dataesr/scientific-paragraphs-categorization huggingface dataset:
7 Application to the entrepreneurship track was particularly high in regions with the highest unemployment. 8 The fourteen groups of subjects were: Economics and Finance; Accounting; Business Administration; Marketing; Humanities; Languages; Sciences; Technical; Telecommunication; Civil Engineering; IT; Sports and Tourism; Food/Agriculture, and Other. 9 Table  2  is presented for the effective sample used for estimations and composed of the 1,580 students that could be tracked at followup. Results are almost identical when using the full baseline sample of 1,702 students. Table  A1  in the annex presents differences in treatment and control groups for several other characteristics. 10 Detailed contact information was collected in the baseline surveys. Most students register at employment offices upon graduation, and contact information (phone numbers and address) from the employment office database was also collected and merged with the data.
Acknowledgements We thank  Luca Etter  for invaluable support and leadership during the implementation phase of the program, as well as  Maaouia Ben Nasr  and  Leila Baghadi  for excellent support during follow-up data collection. This study would not have been possible without the support of  Ali Sanaa ,  Rached Boussema ,  Fatma Moussa  and multiple staff in the  Tunisian Ministère de Formation professionnelle et de l'Emploi (MFPE)  and the  Ministère de l'Enseignement supérieur et de la Recherche scientifique (MERS). We thank  Hosni Nemsia  and his team for effective data collection. The entrepreneurship track was implemented by the  Government of Tunisia  with support from the  World Bank. We acknowledge the financial support of the  Spanish Impact Evaluation Trust Fund  and of the  Gender Action Plan at the World Bank.
Social Context There is a growing need not only to use Information Systems, but also to integrate them with other systems that can benefit from the information generated and shared within them. Furthermore, resources provided by different systems can have positive impact on the use of Information Systems. The following scenario will help in understanding the social context of the work in this paper. For example, a group of researchers are investigating a certain topic of interest. First they might go through various publications to establish the bases for their research and start forming the state-of-art. Next, they might need to unveil the ambiguity on certain aspects to better understand the topic, so one of them starts using scientific databases to search for keywords related to this topic, while the others use the archive system from other databases to search for information.
Model and Architecture In the light of the objectives stated earlier, this study presents a semantic model of the collaborative SoIS built using owl (Web Ontology Language) and based on semantic web standards. The model is present in (Fig. 1 ). The model highlights the position of an agent, either as an Information System or as a user, in the orchestration of the SoIS. The model also shows how resources are created by users and contained in the composing systems of the collaborative SoIS, while the leader system is only showing those resources through a reference key. Each resource has a reference which functions as a link between the resource and the leader system. This key can be an HTML tag, a Database Identifier, or social bookmark as a Hash tag. Following the MEMORAe approach, the leader can view a semantic map of shared terminology in the work environment.
The semantic map allows the indexing of references to the actual resources through the index key and sharing it within different sharing spaces. The architecture of the collaborative SoIS is shown in (Fig. 2 ). As seen in (Fig. 2 ), the collaborative SoIS will aggregate heterogeneous information and resources from different Information Systems (System A, System B etc. ). These systems are autonomous and work separately of each other. Each of which has its own services/functions and databases. The services/functions of these systems are denoted inside the system (Service/Function 1A, Service/Function 1B etc. ). The architectural model here differentiates between services and functions. While some systems are openly providing an API for requesting their services, other systems are closed and operate as black boxes to the outer world and only provide functions invoked within the system itself.
We conducted 22 interviews with diversity managers or finance leaders from public, private, and voluntary organisations. Seven out of 22 interviews were conducted face-to-face with senior managers working in UK branches. We conducted 15 telephone interviews with senior managers working in several countries outside the UK. The duration of the majority of the interviews was between 45 minutes to 1 hour. With the permission of the respondents, all interviews were taperecorded. We used professional transcribing services and all interviews were transcribed verbatim. We have thematically analysed the interview data using three stages of coding. While we adopted  Strauss and Corbin's (1990)  coding procedures of open coding, axial coding and selective coding, we were cognizant of the necessity to have flexibility in using these procedures  (Walker, and Myrick, 2006).
Statistics on cancer incidence from Central and South American countries are scarce because of the small number of population-based cancer registries that continuously collect data. Similarly, comparable statistics on cancer mortality are sparse in spite of efforts made to improve coverage in the last decade. The aim of this study is to describe geographical patterns and trends in cancer incidence and mortality in Central and South America in the 21st century. The primary objective was to obtain the best quality cancer data available from each country within the region. Cancer incidence data were obtained from population-based cancer registries within the region and, in countries where these did not exist, from hospital-based registries; national mortality data were obtained from the World Health Organization mortality database.
