dataesr/scientific-paragraphs-categorization huggingface dataset:
MMEIG 2015 had no data for 23 GBD 2015 countries or territories, including Angola, Djibouti, Federated States of Micronesia, Guinea Bissau, North Korea, Palestine, Papua New Guinea, Samoa, Solomon Islands, Somalia, Tonga, and Vanuatu. They did not generate estimates for American Samoa, Andorra, nd Barbuda, Bermuda, Dominica, Greenland, Guam, Marshall Islands, Seychelles, Taiwan, and the Virgin Islands. GBD 2015 used single-year sibling history survival data from each source, applied Gakidou-King weights to adjust for survivor bias, corrected for incidental HIV deaths using countryyear-age-specific information about the population attributable fraction of HIV to maternal death, and used Bayesian noise-reduction algorithms to help to reduce stochastic variability in data. On aggregate, this approach maximises capture of underlying information about levels and trends of pregnancy-related mortality in health surveys.
MMEIG 2015 combined all data from each survey and assigned them to the midpoint year of the recall period. They then uniformly applied a correction factor to reduce every datum by 10% or 15%, depending on the geography. The resulting MMEIG dataset is fairly sparse in some locations, and estimates are driven by regression coefficients. Examples can be seen in Democratic Republic of the Congo, Ethiopia, Ghana, and Saudi Arabia (see appendix pp 303-498). We would encourage MMEIG to consider using single-year sibling history data in their future analyses, because this difference in data processing might be driving divergence in trends from the early part of the MDG period, especially in sub-Saharan Africa. Another important dataset difference is in the method used for processing and adjustment of vital registration data.
GBD uses a standardised approach for all causes of death, empirically analysing every location's single-year vital registration quality to guide dynamic adjustments to raw data (appendix pp 519-652). In past studies, MMEIG applied a default correction factor of 1•5 to all vital registration data. That method was modified in two ways in 2015. First, correction factors for vital registration were adjusted to match the deaths in published surveillance studies for those countries that had completed them. Second, MMEIG 2015 reclassified selected recent years of vital registration data as special studies. These reclassified years were not subjected to the 1•5 correction factor, but earlier years were. This reclassification resulted in estimates showing faster declines in the MMR than are supported by raw data.
We therefore believe age-specific maternal mortality estimates, covering the entire reproductive age range, should be standard practice. While recognising the potential caveats of adult mortality rates estimated using sibling survival, we made important improvements in GBD 2015 in accounting for selection bias and recall bias. We incorporated the uncertainty around adult mortality rate into our all-cause mortality estimation process for countries affected by HIV/AIDS in the sub-Saharan Africa regions. We did this by using single-year data for adult mortality rate from sibling survival modules-instead of pooled data for 5-year periods-and the crude death rate due to HIV/AIDS into the space-time Gaussian process regression that generates adult mortality rate estimates. This approach helped to reconcile between all-cause mortality estimates based on demographic sources and the HIV-specific mortality estimates using EPP-Spectrum, thus allowing us to better capture levels and trends of mortality in adult women, especially in western and central sub-Saharan Africa.
Research in context Evidence before this study Published in 2012, GBD 2010 presented results for 187 countries with a population greater than 50 000 in the year 2000. Collaborative teams completed subnational assessments for the UK, Mexico, and China for GBD 2013, expanding the number of geographies in the GBD analysis to 296. The value of subnational assessments to local decision makers has led to expansion of subnational analyses in GBD 2015 to also include Brazil, India, Japan, Kenya, Saudi Arabia, South Africa, Sweden, and the USA. Several previous analyses, including several completed as part of the Global Burden of Diseases, Injuries, and Risk Factors (GBD) Collaboration, have sought to provide the best possible information about levels and trends in maternal mortality. In dual recognition of both the importance and difficulty of accurately reporting on maternal mortality in many settings, each has incorporated increasingly large and geographically precise datasets and used more advanced statistical models.
