dataesr/scientific-paragraphs-categorization huggingface dataset:
Because of the growing interest in the relationship between development and institutions, many databases have been put together over the last few decades that rely on expert opinion to compare the quality of institutions across countries and in many different areas, be it the Polity IV database on the functioning of political institutions, Transparency International on corruption, Reporters without Borders on freedom of speech, the World Economic Forum Competitiveness index, the Bertelsmann Foundation Transformation Index, or Varieties of Democracy, to quote a few. As mentioned earlier, the WGI provides a statistical summary of those individual indicators found in a collection of these datasets, which presumably are related to each of the six areas that are considered in the WGI database. But even though they clearly make intuitive sense, do these areas provide the best analytical structure to study the relationship between institutions and development? Why not other areas, maybe more political or sociological, or possibly sub-areas? The other question is whether a statistical summary based on the heterogeneity observed among all countries in the world is the best instrument to study the way institutions may affect the development process among countries at an early stage of economic development.
Differences in institutional quality between advanced countries and low-income countries may not be of much relevance when trying to understand how institutions may be an obstacle to reach lower-middle income status. Would the synthetic WGI in the six institutional areas defined in that database be the same if they had been built on a sample of developing countries only? To answer these questions, the Institutional Diagnostic Project has explored a set of alternative indicators based on developing countries and endogenously defined institutional areas. These are based on the Quality of Government (QoG) database managed at the University of Goteborg, which functions as a kind of repository of all databases gathering expert opinion in institutional areas (Teorell et al. , 2022). They boast today more than 2,000 individual indicators covering more than seventy years and most countries of the world, even though, of course, not all indicators are available for every year and every country -very far from it.
Only a subset of developing countries and indicators were selected so as to avoid missing data and to strictly focus on institutional characteristics. As a result, the size of the country sample and the set of individual indicators were severely reduced, even when working on a single year. 3  Instead of predefining categories of individual indicators related to a single theme such as the control of corruption or the rule of law in the WGI database, a statistical procedure was used to regroup individual indicators by their informational proximity, or more precisely by their capacity to rank countries in roughly comparable order, while maximising the difference in rankings produced by distinct synthetic indicators. Each group or category of individual indicators is then summarised by a single synthetic indicator, in the same way as the synthetic WGI summarise all individual indicators behind 'regulatory quality' or 'government effectiveness'.
A statistical pseudo-cluster analysis permits us to endogenously define an arbitrary number of such categories with a methodology that is somehow equivalent to minimising the country-variability of individual indicators within categories and maximising differences between them. 4  To get a set of categories comparable with the WGI, it was arbitrarily decided to define six categories. 5  The novelty of this procedure lies in the statistical categorising of individual indicators based on how similar their variation across countries is, while not paying attention to what they represent. With the procedure used to summarise the informational content of all individual indicators in a category, the method extracts maximum information from the overall set of individual indicators in the database through a small arbitrary number of synthetic indicators.
The drawback of this methodology, compared with the WGI, is to make the labelling of categories less intuitive. As variables are grouped in an agnostic way, as a function of their informational content but not of their labelling, it may not be obvious a priori to find a common label. The intuition, however, is that, if the informational content across countries is similar, they must be related to some common institutional area. Experience shows that commonalities among indicators belonging to the same group are sufficient to encapsulate them under a single theme. In our comparison of Tanzania with other countries, 160 individual indicators were selected from the QoG covering forty-five developing countries with no missing information. The preceding methodology was then applied to this subset of the QoG database, and resulted into six categories of individual indicators, each one being summarised by a synthetic indicator.
Thus, the conclusion obtained earlier that institutional quality in outperforming peer countries was not overwhelmingly above that of Tanzania, and that Tanzania clearly dominated in terms of political institutions -that is, 'voice and accountability' in Figure In sum, the alternative set of synthetic indicators derived in the present study from the QoG database and focused on developing countries does not lead us to modify the conclusions obtained with the WGI. This is clearly a test of their robustness. In particular, it is remarkable that ignoring the differences between advanced and developing countries, which are likely to strongly structure the WGI, does not really modify the relative institutional profile of Tanzania when set against those of the comparator countries considered in the present study.
C Tanzanian Institutions According to Other Indicators Individual indicators in the databases used to build synthetic institutional indicators often originate from experts who presumably have inside knowledge about the way institutions work in a country and are able to make cross-country comparisons. Views may be different among people who are more directly exposed to the functioning of a country's institutions, as citizens or firm managers. As a complement to the preceding analysis of synthetic expert indicators, this section compares Tanzania with the same set of countries using two surveys that are representative of users of institutions: the World Bank Enterprise Survey, 7  and the Afrobarometer (for the sub-Saharan comparator countries). • World Bank Enterprise Survey The Work Bank Enterprise Survey is a firm-level survey based on a representative sample of private firms, which collects the opinion of entrepreneurs on their working conditions and their daily experience with the institutional fabric of the country, including the government and public agencies.
