent than Ba during the meltdown, meaning that CsMP can selectively incorporate Cs as compared with Ba.
235 U/ 238 U = ~0. 030, as analyzed from the two CsMPs, indicating that the U incorporated in the CsMPs is not from naturally occurring trace U that has a 235 U: 238 U ratio of 0. 00729:1, which was initially considered as the source of U because trace amounts of U are included in the insulating materials surrounding reactor pressure vessel. Rather, nuclear fuel is the most likely origin of U in the CsMPs. The original isotope composition of non-irradiated fuel before operation at the FDNPP was 0. 0389  19. Based on calculations using the ORIGEN code, the U isotopic ratios for the irradiated fuel in Units # 1, 2 and 3 were 0. 0172, 0. 0193, and 0. 0192  19  , respectively. Thus, the isotopic ratios are between the isotopic ratio calculated from burnup and that of the non-irradiated fuel. The isotopic ratios calculated by the ORIGEN code are based on the average burnup for all fuel assemblies.
Methods Sampling. We collected the samples at three locations (Fig. 1 ). As for the sample labels, the capital letters represent the locality and the following number indicate the number in our CsMP database in Kyushu University. The OTZ3 and OTZ10 were separated from the same soil collected from the top ~1 cm of the paddy soil at Ottozawa located ~4 km west from the FDNPP in Okuma Town, Futaba County, Fukushima, on March 16, 2012. The soil was mainly composed of clay minerals, quartz, and feldspar  9. Because it is still prohibited to enter the area due to the high radiation dose, the locality remained untouched and was not disturbed by decontamination or restoration. The radiation dose ~1 m above the ground was measured at 84 μSv/h. The second sample (KOI2) was composed of gravels collected under the drainpipe of the assembly house in Koirino located 2.
The JEOL Analysis Station software was used to control the STEM-EDX mapping. To minimize the effects of sample drift, a drift-correction mode was used during acquisition of the elemental map. The STEM probe size was ~0. 13 nm, generating an ~140-pA current when 40 μm of the condenser lens aperture was inserted. The collection angle of the HAADF detector was ~97-256 mrad. A Gatan imaging filter system was used to conduct electron energy-loss spectroscopy in STEM mode. The convergent and collection semi-angles were ~30 mrad and ~17 mrad, respectively. Gamma spectrometry. The 134 Cs and 137 Cs radioactivities of the CsMPs were determined using gamma spectrometry. The radioactivity of an additional microparticle with a size of ~400 μm obtained from surface soil in Fukushima was precisely determined at the radioisotope center in Tsukuba University, Japan, and utilized as a standard point specimen for  134  Cs and 137 Cs.
Acknowledgements This study is partially supported by  JST Initiatives for Atomic Energy Basic and Generic Strategic Research  and by a  Grant-in-Aid for Scientific Research (KAKENHI)  from the  Japan Society for the Promotion of Science  ( 16K12585 ,  16H04634 , No. JP26257402 ). S. U. is also supported by  ESPEC Foundation for Global Environment Research and Technology (Charitable Trust) (ESPEC Prize for the Encouragement of Environmental Studies). The authors are grateful to  Dr. Watanabe  for her assistance on SEM analyses at the  Center of Advanced Instrumental Analysis, Kyushu University. The findings and conclusions of the authors of this paper do not necessarily state or reflect those of the JST.
Study design and participants Participants were WRA (ages 15-49 y) from 9 data sets from lowincome countries. Five data sets from Africa and Asia were analyzed during the first phase of the WDDP, as described in Arimond et al. (1) , including data collected in Ouagadougou, Burkina Faso, in 2006 (hereafter, abbreviated BF1), in Bamako, Mali, in 2007 (Mali), in rural Mozambique in 2006 (Moz), in rural Bangladesh in 1996 (Ban1), and in peri-urban Cebu, Philippines, in 2005 (Phi). During the current phase, these were reanalyzed and were supplemented with 4 additional data sets obtained from the HarvestPlus Project, from rural Bangladesh in 2008 (Ban2), rural Burkina Faso in 2010 (BF2), rural Uganda in 2007 (Ug1), and urban and rural Uganda in 2008 (Ug2). Study sites, sample size calculation, and sample designs depended on the primary study objectives and are fully described elsewhere  (15) (16) (17) (18) (19) (20) (21) (22) (23).
Briefly, the primary study objectives were often to help refine a program strategy (Ban1 and Mali), to help determine the potential impact of food fortification (Ban2, BF2, and Ug2), or to serve as a baseline for the impact evaluation of a biofortification intervention (Moz and Ug1). One data set was extracted from 1 round of a longitudinal prospective health and nutrition survey (Phi), and 1 study was undertaken with the WDDP primary objective of validating simple dietary diversity indicators as a measure of micronutrient adequacy among WRA (BF1). Study sites were selected on the basis of the study objectives. Study areas from rural regions were therefore either poor and food-insecure regions (Ban 1, Moz, and Ug1) or regions where targeted crops were present [Ban2, BF2, and Ug2 (rural part)].
Dietary data collection and management Data sets had dietary data consisting of 24-h recalls, with the exception of one, Ban2, in which 12-h direct-weighed records were complemented by a 12-h recall. In most studies, the 24-h recalls were performed by using the multiple-pass method, adapted from widely acknowledged guidelines  (24) (25) (26). Recipes were usually collected from the female household member who was responsible for cooking, and standard recipes were used for dishes consumed outside the home. For estimating and measuring quantities, the methods used were those best suited to local foods and conditions (e. g. , direct weighing, volume equivalent using beakers with marked volumes, playdough models, photographs, etc. ). The study team carefully checked that dietary data were collected in all studies with the use of well-documented and sound methods.
General characteristics of women and their diets Data were available for 4166 women, with sample size varying by site (Table  2 ). The median rate of exclusion from original samples due to implausible energy intake according to the Goldberg criteria was 11. 2% [range: 0-18. 0% for all data sets but one (Phi) in which the exclusion rate was 61. 3%, which will be discussed later]. The proportion of lactating women was particularly high in some sites due to study designs targeting mothers of infants and young children. The average age of the women was similar across sites, but BMIs varied widely. Median energy intakes ranged from 1671 kcal in Phi to 2439 kcal in Ug1 (Table  2 ). Macronutrient intakes were  In the BF1, Mali, and Phi sites there were ,100 lactating women and no separate analysis was performed.
" Our analysis and the resulting indicator have several limitations. First, although efforts were made to obtain data sets from urban, rural, and geographically diverse contexts, the 6 African and 3 Asian data sets do not represent all global diet patterns. We acknowledge that most of the studies were not performed in samples that were representative of the whole population living in each particular country or context, but this was deemed not to be a strong limitation for the main purpose of our analysis. In addition, all samples involved randomization and some checking was performed to verify that they correctly represented typical situations of each context. Because the study sites were generally in resource-poor areas, however, the indicator may be most relevant in similar settings. In one case (Phi), the rate of exclusion from the original sample was very high, because reported energy intakes were very low.