Europe PMC Funders Author Manuscripts Europe PMC Funders Author Manuscripts is envisaged to continue so as to sustain comparability over time and across all geographies. The list of causes on the left are raw ICD-10 cause codes according to death certification data sources and those on the right are the final target aetiologies for maternal mortality. The height of each bar is proportional to the number of deaths in each category. The colours are for ease of visualisation. Redistribution categories: A41=other sepsis; A419=sepsis, unspecified organism; D649=anaemia, unspecified; D65=disseminated intravascular coagulation; G809=cerebral palsy, unspecified; G931=anoxic brain damage, not elsewhere classified; I26=pulmonary embolism; I269=pulmonary embolism without acute cor pulmonale; I743=embolism and thrombosis of arteries of the lower extremities;.
Acknowledgments We would like to thank the countless individuals who have contributed to the Global Burden of Disease Study 2015 in various capacities. Data for this research was provided by  MEASURE Evaluation , funded by the  United States Agency for International Development (USAID). Collection of these data was made possible by the  US Agency for International Development (USAID)  under the terms of cooperative agreement  GPO-A-00-08-000_D3-00. Views expressed do not necessarily reflect those of  USAID , the  US Government , or MEASURE Evaluation. The following individuals would like to acknowledge various forms of institutional support:  Panniyammakal Jeemon  is supported by a clinical and public health intermediate fellowship from the  Wellcome Trust-DBT India Alliance  ( 2015-2020 ).
Manisha Dubey  has received financial support from  University Grants Commission, New Delhi, India  for pursuing a PhD. Shireen Sindi  has received postdoctoral funding from the  Fonds de la recherche en santé du Québec (FRSQ) , including its renewal. Ashish Awasthi  has received financial support from  Indian Council of Medical Research, New Delhi, India  for pursuing a PhD. Jasvinder A Singh  has received research grants from  Takeda  and Savient and consultant fees from  Savient, Takeda, Regeneron, Merz, Iroko, Bioiberica, Crealta and Allergan pharmaceuticals, WebMD ,  UBM LLC  and the  American College of Rheumatology ; he serves as the principal investigator for an investigator-initiated study funded by  Horizon pharmaceuticals  through a grant to  DINORA, Inc , a 501 (c)(3) entity; is a member of the executive of OMERACT, an organization that develops outcome measures in rheumatology and receives arms-length funding from 36 companies; a member of the  American College of Rheumatology's (ACR) Annual Meeting Planning Committee (AMPC) ; Chair of the ACR Meet-the-Professor, Workshop and Study Group Subcommittee; and a member of the  Veterans Affairs Rheumatology Field Advisory Committee.
Rafael Tabarés-Seisdedos  and  Ferrán Catalá-López  are supported in part by grant  PROMETEOII/2015/021  from  Generalitat Valenciana , and  Rafael Tabarés-Seisdedos  is supported by the national grant  PI14/00894  from  ISCIII-FEDER. Ai Koyanagi 's work is supported by the Miguel Servet contract financed by the  CP13/00150  and  PI15/00862  projects, integrated into the National R + D + I and funded by the  ISCIII -General Branch Evaluation and Promotion of Health Research  -and the  European Regional Development Fund (ERDF-FEDER). Donal Bisanzio is supported by  Bill and Melinda Gates Foundation  (# OPP1068048 ). Kebede Deribe is supported by a  Wellcome Trust Fellowship in Public Health and Tropical Medicine  (grant number  099876 ). Thomas Fürst has received financial support from the  Swiss National Science Foundation  (SNSF; project no.
P300P3-154634 ). Jost B Jonas reports personal fees from Consultant for  Mundipharma Co (Cambridge, UK) ; other from patent application with  University of Heidelberg  (Heidelberg, Germanysubmitted work. Rodrigo Sarmiento-Suarez  has received institutional support from  Universidad de Ciencias Aplicadas y Ambientales ,  UDCA ,  Stefanos Tyrovolas 's work is supported by the  Foundation for Education and European Culture (IPEP) , the  Sara Borrell postdoctoral programme  (reference no. CD15/00019  from the  Instituto de Salud Carlos III (ISCIII -Spain)  and the  Fondos Europeo de Desarrollo Regional (FEDER). Beatriz Paulina Ayala Quintanilla  would like to acknowledge the Institutional support of  PRONABEC  ( National Program of Scholarship and Educational Loan ), provided by the  Peruvian Government , while studying for her doctoral course at the  Judith Lumley Centre of La Trobe University  funded by  PRONABEC.
