dataesr/scientific-paragraphs-categorization huggingface dataset:
Ease of diagnosis Pathologies with tracer drugs or tracer medical acts are better identified in medico-administrative refund information databases  [12, 13]. The Kappa coefficients for the status of diabetes and HIV were higher than those for coronary disease and cancer. These comorbidities identified in the SNDS database are treated with medications that are specifically used for the disease, allowing us to identify patients whose LTD registration or hospitalization diagnoses are not reported. Coronary disease as a medical diagnosis is slightly more difficult to identify in the SNDS database, as it relies on discharge records for patients hospitalized during the given period or an LTD reported in the four years before the year of interest. There are no specific drugs or medical procedures that are integrated into the HECM that can help identify patients who do not comply with the specified conditions.
The REIN database benefits from direct patient interviews and medical records to record information on these conditions. The definition of active cancer in the HECM is based on patients with a reported LTD and hospital diagnosis during the year. These definitions could lead to an underestimation of patients who either did not receive treatment or whose treatment was received in ambulatory care (whose LTD is not reported for the year of interest). As an example, a patient receiving antiestrogen therapy for breast cancer treatment in an ambulatory setting, without hospitalization associated with the reported disease and no LTD reported could be missed by the HECM tool  [14]. On the contrary, the REIN database reports active cancer regardless of the patients' current treatment status. These differences in definition could explain some of the false negative patients.
Timelines In comparing these databases, we should consider that the REIN database collects information prospectively and that the HECM categorizes diseases retrospectively for a given year. The identification of patients with ESKD improved when adding information from the year 2019. A great number of patients with mismatched ESKD status were found to be patients coded in the SNDS as acute kidney disease in the REIN incident year. These may have been patients with chronic kidney disease but who started chronic dialysis after an acute episode who did not fulfill the required time under treatment to be classified as ESKD by the end of the year of interest. As well, despite the work of the REIN registry's research assistants, whose mission is to check the completeness of the cases and compliance with the protocol, we cannot rule out a few marginal errors.
Concerning false negatives for diabetes, a patient identified in the REIN database in December as being diabetic that did not fulfill the requirements to be identified by HECM (e. g. , needs 3 antidiabetic drug deliveries to be identified through medication) for that year would have resulted in a mismatch. A patient identified in the REIN database in January as a patient without cancer might have developed the disease later in the same year and the HECM tool would register them as positive for cancer in that same year, resulting in a false positive. For, coronary disease, we observed better performance when data for the year 2017 was added. This could be a result of HECM considering data for the four years prior to the year of interest to classify patients, therefore, including adding information for patients from 2015.
Patient characteristics We explored the characteristics between the unmatched and matched populations for each condition. We found a higher proportion of early deaths, first RRT with dialysis, males, and residents of Ile-de-France among the unmatched population. Patients with short survival would not have the opportunity to have their record corrected in the REIN database and in the SNDS, they may not have had sufficient healthcare consumption to be identified. First RRT treatment with dialysis and residency in Ile-de-France were the biggest subgroups for which linkage was more likely to be less precise. The Ile-de-France region is a densely populated region were patients could easily mobilize between the different facilities  [8]. Patients could start their treatment at an ICU (recorded in the SNDS) in a postal code and later transferred to a less medicalized center elsewhere (recorded in the REIN database).
Strengths and limitations The strengths of this study were that it used two national databases in which comorbidities are identified by two different methods. However, this study also had several limitations. First, even though the parameters to categorize a patient as having or not a condition are comparable between both databases they are not identical. Therefore, certain patients' conditions could be disparate eeven when correctly categorized in both databases. Unfortunately, among the 58 conditions of the HECM, only 5 had similar identification method with REIN. Many medical conditions explored by the HECM are not collected in REIN like precise cancer location or psychiatric disorder or neurodegenerative disease. We recognise that the results observed for these 5 diseases would have been significantly poorer if we had used diseases whose identification method initially differed.
Second, for legal reasons the databases used do not have a shared unique identifier for patients and therefore relied on a direct deterministic algorithm to link patient information between them. Even when only including patients with a good linkage, there might have been certain patients who were imperfectly linked. The choice to keep only patients with a good match led to the exclusion of 2,338 patients. It seemed to us that in the case of our objective, this did not constitute a bias but may reduced the scope of the extrapolation of our comparison. Third, HECM algorithms were designed for medicoeconomic rather than epidemiological purposes. As such, they do not aim to collect the exhaustive number of incident cases over one year, as economists are generally more interested in the longitudinal evolution of healthcare expenditure and consumption, observed on specific samples.
