dataesr/scientific-paragraphs-categorization huggingface dataset:
With international migration open to only very few Africans, we should expect massive internal migration to form a core part of the development process. The scale of this demographic process is captured in the data that form the basis of this paper, further motivating our focus on internal migration. These data are part of an exceptional panel data set from the Kagera region in Tanzania, spanning nearly two decades of migration and development. The 2004 and 2010 follow-up surveys attempted to trace all 6,353 individuals listed on the baseline 1991/94 household rosters and re-interview them irrespective of their location. Once we exclude the 1,275 individuals who had died by 2010, we are left with 4,996 baseline individuals whose 2010 locations are known. 1 Of those, 45 percent were found residing in the baseline village, 53 percent had migrated within the country, 2 percent to another East African country (primarily Uganda) and 0.
8 [Table  2 here]  The consumption data originate from extensive food and non-food consumption modules in the survey, carefully designed to maintain comparability across survey rounds and to control for seasonality. The aggregates are temporally and spatially deflated using data from a price questionnaire included in the survey. Consumption is expressed in annual per capita terms using 2010 Tanzanian shillings. 9 Using the 1991-2004 panel  Beegle, De Weerdt, and Dercon (2011)  document how migrants grow much richer than their family members who did not migrate. Table  3  provides the summary of the consumption and poverty developments of the panel respondents with respect to their 2010 location. On average, consumption levels in the sample almost doubled over 19 years. Individuals who stayed in their community saw their consumption increase by more than 40 percent.
11 This difference is statistically significant at the 1 percent level. The severing of the most basic links does not seem to be associated with higher consumption growth; if anything, the reverse is true. We use data from shock modules administered in 2004 and 2010. During both of these rounds, the panel respondents were asked to consider each year between the survey rounds and indicate whether a particular year was, in economic terms, 'Very good', 'Good', 'Normal', 'Bad', 'Very bad'. For each 'Very bad' response, the respondents were asked to provide the main reason for the hardship. We consider each 'Very bad' response as a negative economic shock and each 'Very good' response as a positive economic shock. More than 60 percent of the panel respondents reported experiencing at least one negative shock between 1994 and 2009.
First, if shocks are self-reported then respondents may fail to mention those that were effectively insured. Second, the extended family network in the home community may extend beyond the networks as defined in our data. Fortunately, the survey provides an alternative shock measure, which is community-wide and not self-reported. We have historical rainfall data from the Tanzanian Meteorological Agency for gauges in 212 weather stations in Kagera and at the migration destinations in our sample. The drawback is that this shock measure does not allow us to quantify positive shocks: too much rain is not good for yields, especially when it falls in the wrong season (e. g. when the beans are drying in the field). We therefore treat this exercise as robustness check for the self-reported negative shocks.
The fieldwork was primarily funded by the  Rockwool Foundation  and the  World Bank , with additional funds provided by  AFD ,  IRD  and AIRD through the " Health Risks and Migration " grant of the  William and Flora Hewlett Foundation. Kalle Hirvonen  gratefully acknowledges the financial support from the  Economic and Social Research Council  [grant number  ES/I900934/1 ], the  Finnish Cultural Foundation  and  Yrjö Jahnsson Foundation. Stefan Dercon  was instrumental in conceptualising this paper.
Estimates of the numbers of pastoralist and agropastoralists and their livestock are usually based on GIS modelling. The most quoted internationally comparable source is compiled by the International Livestock Research Institute  (Thornton et al. 2002). This is the only dataset available which makes a global comparison, but it does not distinguish between ranching, pastoral and agropastoral systems and excludes Europe, North America, Russia, and Australia (Figure  4 ). However, the number of people associated with "grassland-based production sys-Spatial distribution of agro-ecological zones in the developing world.