I. INTRODUCTION Videos streaming has grown to become one of the largest sources of worldwide Internet traffic, with reports of video content accounting for up to 60% of an ISP's peak load  [1]. A large proportion of this traffic is caused by User Generated Content (UGC) services such as Youtube, Dailymotion, or Vimeo: in 2013 for instance, Youtube accounted for 18. 69% of the overall network traffic in North America, 28. 73% in Europe, and up to 31. 22% in Asia  [2]. Storing, processing, and delivering this amount of data poses a constant engineering challenge to both UGC service providers and ISPs. One of the main difficulties is the sheer number of submissions these systems must process  [3] , most of which need to be served to niche audiences, in limited geographic areas  [4] ,  [5] ,  [6].
Similarly, despite the critical role of tags in UGC online systems  [14] , very few works have explored how tags relate to the viewing patterns of the videos they describe  [15] ,  [16] , and to the best of our knowledge, none have considered how tags could help design better UGC video delivery systems. The lack of works in these areas is striking as tags and geographical areas seem to drive to a large extent the sharing and consumption of UGC videos  [4] ,  [6]. In this paper, we investigate how tags could help improve the design of UGC platforms by providing insights on the geographic distribution of video views. We first analyze an extensive Youtube dataset of 590,897 videos to substantiate our claim that tags can be used as markers of a video's geographic diffusion. Based on our findings, we then show that the geographic distribution of videos' views can be predicted from their tags.
C. Evaluation and metric To compare the two approaches, we divide our dataset into two equal parts: a training set V train and a testing set V test , containing 295449 (±1) videos each. We then use  (7)  to predict the view distribution of each video v in V test from the tag distribution extracted from V train (which plays the role of known videos in the formula). To evaluate the quality of a prediction p geo (v), we compute the proportion of views correctly placed by the prediction (what we term the prediction's accuracy): p correct (v) = 1 - 1 2 × c∈World p geo (v)[c] -p geo (v)[c] (8) where p geo (v) is the actual geographic distribution of video v, and the division by 2 normalizes the result between 0 (all views were misplaced) and 1 (no misplaced views). and by the baseline (Distribution-based prediction) with the corresponding mean and median values indicated below.
C. Experiment, metrics, baseline As in Section III, we split our dataset in two, using the same reference (V train ), and testing sets (V test ). V train plays the role of known videos V known. Because V test remains particularly large (295448 videos, and 86,624,310,171 views), we sample it down: We first generate a trace T of 10 millions requests for the videos of V test that respects the distribution of views between videos and countries. We then choose V new as the set of unique videos present in the trace T. As baseline, we use a random placement policy, which randomly allocates each of the R replicas of a video in V new to any country with some remaining storage capacity. We evaluate the quality of a placement by replaying the trace T , and counting how often a request can be served from the country it originates from (a hit).
A. Tags & folksonomies in UGC systems Geisler and Bruns report an average number of 7. 86 tags per video in a Youtube dataset of a size similar to ours collected in 2007  [15]. Although from a similar order of magnitude, this number diverges from our measurement  (11. 56 ). This might be explained by the distance in time between the two datasets  (2007 and 2011). This might also be due to our different methods of sampling (a snow-ball approach in our case, vs. a search on random words in  [15] ). Heckner, Neubauer and Wolff have compared how tagging is used across different on-line media  [14]. They highlight interesting features of Youtube tags: Youtube users tend to use the tag field as a general free text description of a video, rather than as a organizational means. Some users simply repeat a video's title, while others "overtag" their videos in an attempt to attract more views.
VI. CONCLUSION In this paper we have proposed to use the tags attached to videos to improve the design of User Generated Content (UGC) video services. To inform our work, we have first presented an analysis of the geographic distribution of tags in Youtube, using of an original dataset of 590,897 videos. This analysis shows that the geographic distribution of tags is strongly correlated to that of the videos they are attached to, hinting at the potential of tags to design better UGC services. We have confirmed this potential by demonstrating how the tags attached to a video could be exploited to predict a video's geographic distribution. More specifically, we have shown that we were able to predict a minimum of 65. 9% of a video's views for a majority of videos using tags. Building on these results, we have proposed a novel tag-based placement strategy.