Her role has been crucial for its satisfactory completion. regular basis. Given its broad sample of respondents, CIS intends to yield more diverse views and deeper insights into the way institutions work than expertbased institutional indicators in international databases. The pilot CIS, carried out in Tanzania in early 2017, targeted individuals who had been or were in a first-or second-tier decision-making position in business, public administration, academia, non-profit organisations, or local branches of development agencies. They daily interacted with Tanzanian institutions, and possibly also affected the way they functioned as part of their activity. They were thus expected to have a better knowledge of the country's institutions, their strengths and weaknesses. The remainder of this section is organised into six sub-sections.
Respondents then had to allocate twenty points among these three areas -the higher the number of points, the more detrimental the area for development. The selected areas are important for the analysis but also for the subsequent part of the survey because they determined the set of questions presented to the respondent in the main part of the survey. The core section of the CIS comprises 345 questions on the perception of institutions. All rely on a Likert scale, ranging from 'Not at all' and 'little' to 'moderately so', 'much', and 'very much'. Responses are then converted into discrete numbers, ranging from one to five, for the analysis. The questionnaire is inspired by the Institutional Profile Database (IPD), an expert From a practical point of view, administering the whole questionnaire was not an option owing to its length.
This did not prevent optimistic expectations and hopes about the way the new administration would address some of the preceding challenges. Stepping back from the analysis of results, the question then arises of whether a survey which relies on the country's political, economic, and social decision-making population leads to a different evaluation of institutional quality than the expert-based institutional indicators found in international databases. As a way of testing this, use has been made of the fact that many questions in the CIS overlap with the IPD questionnaire submitted to some French diplomats posted in Tanzania. Based on common questions between the two questionnaires, it is possible to measure the degree of correlation between the opinions of a sample of 100 economic, administrative, or academic actors in Tanzania and those of a few close foreign observers.
For the identified candidate events, we then checked whether the events were additionally observed by other lensing surveys to include the data in the analyses if they exist. We found that KMT-2021-BLG-0588 was additionally observed by the Microlensing Observations in Astrophysics (MOA;  Bond et al. 2001 ) group, who referred to the event as MOA-2021-BLG-139, and the other events were observed solely by the KMTNet group. For KMT-2021-BLG-0588, we use the KMTNet ID reference because the KMTNet group found the event first.
Observations and data The KMTNet group has carried out a high-cadence survey since 2016 by monitoring stars lying toward the Galactic bulge field in search of light variation of stars that is caused by microlensing. The survey group uses three wide-field telescopes that are distributed in three sites of the Southern Hemisphere for continuous and dense coverage of lensing events. The sites of the individual telescopes are the Siding Spring Observatory in Australia (KMTA), the Cerro Tololo inter-American Observatory in Chile (KMTC), and the South African Astronomical Observatory in South Africa (KMTS). The telescopes are identical, and each telescope with a 1. 6 m aperture is equipped with a camera that yields a field of view of 4 deg 2. KMTNet observations were mainly conducted in the I band, which is relatively little affected by extinction, and about one-tenth of the images were acquired in the V band for the source color measurements of the lensing events.
Photometry of the events was conducted using the automatized pySIS pipeline  (Albrow et al. 2009) , which is based on the difference-image method  (Tomaney & Crotts 1996; Alard & Lupton 1998). For the color measurements of the source stars, we additionally used the pyDIA code  (Albrow 2017)  to construct a set of the I-and V-band light curves and colormagnitude diagrams (CMDs) of stars that lie in the neighborhoods of the source stars. For the events analyzed in this work, we rereduced the data to obtain optimized photometry data after the events were selected as BD candidates. We normalized the error bars of the data to make them consistent with scatter of data and χ 2 per degree of freedom (d. o. f. ) for each data set to become unity. In the error-bar normalization process, we used the routine described in  Yee et al.
(2012). Of the four analyzed events, the lensing event KMT-2021-BLG-0588 was additionally observed by the MOA survey. The observations of the event by the MOA survey were made with the use of the 1. 8 m telescope of the Mt. John Observatory in New Zealand. The camera mounted on the telescope yields a field of view of 2. 2 deg 2. The MOA observations were mostly conducted in the customized MOA-R band, and the photometry was made using the MOA pipeline. The MOA data set was normalized using the same routine as was applied to the KMTNet data sets.