Methods The primary objective was to obtain the best-quality cancer data available from each country within the region. Populationbased cancer registries located in the region that were on the contacts databasemaintained by the International Agency for Research on Cancer (IARC) or that were member organizations of the International Association of Cancer Registries, IACR (http:// www. iacr. com. fr/) or the Red de Institutos Nacionales de Cancer, RINC (www. rinc-unasur. org/) between December 2012 and April 2013were invited to participate by submitting incidence data or by providing consent to use cancer incidence data previously submitted for any volume of Cancer Incidence in Five Continents (CI5)  [3]. To include as many countries as possible, those that did not have a population-based cancer registry (PBCR) were invited to participate by submitting data from pathology-based registries and/or hospital-based cancer registries (HBCRs) as the best available source of information.
Incidence data An invitation letter (including the call for data), a questionnaire, and an instruction manual were sent to all cancer registry directors and coordinators in the country's official language (Spanish, Portuguese, English or French); registries had about 6 months to respond to the data call. Registries that did not respond to the original call received two reminders and then a written e-mail from one of the Editorial Board Members. Several attempts were made to contact key personnel in countries that had not submitted data, including telephone calls to directors and/or e-mail and postal invitation letters to the Ministry of Health. All the participant registries were required to complete a questionnaire that included background and history (year registry started), geographic location, population size, year populationbased data first became available, reporting facilities and data sources, procedures for case finding and data abstraction, coding methods, type of data collected on incident cases, and the methods of follow-up for vital status.
Processing and checking: incidence data The data submission and processing for this project followed principles similar to those used by CI5 (see  [3]  for more details). Registries were asked to submit the material (data, questionnaires, consents) via the Registries Portal (https://cinportal. iarc. fr/), although some registries submitted required information by email. All new data at the individual level collected for this project were stored on a secure protected server at the IARC in the Section of Cancer Surveillance (CSU), to which only a limited number of identified staff members had access. These data (as per their consent) will not be used for any other purpose or transferred to any third party without the registries' explicit permission. The process of checking and validating the data was iterative and required frequent communication and interactions with the cancer registries; this took several months to complete (Fig.
Incidence data (the number of newly diagnosed cases each year) Registries were asked to submit the following data items for each case:  (1)   what is a case, date of incidence, rules for multiple primaries) as recommended by the IARC/IACR  [15] , and only the registries of Mendoza (Argentina), Belem (Brazil) and French Guyana used the European Network of Cancer Registries recommendations  [16]. Data items 6-8 were converted into ICD-O-3 using the IARCcrgTools  [17]  program. The process was complex when registries submitted alternative coding systems such as ICD-O-1, ICD-O-2, or combinations of ICD-9 or ICD-10 topography with ICD-O morphology (Fig. 1 ). Code verifications were made for: sex, incidence date and, if provided, birth date, and ICD-O-3 topography and morphology. Consistency checks were performed between the following items: age versus birth/ incidence dates, sex versus site, sex versus histology, age versus site, age versus histology, site versus histology, and basis of diagnosis versus histology.
Any conversion errors or unlikely or rare combinations identified during this process were communicated to the registry (Fig. 1 ). After receiving a corrected or a new file from the registries, the entire process was repeated to ensure that no more errors were found. Using IARCcrgTools, the database was checked for multiple primaries (multiple tumors) following the IARC/IACR rules, or for duplicate registrations. Once there were no more errors in the database, the ICD-O-3 codes for topography and morphology were converted to ICD-10 codes (Fig. 1 ).
Inclusion criteria All population-based cancer registries that submitted new data or provided written consent to use data previously submitted to the IARC were eligible to be included in the study. Even though there has been no selection based on quality, all datasets received were passed through the same standard data quality control and checking procedures as used in CI5  [18] , and registries were then contacted to correct identified errors and inconsistencies (Fig. 1 ). Pathology-based cancer registries that submitted data were eligible to be included in the study after passing through the same standard data quality control and checking procedures as used in CI5  [18]  (Fig. 1 ). Pathology-based registries were asked for further information regarding sources of information, coverage and exhaustiveness in order to assess whether the estimation of incidence rates was appropriate.