The pathologies categorized by the algorithm are based on short periods, with individuals not taken into account when they are treated at the beginning or end of the year. This may explain the improvement in performance when the search was extended to the years 2017 and 2019. On the other hand, despite the fact that completeness and accuracy are ascertained by REIN research assistants during regular visits in every dialysis centre, and update at each annual visit, we may not exclude coding error in transcription from medical record. The REIN database included only patients with ESKD, representing only a small proportion of the French population. Therefore, the generalisability of the results to other populations should be explored. Other French registries have successfully linked most of their patients (all over 85%) to the SNDS database: CONSTANCES, FRESH HR, ACIRA, France-TAVI, CANARI  [18] [19] [20] [21].
Data The data set used for the study's empirical design is drawn from an international research project on business competitiveness (Global Competitiveness Project, GCP: www. sme-gcp. org) developed at the Polytechnic University of Catalonia (UPC Barcelona Tech, Spain) and the University of Pecs (Hungary). In 2020, universities from ten European and Latin American countries participated in the GCP (i. e. , Bosnia and Herzegovina, Czech Republic, France, Hungary, Spain, Serbia, Brazil, Colombia, Costa Rica, and Mexico). The objective of the GCP is to analyze the factors of business competitiveness by using composite indicators  (Lafuente et al. , 2020). Recent work by Alonso-Ubieta and Leiva (2019),  Bayon and Aguilera (2021) ,  Moreno-Gómez et al. (2021) ,  Lafuente et al. (2021) ,  Lafuente and Vaillant (2021), and Lányi et al.
(2021)  corroborate the validity of the GCP methodology and databases. Researchers from the participating universities supervised the entire data collection process. The selection process of the surveyed firms in each country was conducted in two different phases. First, each participating team identified businesses operating in different industries. At this phase, owners and/or top managers are the relevant respondent group. An appointment was arranged after an initial telephone call to gain approval from the owners or a top manager. In the second phase, a face-to-face or virtual interview was held with one of the owners (only if they are part of the top management team) in the case of businesses with less than 20 employees. In comparison, a top manager was interviewed in firms with more than 20 employees.
Self-administrated, structured interviews were conducted during the data collection process in which respondents were asked to answer essentially closed questions. Members of the participating teams conducted the survey, and the data was collected between March and June 2019. The GCP teams use a homogeneous questionnaire to ensure enhanced comparability of results. The final sample includes information for 734 businesses from seven countries: Colombia (N = 63), Costa Rica (N = 171), Czech Republic (N = 111), France (N = 131), Hungary (N = 90), Mexico (N = 102), and Spain (N = 66). Note that the purpose of the sampling is in no way to reach representativity of any kind, but much the opposite, as is postulated in the theoretical development of the paper and is stated in the study's first hypothesis: optimal co-innovation configurations are particular to each national innovation system and cannot be standardized/generalized across countries.
When conducting randomised clinical trials, the choice of methodology and statistical analyses will influence the results. If the planned methodology is not of optimal quality and predefined in detail, there is a risk of biased trial results and interpretation. Even though clinical trial methodology is already at a very high standard, there are many trials that deliver biased results due to the implementation of inadequate methodology, poor data quality and erroneous or biased analyses. To increase the internal and external validity of randomised clinical trial results, several international institutions within clinical intervention research have formed The Centre for Statistical and Methodological Excellence (CESAME). Based on international consensus, the CESAME initiative will develop recommendations for the proper methodological planning, conduct and analysis of clinical intervention research.
All source codes will be made publicly available (eg, at GitHub, San Francisco, CA, USA) for the immediate benefit of researchers worldwide. The CESAME guidance documents will primarily be written for experienced trialists, data managers and data analysts. 33 ased on these documents, further congruent guidance documents may be developed for researchers with less experience as well as for experts within the specific fields. 33 here are several relevant issues that CESAME will try to improve and standardise. Please see Table  1.
International collaboration The CESAME initiative is taken by an international group of experienced trialists, methodologists and biostatisticians from the Copenhagen Trial Unit, Centre for Clinical Intervention Research, Department of Regional Health Research at University of Southern Denmark, the Biostatistical Department at University of Copenhagen and Centre for Anaesthesiological Research at Zealand University Hospital (Denmark), Istituto di Ricerche Farmacologiche Mario Negri (Italy), Lund University (Sweden), Department of Data Driven Medicine, Medical Center at University of Freiburg (Germany), McMaster University (Canada), ECRIN (France) and George Institute (Australia). Members of this group have previously collaborated in various projects and made important contributions to the statistical and methodological aspects of conducting randomised clinical trials.