KMT-2021-BLG-1110 We present the light curve of the lensing event KMT-2021-BLG-1110 in Fig. 2. The lensing magnification of the source, which had a baseline magnitude I base ∼ 19. 52 before lensing, was found by the KMTNet group on 2021 June 2 (HJD = 9367), when the source was brighter than the baseline by ∆I ∼ 0. 5 mag. The source lies in the overlapping region of the KMTNet prime fields BLG01 and BLG41, toward which observations were made with a 0. 5 h cadence for each field and with a 0. 25 h cadence in combination. The light curve is characterized by the double spikes appearing at t 1 ∼ 9370. 85 and t 2 ∼ 9371. 56. The rising and falling sides of both spikes were densely and continuously resolved from the high-cadence observations conducted with the use of the three KMTNet telescopes. The first spike was resolved by the KMTC data, and the second one was covered by the combined data from KMTS and KMTC.
KMT-2021-BLG-1643 The lensing light curve of KMT-2021-BLG-1643 is presented in Fig. 3. The event was found in its early stage by the A190, page 5 of 11 The event exhibited a pair of caustic spikes, which occurred at HJD ∼ 9401. 1 and 9403. 4, and a weak bump, which was centered at HJD ∼ 9409. The region between the two caustic spikes exhibited a characteristic U-shape pattern, indicating that the spikes occurred when the source entered and exited a caustic. The first caustic spike was not resolved because the sky at the KMTA site was clouded, but the second caustic was partially covered by the two KMTS and one KMTC data points. From the 2L1S modeling of the light curve, we found a pair of solutions resulting from the close-wide degeneracy. The binary lensing parameters are (s, q) close ∼ (0.
KMT-2021-BLG-1770 Figure  5  shows the light curve of the lensing event KMT-2021-BLG-1770. The event was found by the KMTNet group on 2021 July 16 (HJD ∼ 9406). The source, which had a baseline magnitude I base = 19. 06, was in the KMTNet prime field BLG03, for which images were taken with a 0. 5 h cadence. Most region of this field overlaps with the region covered by the BLG43 Fig. 4. Scatter plot of points in the MCMC chain on the π E,E -π E,N parameter plane obtained from modeling KMT-2021-BLG-1643 considering higher-order effects. The color-coding is set to represent points with ∆χ 2 ≤ 1 (red), ≤4 (yellow), ≤9 (green), ≤16 (cyan), and ≤25 (blue). field, but the event lies in the offset region that was not covered by the BLG43 field. In our analysis, we did not use the KMTA data set due to its low photometric quality.
Similar to the event KMT-2021-BLG-1643, the light curve of KMT-2021-BLG-1770 is characterized by a pair of caustic spikes and a following weak bump. The first caustic spike, which occurred at HJD = 9412. 2, was not covered, but the second spike, which occurred at HJD = 9412. 4, and the U-shape region between the two spikes were resolved by the combination of the KMTS and KMTC data sets. The weak bump is centered at HJD ∼ 9414, which was about two days after the caustic spikes. From the analyses of the light curve, we identified two local solutions, in which one solution has a binary separation s < 1 (close solution) and the other has a separation s > 1 (wide solution). The model curves of the solutions are drawn over the data points and residuals from the models are shown in Fig. 5. The binary lensing parameters of the individual solutions are (s, q) close ∼ (0.
Acknowledgements. Work by C. H. was supported by the grants of  National Research Foundation of Korea  ( 2019R1A2C2085965 ). This research has made use of the KMTNet system operated by the  Korea Astronomy and Space Science Institute (KASI)  at three host sites of  CTIO in Chile ,  SAAO in South Africa , and  SSO in Australia. Data transfer from the host site to KASI was supported by the  Korea Research Environment Open NETwork (KREONET). This research was supported by the  Korea Astronomy and Space Science Institute  under the  R&D program  (Project No. 2023-1-832-03 ) supervised by the  Ministry of Science and ICT. The MOA project is supported by  JSPS KAKENHI  Grant Number  JP24253004 ,  JP26247023 ,  JP23340064 ,  JP15H00781 ,  JP16H06287 ,  JP17H02871  and  JP22H00153. J. C. Y. , I. G.
In this context, the distinction between menstrual regulation and abortion largely relates to timing, with early abortions considered menstrual regulations. Outside of these two countries in Asia, the authors know of no study that has sought to systematically quantify the extent and nature of menstrual regulation practices. The current study uses population-based data from reproductive-aged women in Nigeria, Cote d'Ivoire, and the state of Rajasthan in India to address this gap in the literature. The first aim of this study is to measure the rate of menstrual regulation for the purposes of fertility regulation in these three settings and to determine the characteristics of women who report engaging in this behavior. The second aim is to describe the methods and sources of menstrual regulation for fertility management.