ACKNOWLEDGMENTS This work was partially funded by the  French ANR  project  So-cioPlug  ( ANR-13-INFR-0003 ), by the  DeSceNt project of the Labex CominLabs excellence laboratory  ( ANR-10-LABX-07-01 ), and by the " Politique doctorale 2013" grant of the University of Rennes 1 "Towards a Decentralized Embryomorphic Storage System ".
The SoDa website (www. soda-pro. com) is populated with numerous solar-related Web services. Among them, three satellite-derived irradiation databases can be manually or automatically accessed to retrieve radiation values within the geographical coverage of the Meteosat Second Generation (MSG) satellite: the two most advanced versions of the HelioClim-3 database (versions 4 and 5, respectively HC3v4 and HC3v5), and the CAMS radiation service. So far, these databases have been validated against measurements of several stations in Europe and North Africa only. As the quality of such databases depends on the geographical regions and the climates, this paper extends this validation campaign and proposes an extensive comparison on Brazil and global irradiation received on a horizontal surface. Eleven stations from the Brazilian Institute of Space Research (INPE) network offer 1 min observations, and thirty-one stations from the Instituto Nacional de Meteorologia (INMET) network offer hourly observations.
The satellite-derived estimates have been compared to the corresponding observations on hourly, daily and monthly basis. The bias relative to the mean of the measurements for HC3v5 is mostly comprised between 1 and 3 %, and that for HC3v4 between 2 and 5 %. These are very satisfactory results and they demonstrate that HC3v5, and to a lesser extent HC3v4, may be used in studies of long-term changes in SSI in Brazil. The situation is not so good with CAMS radiation service for which the relative bias is mostly comprised between 5 and 10 %. For hourly irradiation, the relative RMSE ranges from 15 to 33 %. The correlation coefficient is very large for all stations and the three databases, with an average of 0. 96. The three databases reproduce well the hour from hour changes in SSI. The errors show a tendency to increase with the viewing angle of the MSG satellite.
Introduction Knowledge of the solar resource at ground level is a critical issue for developing solar energy. Of particular interest here is the surface solar irradiation (SSI), i. e. the downwelling broadband solar irradiation received at ground level on a horizontal plane. Many studies have demonstrated the potential of satellite images to assess the SSI  (Rigollier et al. , 2004) , and their use within the framework of feasibility studies of solar plants. The SoDa Service (www. soda-pro. com) is dedicated to professionals in solar energy  (Gschwind et al. , 2006)  and provides an access to different solar-related resources, including databases containing estimates of the SSI  (Wald et al. , 2002). Among them are the HelioClim-3 database (abbreviated HC3) and the CAMS radiation service derived from images acquired by the Meteosat Second Generation (MSG) satellite.
CAMS stands for Copernicus Atmosphere Monitoring Service and is a follow-up of the successive Europeanfunded MACC (Monitoring Atmospheric Composition & Climate) projects. The three databases contain SSI data over Published by Copernicus Publications. the area observed by MSG, i. e. Europe, Africa, the eastern part of South America and Middle East. They span from 2004 up to the current day. HC3 has been set up in 2005. It is widely used by professionals in solar energy, whether companies or academics with approximately 4 million requests for time-series of SSI per year. The CAMS radiation service has been introduced more recently in 2014 and is getting more and more popular, especially among academics. So far, these databases have been validated against measurements of several stations in Europe and North Africa only.
As the quality of such databases depends on the geographical regions and the climates, this article extends this validation campaign to Brazil, i. e. to a geographical area located on the edge of the field of view of MSG. It presents the results of an objective evaluation of HC3 version 4 (HC3v4) and 5 (HC3v5) and CAMS radiation service against the measurements of SSI performed at 31 stations belonging to the Instituto Nacional de Meteorologia (INMET) and 11 stations of the Brazilian Institute of Space Research (INPE) network.