Experimental designs with more realistic extinction processes are one step in that direction (e. g. ,  16, 20, 21  ). Here, we develop a complementary approach by leveraging a global grassland dataset  33  and methods designed for inferring causality from observational data  26, 27, 34, 35. This suite of methods now comprises the dominant approach to causal inference in fields outside of ecology, such as economics, medicine, and public health. When combined with our global longitudinal dataset, they allow us to account for the ecological complexity of grasslands without making strong assumptions about our ability to measure all confounding variables  36  , and they allow us to isolate the effect of biodiversity on productivity separate from the reverse relationship  35, 37. Applying traditional methods to our data, we would conclude that, on average, an increase in biodiversity increases productivity in grasslands, a result found in many prior studies.
In such cases, interpreting and generalizing causal effects is challenging, whether ecologists use experimental or observational designs. The challenge is best met by using large observational datasets that capture the types of species changes that occur in nature and then, using methods like the ones in our study, determine which changesin terms of which species are changing and how (Fig. 5 )are consequential for the effect of richness on ecosystem function. The results of these observational studies could then guide experimentalists in selecting experimental designs that can help confirm the results from observational studies and elucidate the underlying mechanistic processes. Most importantly, our study extends prior research  3  that highlights the importance of study design in credibly isolating causal relationships in natural ecosystems.
Methods To ensure reproducible results, we implemented all analyses in two software programs (R using the 'fixest' package v 0. 8. 2 and Stata v. 16) and multiple researchers confirmed the results. The code for reproducing all analyses, figures, and tables in this study are available through Zenodo (https://doi. org/10. 5281/zenodo. 7675340). A RMarkdown tutorial on the main methods can also be found on our Zenodo release (https://doi. org/10. 5281/zenodo. 7675340) and as Supplementary Data.
Data description Study sites include mesic grasslands and prairies, savanna, desert grasslands, montane meadows, old fields, and alpine tundra from 11 countries. To measure productivity, we use plant above-ground live mass (biomass) (see Fig. S1 ). Biomass production supports many ecosystem processes and services and this measure of productivity has been widely used in studying the relationship between diversity and productivity with both observational (e. g. ,  3, 9, 31  ) and experimental data (reviewed in ref. 4,52,53). For herbaceous vegetation, aboveground live biomass provides a reasonable estimate of primary productivity  54. Biodiversity measures are determined from species cover data from the Nutrient Network (SI Section 3a).
Richness and biomass measures in our data are taken simultaneously each year, as they are in most ecological datasets. Thus, we cannot rely on temporal sequencing of the data to rule out reverse causality. To assess the potential threat of reverse causality, we adopt a statistical approach that is common in economics and public health, but rare in ecology: an instrumental variable design  [59] [60] [61] [62] [63]. When its underlying causal assumptions are valid, this design allows researchers to eliminate not only the influence of reverse causality but also the influence of unobservable confounders, both static and dynamic. To implement this design, we need to measure an attribute of the system that has a relationship with richness, but, after conditioning on the other plot and site attributes in Equation  2 , has no relationship with productivity other than through its relationship with richness.
Acknowledgements We thank  E. Seabloom ,  A. Asmus ,  H. Correa ,  J. Firn , and  F. Isbell  for their input. The views herein are solely those of the authors and do not necessarily reflect those of the  Federal Reserve Bank of Philadelphia  or the Federal Reserve System. We thank  UMN Supercomputer Institute  for hosting data and  IoE  for hosting Network meetings. We acknowledge support from  NASA BioSCape  to L. E. D. and P. A. We acknowledge support from SF Long-354  Term Ecological Research (LTER) Network Communications Office  and  US NSF 823 DEB-355 1545288. This study used data from the  Nutrient Network  (http:// www. nutnet. org),funded at the site-scale by individual researchers. Coordination and data management was supported by funding to  E. Borer  and  E. Seabloom  from  NSF-DEB-1042132  and Institute on the Environment ( DG-358 0001-13 ) and to E.
The politicized nature of votes also suggest that the focal partition should have predictive power if the alignment mattered. For OECD countries,  Potrafke (2009)  finds that ideological alignment predicts UN voting patterns. Data. UN roll-call votes highlight the nature of political alliances in the Cold War. Generally, the US and the USSR did not vote together, so voting with either country indicated alignment in one of the two blocs. 27  To capture international political alignment during this period, we employ a dataset of United Nations General Assembly roll-call votes between 1946-2017  (Voeten, 2013). 28  The dataset provides information on each country's vote (Yes, Abstain, or No) for a given resolution. These votes aggregate diverse interests across varied themes and voting similarity reveals compatible preferences or views (  Gartzke and Gleditsch, 2006).