Incidence and mortality rates Numbers of new cancer cases and deaths were grouped into 5-year time periods and 16 5-year age groups (0-4, 5-14, 15-19,. 70-74, and 75+ years). Age-specific rates were estimated using mid-period population denominators by age group and sex. Crude incidence and mortality rates per 100,000 for each cancer were estimated, as well as their percentage contribution to a total number of cancers. Age-standardized rates per 100,000 were calculated using the direct method and the World standard population  [23, 24] , to facilitate comparisons between populations with different age compositions as well with the results from other international publications such as CI5. National incidence rates (age-specific and age-standardized) were estimated by aggregating the data from the available cancer registries using a weighted average of local rates.
[26]. EAPC in age-standardized incidence and mortality incidence rates by cancer site were estimated using a log-linear regression model with a Poisson distribution, through the maximum likelihood procedure, using the calendar year as the regression variable, rejecting the null hypothesis that EAPC equals to zero if the resulting P-value was <0. 05. Changes in rates comprised a shift in the magnitude or a change in the direction of the rate; for example, a positive EAPC indicates an increasing trend while a negative EAPC indicates a decreasing trend. Trends in incidence and EAPCs were estimated only for those registries and countries that provided written consent to use their data or that submitted new data for approximately 10 years. Mortality data were matched to the same time-period to generate time trends and EAPCs.
Results In total, 88 (77 population-based, two histopathology based, and nine hospital-based) cancer registries were invited to participate in this publication; 53 (60%) responded to the data call, 22 of which were previously accepted for CI5 Volume X (Table  1 ). All registries that submitted data were included in the project except for one cancer registry in Argentina where only seven cancer cases remained after the data processing protocol, and one cancer registry in Peru because the registry failed to respond to queries after data processing. One hospital-based cancer registry was excluded from the current report because the data received had no codes with which to conduct any quality control data checks.
Incidence data quality indicators Table  4  shows the percentage of MVs and DCOs by sex, country and registry location for all cancer sites except C44. For most registries, the MV% ranged from 60% to 97%, except for the registries of Chaco and Neuquen in Argentina and Campo Grande in Brazil with 44-57% of the cases being MV, and Jalisco in Mexico with 100% of the cases MV. The percentage of DCO cases ranged from 0% to 45%. The highest DCO%s were recorded in Chaco, Neuquen, and Bahia Blanca in Argentina and Natal, Manaus, Campo Grande, Teresina, Porto Alegre, Santa Fe, Recife in Brazil. Negligible DCO% were recorded in five Brazilian registries, two in Peru, one in El Salvador, one in Bolivia, French Guyana, and Mexico (ranging from 0% to 1. 7%). Annex 2 shows MV% and DCO% for all cancer sites evaluated in this project.
Discussion The goal of this project was to collect incidence data from one or more cancer registries from every country located in Central and South America. Collecting and analyzing data in such a large area (22 countries and territories) in a systematic and comparable way was a major undertaking given the variability in data quality (due mainly to the age and development in maturity of the registries). Hence, an exhaustive review of the data was necessary in order to appropriately analyze, describe and interpret patterns of cancer incidence and mortality between countries and within cancerspecific sites. We also compiled national mortality data from similar time periods as incidence data for all Central and South American countries to provide a more detailed description of the cancer burden in the region.
To ensure data comparability, all cancer registries included in this project underwent the same careful revision and validation as used for CI5, but the inclusion criteria were less stringent. Although some registries may supply suboptimal data quality, they represents the best available data for each country. National incidence data are lacking for most Central and South American countries (except for Costa Rica and Uruguay), therefore nationwide incidence rates are represented by aggregated data from regional registries which do not cover the entire country. Cancer registration between and within countries may differ in completeness and data quality which, among other factors, depend on the maturity of the cancer registry (i. e. Bolivia). Incidence rates could be influenced by differences in case ascertainment and reporting of certain cancers across countries and registries within the region.
Using data quality indicators, most cancer registries were within the regional MV% mean values for all cancers combined (excluding non-melanoma skin cancer) with a few  exceptions (ranging from 79% to 82%)  [29]. Although it is difficult to establish an objective criterion for an acceptable DCO%, as it depends on the local circumstance  [29] , there was a large percentage of cases identified by DCO in nine registries (ranging from 19% to 45% of the cases) which raises questions about the validity of the data  [29]. The histopathology-based cancer registries of El Salvador and Jalisco State (Mexico) were included in the estimation of incidence due to the large coverage of the population and exhaustiveness of data collection which may provide useful estimates of the true incidence for several cancer sites.