Added value of this study GBD 2013 includes an annual assessment covering 188 countries, from 1990 to 2013. It covers 306 diseases and injuries, 1233 sequelae, and 79 risk factors. GBD 2013 included key methodological diff erences from GBD 2010, which were inclusion of new data through updated systematic reviews and through the contribution of unpublished data sources from many collaborators; use of a counterfactual approach for estimating diarrhoea and pneumonia causes; elaboration of the sequelae list to include asymptomatic states; use of more detailed nature of injury codes (N-codes); improvements to the Bayesian meta-regression method; increased simulation size for comorbidity; estimation of the prevalence of injuries by cohort; and use of a new method to estimate the distribution of mild, moderate, and severe anaemia by cause.
Mortality estimation We generated child mortality rates and adult mortality rates under the infl uence of natural disasters and armed confl icts as previously described. 7 Because of ongoing unrest and war, some datapoints needed for our analyses were not available. A list of all datapoints used in this study are available on our Global Health Data Exchange web page. When data were unavailable, we relied on our ensemble modelling techniques to generate the estimates using other available variables and the information for neighbouring countries or countries with a similar health profi le in the region. We used six diff erent modelling strategies for the 240 causes of death using our cause-of-death ensemble model for causes with suffi cient information. We estimated national time series from 1950 to 2013 for gross domestic product, educational attainment, tobacco prevalence, and obesity.
For mortality rates of children younger than 5 years, we analysed all survey, census, sample registration, and vital registration sources. Wherever possible, we analysed microdata from surveys and censuses with updated methods for child mortality. We corrected for bias in diff erent sources in specifi c countries. For adult mortality, we identifi ed all available vital registration data, sibling history survey data, sample registration data, and household recall of deaths. We assessed vital registration data for completeness and analysed sibling history data to account for survivor bias, zero-surviving sibships, and recall bias. We used spatiotemporal regression and Gaussian process regression to synthesise all measurements of mortality. We used UN population estimates that account for migration in our analyses.
www. thelancet. com/lancetghVol 4 October 2016 ( T Setegn MPH );  Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran (H Amini MSPH) ;  Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland (H Amini );  University of Basel, Basel, Switzerland (H Amini) ;  Ministry of Public Health ,  Beirut, Lebanon  ( W Ammar PhD ,  H L Harb MPH );  Kabul, Afghanistan (P Anwari MD) ;  Department of Medical Emergency, School of Paramedic, Qom University of Medical Sciences, Qom, Iran  ( H Asayesh PhD );  South Asian Public Health Forum, Islamabad, Pakistan  ( R Asghar MD );  Faculty of Medicine and Health Sciences, Sana'a University, Sana'a, Yemen  ( Prof A M Assabri PhD );  Mashhad University of Medical Sciences, Mashhad, Iran  ( R Assadi PhD );  School of Health Sciences, University of Management and Technology, Lahore, Pakistan (U Bacha PhM) ;  Public Health Agency of Canada, Toronto, ON ,  Canada  ( A Badawi PhD );  Ministry of Health, Damascus, Syria (T Bakfalouni MD) ;  Charles R Drew University of Medicine and Science, Los Angeles, CA, USA  ( S Bazargan-Hejazi PhD );  College of Public Health and Tropical Medicine, Jazan, Saudi Arabia  ( N Bedi MD );  National Institute of Mental Health (A R Bhakta MD) ,  Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute  ( G A Mensah MD ),  National Institutes of Health ,  Montgomery Village, MD ,  USA  ( A M Durrani MD );  Centre of Excellence in Women and Child Health (Z A Bhutta  PhD),  Department of Paediatrics and Child Health (J Das MBA), Aga Khan University, Karachi, Pakistan (M I Nisar MSc); Centre for Global Child Health, The Hospital for Sick Children , Toronto, ON,  Canada (Z A Bhutta) ;  University of Iowa Hospitals and Clinics, Iowa City, IA, USA  ( A A Bin Abdulhak MD ); Transport and  Road Safety (TARS) Research (S Boufous PhD) ,  Brien Holden Vision Institute (S Resnikoff MD), University of New South Wales, Sydney , Acknowledgments This study was funded by the  Bill & Melinda Gates Foundation.