HC3 MSG images are acquired every 15 min at Transvalor and MINES ParisTech premises. They are routinely processed with the Heliosat-2 method  (Rigollier et al. , 2004)  to update the HC3 database  (Blanc et al. , 2011). Heliosat-2 combines a clear sky model with a "cloud index". The cloud index approach is based on the assumption that the appearance of a cloud over a pixel results in an increase of reflectance in visible imagery  (Moussu et al. , 1989) ; the attenuation of the downwelling shortwave irradiance by the atmosphere over a pixel is related to the magnitude of change between the reflectance that should be observed under a cloud-free sky and that currently observed. This magnitude of change is quantified by the cloud index. HC3v4 and HC3v5 are the two most advanced versions of HC3.
HC3v4 uses the ESRA clear-sky model  (Rigollier et al. , 2000)  with the climatological database of the Linke turbidity factor of  Remund et al. (2003)  as input. The major drawback of this database is that it is never updated to take into account changes in the atmosphere turbidity due to local effects such as maritime inputs, volcanoes, fires, evolution of the water vapor content, pollution. The McClear clear sky model  (Lefèvre et al. , 2013)  is an outcome of the European-funded MACC (Monitoring Atmospheric Composition & Climate) projects. It takes as input updated information on the properties of the cloud-free atmosphere updated every 3 h and provides estimates of the SSI that should be observed if the sky were cloud-free for any site in the world since 2004. HC3v5 is an attempt to overcome the limitation of the climatological database of  Remund et al.
(2003)  by combining HC3v4 and the McClear model  (Qu et al. , 2014). HC3 estimates of SSI are available at integration periods (or summarizations) of 15 min, 1 h, 1 day and 1 month. The temporal coverage of data is from 1 February 2004 up to current day-2 for the version 5, and day-1, real time and even d+1 forecast data for version 4. HC3 provides global irradiation received on horizontal surface and empirical decomposition models are applied to compute all the components of the radiation over a horizontal, fix-tilted and normal plane for the actual weather conditions. When a request is launched, postprocessing layers are applied for instance to modulate the radiation values inside the MSG pixels to take into account the actual elevation of the required location, or to compute the shadowing effect of the far horizon.
CAMS radiation service The CAMS radiation service, previously named MACC-RAD database  (Hoyer-Klick et al. , 2015) , is available for free via the CAMS and SoDa portals. It makes use of the Heliosat-4 method which models the radiative transfer in atmosphere to compute the SSI  (Qu, 2013). The SSI can be approximated as the product of the irradiance under clear atmosphere given by the McClear model by a modification factor due to APOLLO cloud properties and ground albedo  (Oumbe et al. , 2014). The database of APOLLO cloud properties is the property of the German DLR, and results from the processing of the different channels of the MSG satellite. The ground albedo is that from  Blanc et al. (2014). The CAMS radiation service provides time series of global, direct, and diffuse irradiations on horizontal surface, and direct irradiation on plane normal to sun rays (DNI for direct normal irradiation) for the actual weather conditions.
Protocol of evaluation The protocol addressed the comparison of hourly, daily and monthly satellite-based irradiation values against the measurements of the 42 stations. The three satellite-derived databases under concern are derived from the MSG imagery, i. e. from images acquired every 15 min. Satellite estimates need to be aggregated to the hourly time step. This is done in the usual case by summing up the four 15 min values. It may happen that MSG images may have been unavailable. In the creation of HelioClim-3 and CAMS radiation service, the decision had been taken that if at least one image was available in the day, an intelligent interpolation based on the clearness index and taking into account the sun position every minute was applied to synthesize all 15 min irradiation within this day.
Such cases are rare and were included in the comparison. Since February 2004, only consequence are missing in the satellite-based databases. At that step, both estimates and measurements were available at the hourly time step, and the comparison was carried out. First: -Measurements at night, sunrise and sunset were set to 0, as well as slots when hourly mean of irradiance does not exceed 10 W m -2. -Estimates were set to "Not a Number" (NaN) when the measurements are missing, and reciprocally. In this way, the data sets contain the same number of data with coincidence in time. -For the validation of daily and monthly values, hourly values were aggregated to generate partial daily and monthly sums if at least respectively 65 and 50 % of the hourly slots were available and valid. Then deviations (estimates-measurements) were computed.