However, mortality data provide valuable information to describe the cancer burden since it may be the only source of information available on cancer. Even though data from the WHO mortality database was available for several countries in the region, only countries that had both incidence and mortality data available were included in the analysis of time-trends in order to distinguish possible determinants of observable change over time: e. g. early detection, risk factor or poor treatment. In this study we included the best-quality cancer data available from each country located in the region, covering 10% of the population, greater than what was reported in CI5. In the cancerspecific papers presented in this volume, we describe incidence and mortality patterns of the following 14 cancer types: (ICD-10): head and neck (C01-C14, C32), esophagus (C15), stomach (C16), colorectal (C18, C19-20), gallbladder (C23-24), lung (C33-34), melanoma of skin (C43), breast (C50), cervix (C53), prostate (C61), brain and central nervous system (C70-72), thyroid (C73), Hodgkin lymphoma (C81), and non-Hodgkin lymphoma (C82-85, C96).
Acknowledgements The authors would like to thank sincerely all of the cancer registry directors and their staff (listed in the Appendix to the Introduction of this Supplement) for their considerable efforts in collecting the data presented in this paper, together with members of the  IARC Section of Cancer Surveillance , especially  Sebastien Antoni ,  Murielle Colombet  and  Mathieu Laversanne , and  Graciela Nogueras ,  Elisenda Renteria , and  Daniel McKay  for their collaboration. The authors also wish to acknowledge  Jacques Ferlay  for his valuable comments in reviewing earlier drafts of the manuscript.
I nternational comparisons of perinatal health indicators provide valuable evidence for public policy planning and practice by allowing benchmarking across countries, and revealing the diversity in clinical practice related to antenatal and delivery care. 1, 2 In Europe, recent results from the European Perinatal Health Report 2010 (EPHR 2010) showed wide differences in health outcomes and indicators of clinical practice. ] [5]  International comparisons are limited by the quality and completeness of information held in national data systems however. The EPHR 2010, which reported on population-based aggregate data from 26 EU Member States, plus Iceland, Norway and Switzerland  6  showed gaps in data availability of many key indicators. For instance, only 19 out of 29 countries had data on the gestational age distribution of neonatal deaths, 17 on smoking during pregnancy and 5 on severe maternal morbidity.
7 o compile the Euro-Peristat indicators, countries used multiple data sources: birth certificates, death certificates, medical birth registers, specific registers or audits, hospital discharge data, professional databases and surveys. These varied in their inclusion criteria and data quality, raising questions about comparability. 1, 6 ecord linkage between health, civil and administrative data systems is one way to increase the completeness, quality and breadth of data available for perinatal health monitoring and research. Linkage is the term used to describe the process of merging individual records from two or more datasets in order to extend the range of data items available. 6 Linked data have been used to generate knowledge and investigate the association between population risk factors and a wide array of both maternal  7, 8  and infant health outcomes.
Search strategy We searched PubMed for perinatal health studies based on linkage of routine data sources published between 2001 and 2011. Routine data sources are those that collect information continuously or regularly (in the case of surveys). We used the following key works: data linkage, perinat Ã, matern Ã , link Ã , registr Ã , medical record linkage, infant newborn and birth certificates. Publications were screened by the first author based on information provided in the titles and abstracts. We identified 990 studies from which we excluded conference reports, summaries and reviews. We did not include studies with data that are not regularly repeated. We included research related to the perinatal period: pregnancy, delivery and the post-partum, which linked two or more routine datasets together or paired mother and newborn records within the same data collection system.
Data extraction From each study, we extracted the types and number of routine data sources used, the main outcome variables, the dependent variables, country of origin and year of publication. Principal types of sources were: civil registration (birth and death certificates), hospital discharge data (i. e. admissions, inpatient and other medical records) and medical birth registers (which hold birth data augmented with clinical information about each delivery and birth). 1  We described how linkage was used in perinatal health research by classifying publications according to themes and linkage types within countries and by year. We categorized studies based on their outcome variables into the following research themes: (i) fetal, neonatal and child health, (ii) maternal health and (iii) methodsthis theme included studies focused on validating data through record linkage use, or on usage of specific data linkage techniques such as probabilistic vs.
deterministic methods. We further assessed which of our included studies were longitudinal. We flagged studies as 'longitudinal' when researchers studied the impact of health events outside the perinatal period (i. e. exposure to environmental risk factors during pre-conception) on outcomes during the perinatal period (i. e. birth weight), or when researchers studied the influence of perinatal risk factors (i. e. preterm birth) on longer term maternal or child health outcomes (i. e. educational attainment). Data extraction was carried out by the first author and validated by the co-authors.