Actions • Complete an acute oncology service specification in 2015 and work to deliver a nationwide acute oncology service with cancer jointly with the British Geriatrics Society, which will be published within a few months. Some medical oncologists will take specific leading and organisational roles to address the important issues within cancer care for older people, such as the evaluation of frailty and concomitant morbidity. These roles should have support and protected time to ensure service developments and drive continuing professional development. As researchers, we would wish to provide leadership in the evaluation and development of therapies in older patients, supported by the National SACT database, currently unique in national cancer management.
However, for this approach, to become truly useful for patient care, it is essential to integrate ePROMs with the electronic patient records (EPR) to inform care and provide rapid access to specialist clinicians  [21, 22]. The future of patient-centred care will rely on health informatics, will promote intrinsic patient involvement via patient portals, integrating ePROMs with EPR to routinely monitor toxicity, inform individual care, collect comparative effectiveness data on treatments and service evaluation. The great potential of ePROMs is recognised but key challenges remain, including developing specific e-platforms, their interoperability and integration with EPR and clinical trials databases, and developing innovative care models that include ePROMs to monitor symptoms and toxicity with clinical algorithms leading to appropriate management.
The need to safely deliver modern care and the growth of new areas of activity must bring the number of medical oncologists per head of population to considerably more than 1:100,000, and this number is currently only provided in North West London  [2]. In 2011 (revised in 2013), the RCP recommended a requirement of a minimum of 2. 75 FTE posts per 200,000-250,000 population (representing 705-881 posts for United Kingdom, at 2013 population estimates). However, the specialty advisory committee (SAC) and the Association of Cancer Physicians has recently submitted a revised estimate of 1. 5 FTE per 100,000 population (representing 962 posts for United Kingdom). The European Society of Medical Oncology coordinates the development of medical oncology in Europe  [3]  and a recent publication  [4]  has noted that among 12 European countries which provided data on consultant medical oncology numbers, the United Kingdom has the highest ratio of patients with cancer to medical oncologists, but the fastest rate of growth in the specialty.
These groups of patients, who present via acute medical departments with a constellation of symptoms and are subsequently found to have cancer, represent 22% of all new cancers diagnosed each year in England, with lung, pancreas, and brain malignant tumours forming the largest group. Data collected by the National Cancer Intelligence Network (NCIN) have shown that, apart from acute leukaemia, the relative survival for all patients diagnosed via the emergency route is worse than for those diagnosed by non-emergency services. Emergency presentation patients are usually of poor performance status, often elderly, and have multiple comorbidities. They are less likely to receive a histopathological diagnosis of cancer, to see a specialist in their tumour type, or to receive treatment. It is clear this group of patients need properly co-ordinated pathways with early oncology and palliative care input to ensure appropriate care is given.
The ACP believes that this model is no longer fit for purpose and will where possible work with both commissioners and deliverers of cancer services to ensure the use of integrated electronic patient records shared between providers. This will facilitate the integration of clinical decision support and error checking, allow referrals and orders to occur in real time, enhance patient access and engagement, facilitate audit and research and has the potential to improve compliance with national data collection such as Cancer Outcomes Services Dataset (COSD) and the Systemic Anti-Cancer Therapy dataset (SACT)  [1, 2]. The first element of this strategy is where possible to ensure that all relevant cancer information regarding diagnosis, treatment and follow-up is created through the use of electronic systems rather than hand-written paper-based systems.
Shared data, shared systems With the universal use of NHS number, all relevant data can and should be linked to allow clinicians access to all available data through a single common portal or ideally a single electronic health record (EHR). The cancer record should however not be developed as a standalone system, distinct from the patients other health records. The ACP believe it is essential that data relevant to patients' cancer diagnosis is integrated into a hospital-wide EHR that links readily to primary care databases to ensure that effective communication between specialties/ services within any individual healthcare provider is not compromised. Although no individual system or supplier is supported or recommended by the ACP, the systems must be capable of sharing data using internationally agreed standards or cloud-based data repositories to facilitate the exchange of information between organisations.