30 We can also classify the votes by cold war topic, including 921 on the Palestinian conflict, 498 on nuclear weapons and nuclear material, 685 on arms control and disarmament, 871 on colonialism, 843 on human rights, and 591 on economic development. We also coded different types of Cold War interventions in Africa by the superpowers, resulting in eight intervention types: involvement of the US or USSR, the economic or military nature of the intervention, and whether it was hostile or not. Econometric specification. If our theory predicts Cold War alliances, then we expect the focal partition to predict UN voting patterns. Labeling of the partition is data driven as we code the indicator variable as P seudoW estern so that it is positively correlated with voting with the USA. Thus, we test whether the pseudo-Western (Eastern) Bloc predicts UN voting patterns using the square root of the mean squared error as a test statistic.
National Material Capabilities We employ National Material Capabilities data set from the Correlates of War Project. The widely-used Composite Index of National Capability (CINC) index is based on six variables in the data set: total population, urban population, iron and steel production, energy consumption, military personnel, and military expenditure of all state members. We normalize the variable to lie between zero and one. For details see also  Singer (1988)   and Singer et al. (1972).
A. 2 Data for Development Outcomes GDP per capita These data are from the World Development Indicators Database of the World Bank. GDP per capita is gross domestic product divided by midyear population. Data are in constant 2010 U. S. dollars. Life Expectancy at Birth These data are from the Africa Development Indicators Database of the World Bank. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life. Poverty Head Count Ratio These data are from the Millennium Development Goals Database of the World Bank. National poverty headcount ratio is the percentage of the population living below the national poverty lines. National estimates are based on populationweighted subgroup estimates from household surveys.
Measles Immunity These data are from the Statistical Capacity Indicators Database of the World Bank. The proportion of one-year-old children immunized against measles, which is the proportion of children aged one who received one dose of measles vaccine. Urban Share of Population These data are from the World Development Indicators Database of the World Bank. Urban population refers to people living in urban areas as defined by national statistical offices. Income Inequality (Gini) These data are from the Africa Development Indicators Database of the World Bank. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. Financial Account Holders These data are from the World Development Indicators Database of the World Bank.
Account at a financial institution denotes the percentage of respondents who report having an account (by themselves or together with someone else) at a bank or another type of financial institution. Net ODA (Official Development Assistance) These data are from the Africa Development Indicators Database of the World Bank. Net official development assistance (ODA) consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients. Adult Literacy Rate These data are from the Education Statistics Database of the World Bank.
Percentage of the population age 15 and above who can, with understanding, read and write a short, simple statement on their everyday life. Education Expenditure as a Share of Government Expenditure These data are from the Education Statistics Database of the World Bank. Total general (local, regional and central) government expenditure on education (current, capital, and transfers), expressed as a percentage of total general government expenditure on all sectors (including health, education, social services, etc. ). Gender Parity Index (Education) These data are from the Education Statistics Database of the World Bank. Ratio of female gross enrollment ratio for primary and secondary to male gross enrollment ratio for primary and secondary. Access to Electricity These data are from the World Development Indicators Database of the World Bank.
Access to electricity is the percentage of population with access to electricity. Government Expenditure as a Share of GDP These data are from the World Development Indicators Database of the World Bank. General government final consumption expenditure (formerly general government consumption) includes all government current expenditures for purchases of goods and services (including compensation of employees). Agriculture Share of Value Added (% of GDP) These data are from the World Development Indicators Database of the World Bank. Agriculture corresponds to ISIC divisions 1-5 and includes forestry, hunting, and fishing, as well as cultivation of crops and livestock production. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. Polity IV Index These data are from the Africa Development Indicators Database of the World Bank.
The Combined Polity score is computed by subtracting the AUTOCRACY score from the DEMOCRACY score; the resulting unified polity scale ranges from +10 (strongly democratic) to -10 (strongly autocratic). Disappearances CIRI These data are from the CIRI Human Rights Data Project Database. Disappearances are cases in which people have disappeared, political motivation appears likely, and the victims have not been found. Knowledge of the whereabouts of the disappeared is, by definition, not public knowledge. However, while there is typically no way of knowing where victims are, it is typically known by whom they were taken and under what circumstances. A score of 0 indicates that disappearances have occurred frequently in a given year; a score of 1 indicates that disappearances occasionally occurred; and a score of 2 indicates that disappearances did not occur in a given year.