Analysis To achieve aim one, we estimated the overall one-year incidence of menstrual regulation. Due to survey constraints, we were unable to collect data on event month, only year. Thus, to calculate the one-year incidence we included reported events that occurred between January 1, 2017 and the date of the survey in 2018 and divided by the number of woman-years between January 1, 2017 and the date of the survey in 2018; each respondent contributed on average 1. 28 woman-years in Nigeria, 1. 55 woman-years in Cote d'Ivoire, and 1. 35 woman-years in Rajasthan. We then multiplied the value by 1000 to convert the estimate into a one-year incidence per 1000 women age 15 to 49; we scaled the standard errors using the same approach. We calculated the menstrual regulation one-year incidence rates overall and by background characteristics for each setting, including categorical variables for age (5-year age groups), education (never, primary, secondary, higher), marital status (currently married/cohabiting, divorced or separated/widowed, never married) wealth quintile, parity (0, 1-2, 3-4, 5 +), residence (urban, rural), as well as country specific measures for religion, ethnicity (caste in Rajasthan), and in Nigeria, state.
To evaluate whether menstrual regulation rates different by respondent characteristics we used adjusted Wald tests. There were little to no missing data on study variables in each setting (1% or less), thus we simply omitted these observations in corresponding calculations. However, for the menstrual regulation source variable we coded methods other than surgery and pills (i. e. , all traditional methods, home remedies, or other methods) as being accessed from traditional or other non-clinical sources given we did not ask about their source directly. To achieve aim two, we examined the distribution of menstrual regulation methods and sources using univariate analyses. Method categories included surgery (we did not probe to determine the specific abortion surgery given the limitations of respondent knowledge regarding the procedure), medication abortion pills (misoprostol with or without mifepristone), other pills (antibiotics, antimalarial medicines, and other pills) as well as those where the pill type was unknown, and traditional or "other" methods (herbs, homemade remedies, injections, alcohol, etc.
The data we used are representative of reproductive-aged women in each setting, providing population-based estimates of the extent of this behavior and the characteristics of the women practicing it. Additionally, the contemporaneous collection of these data in three different settings using similar instruments and training methodologies enabled cross-country comparisons of this practice across geographically and culturally diverse regions with different abortion legality and availability of medication abortion pills. Despite these strengths, our study is not without limitations. The main limitation is that we are unable to validate the incidence estimates. Similar to abortion, this practice may also be viewed as stigmatizing to some women who think it is no different from abortion, which would mean the observed rates are an underestimate.
To the extent that women use menstrual regulation for broader health reasons, as some other evidence suggests  [3, 4] , our data would not capture these circumstances of menstrual regulation. Another concern is the potential for misclassification in the methods and sources of the period regulations or misinterpretation of the questions. Many women were not able to provide details about the specific pills they took, which would tend to misclassify medication abortion pill use as other pills. Additionally, while we checked for correct interpretation of the period regulation translated phrases during the pilot studies in each country, the novel framing of these questions could have unintentionally captured some miscarriages or emergency contraception use. Lastly, since abortion was the focus of the broader study and we were primarily interested in the most recent event that could potentially be interpreted as such, for women who reported a pregnancy removal and a period regulation, we only collected method and source details on the most recent of the two events.
The mycorrhizal quality response (MQR%) was calculated in the same way, with values of DQI in the place of dry weight in the latter equation. We determined the effect of pre-inoculation with R. irregularis on the shoot and root Pi content of the plant (mycorrhizal phosphate response; MPR %) according to the following formula  (Wang et al. , 2016) : MPR% = 100 x (Pi content of mycorrhizal plant -Pi content of non-mycorrhizal plant)/Pi content of non-mycorrhizal plant). Results were explored through a principal component analysis (PCA) with XLSTAT (Microsoft). As mycorrhizal colonization parameters represent percentages, all data were arcsine square root-transformed. Significant differences (p < 0. 05) between transformed values recorded between the two contrasting Pi fertilization regimes were analysed using XLSTAT by the Welch-test (degrees of freedom = n-1), which is compatible with unequal variances between groups  (Staehr et al.
Data acquisition Data was collected in a single-pass survey through a digital questionnaire hosted on the KoboToolBox platform and deployed on the KoBoCollect v2021. 3. 4 application. Enumerators surveyed farm managers between September and October 2021. The reference period for the survey was a full year running from May 2020 to May 2021. A guide was produced to help enumerators fully understand the questions and find the information needed to phrase the answers correctly. On the day of the survey, once the respondents' consent to the results being published had been secured, the enumerators started collecting data on the general characteristics of the farms (farm manager's ID and contact details, information about dependants and staff, equipment, tools, and buildings for farming and livestock), followed by detailed data on the following: • Fields (acreage, facilities, tree density); • Crops (plot size, production, soil preparation, use of organic manure and mineral fertilizers, use of herbicides, soil coverage with crop residues or mulch in the dry season);.