Secure data, secure analysis The ACP recognises the fundamental importance of data security and the need to ensure the protection of personal and private healthcare data. We will therefore work with and on behalf of patient representatives to ensure the benefits of shared data analysis are appreciated by patients. We will work with national bodies such as the National Cancer Intelligence Network and the National Cancer Registration Service to deliver the maximum intelligence from the data collected. Data must be collected as a consequence of clinical care (not as a separate 'industry' in its own right) and must be used routinely to monitor the clinical and cost-effectiveness of new treatments and new pathways of care. Data access and software tools must be developed to facilitate a comparison of clinical outcomes including for example deaths within 30-days of chemotherapy across different organisations to ensure the adoption of best-practice across United Kingdom.
To support these disparate analyses, it is imperative that each organisation is required to collect data once in a form common to all regional and national requirements, ideally through the use of electronic health records. To this end, the ACP propose the adoption of a common data model based on the Cancer Services Outcomes Dataset (CSOD) to ensure that healthcare providers are able to readily export, combine and compare data between organisations without the need for complex data manipulation or transformation. All organisations wishing to use this data (e. g. COSD, cancer registration, national audits, SACT) must conform to this standard.
Next-generation informatics The last 20 years has seen massive advances in the molecular characterisation of cancer through technologies such as next-generation sequencing. The ACP believe that a similar focus needs to be applied to health informatics to maximise the benefit of new technology in this area. The ACP believes that the technology must adapt to the clinicians not an expectation for clinicians to adapt established ways of working. To this end, the analysis of plain-text with natural language processing and self-learning platforms, such as IBM's Watson, must be assessed and implemented to facilitate and enhance the collection of a coded dataset collected through automatic rather than manual data extraction  [5]. www. ecancer. org ecancer 2016, 10:608 The ACP would also want to see medical oncology at the forefront of new technologies including the development and use of 'wearable technology', mobile heath 'apps', rapid learning systems  [6] , and advanced clinical decision support.
Deliverables and monitoring 1. The ACP will seek wide support for a common dataset for all national data collection on cancer in 2016. In 2017, an agreed data model for cancer will be developed and published. 2. The ACP will work to ensure that patients have access to their medical records, by 2016. By 2017, the ACP wishes to see the widespread introduction of electronic access to electronic health records. 3. Work with commissioners and suppliers of cancer services to ensure the exclusive and nationwide use of electronic prescribing systems to support the delivery of chemotherapy. 4. Work with the NCIN to see the development of a portal of data driven presentations of clinical outcomes which can be configured at national, regional and local level. www. ecancer. org ecancer 2016, 10:608 Recent data from Cancer Research UK  [3]  suggest that of those patients diagnosed in 2010/2011 over 50% will become long-term survivors.
The governmental model of integration of health and social care, alongside third sector partnerships, will be a vital strategic route for this emerging area. Research in the area of survivorship will require shifts in our approach to study-design and a recognition of the need for protracted support for cancer survivors beyond routinely measured survival end points and beyond conventional hospital follow up. Cancer registries and other routinely collected health information may be useful in providing linkage of long-term data with outcomes from clinical trials, regarding late effects. The EORTC has developed a broad strategy for survivorship research and development  [28]. While there are well recognised, though physician defined, QOL measures it is likely that evaluation of interventions and service developments for cancer survivors will be assessed using patient reported outcome measures (PROMs), which will drive the quality of survivorship care.
decision making -the pinnacle of patient-centered care N Engl J Med 366 780-781 DOI: 10. 1056/NEJMp1109283 PMID: 22375967 22. Legare F, Stacey D and Turcotte S et al (2015) Interventions for improving the adoption of shared decision making by health- care professionals Cochrane Database Syst Rev 9 1-164 23. Taylor C, Finnegan-John J and Green J (2014) ".
Data collection Wave data were collected in the Beaufort Sea during the 2014 open water season using three Surface Wave Instrument Floats with Tracking (SWIFTs). SWIFTs are freely-drifting buoys that collect observations in ten-minute bursts at the top of each hour, and burst results are transmitted hourly via Iridium satellite antenna. SWIFTs 10 and 11 were deployed on July 27 from the R/V Ukpik roughly 100 km north of Alaska. They followed approximately the same path until September 1, when SWIFT 10 was caught in partial ice cover until September 15. Both buoys were recovered from the R/V Norseman II between September 28-29, 2014. SWIFT 15 was deployed and recovered from the R/V Araon from August 5 to 17, 2014, and was in partial ice cover the entirety of its deployment. Drift tracks of the SWIFTs are shown in Figure  1.
Additionally, photos were taken at 0. 25 Hz during data collection bursts from a mast-mounted uCAM serial camera. A more detailed description of the SWIFT platform can be found in  Thomson (2012). Time series of measured wind speed, significant wave height, energy-weighted period, and peak wave direction are shown in Figure  2. The anemometer and lower hull were lost from SWIFT 10 on September 15 as it exited the ice. As wind speed and wave directions are missing after this time, Navy Global Environmental Model (NAVGEM) wind speeds and wave direction were used instead  (Hogan et al. , 2014).
7 × 10 ߯ -7 0. 98 (5) with a coefficient of determination of R 2 = 0. 79. Nondimensional variables calculated with NAVGEM model winds, at the end of SWIFT 10's deployment, scattered well with the rest of the data and negligibly changed results. The strength of this scaling supports the dependence of wave energy flux in the Beaufort Sea on sea ice extent, as observed in 2012  (Thomson and Rogers, 2014). Scaling analysis such as this is susceptible to spurious correlation, due to the presence of U 10 in all nondimensional variables. We find a spurious correlation of R 2 = 0. 37 when a random number generator is used for distances. The spurious correlation is primarily due to the correlation between wind speed and wave height, and confirms that the correlation of distance with wave energy is in fact significant.
The original analysis of these data in  Thomson and Rogers (2014)  used a different ice product from the NIC and did not screen duration-limited waves to find a slope of χ 1. 6 for the scaling of wave energy with open water distance. Recalculating open water distance with the IMS ice product reduced the slope to χ 1. 35 , and the R 2 to 0. 50. The screening of duration-limited and unsteady points excludes 73% of the data (see Table  1 ), and increases the R 2 to 0. 57, with a slope of χ 1. 56. A summary of the data set is presented in Table  1. Interestingly, the results of this reanalysis are distinct from the analysis of the 2014 data set. Although identifying the distance-limited waves improves the coefficient of determination, the slope of the regression is much greater than that found from the 2014 results (Figure  10 ), and falls outside the range of coefficients previously stated in the literature  (Young, 1999).
The reasons for these differences are not clear, but illustrate the limitations of these methods. Thomson and Rogers (2014)  found the power-law fit to wind sea waves, where wave age is less than one, to be in much closer agreement to the expected dependence found in the literature. We found a similar slope for wind sea waves in our reanalysis, χ 0. 98. Based on records from the National Ice Center (  2008 ), the 2012 Arctic Ocean open water season had the lowest ice extent recorded. As duration limitation is more likely to occur in ocean with large open water area, the substantial ice retreat may have led to wave conditions less frequently limited by basin size. The method we have developed based on the 2014 data set for identifying distance limitation appears to be less applicable to data from 2012, where wave age plays a larger role.
Conclusions We have demonstrated a significant exponential relationship between the nondimensional wave energy and open water distances across the Beaufort Sea. Waves measured in both 2012 and 2014 in this region indicate that the hard ice edge limits fetch in a conventional manner. Scatter in the dataset indicates that the ocean rarely behaves in a purely distance-limited manner and will also be limited by wind duration. An empirical screening for relatively stationary winds and waves suggests that the likelihood of distance limitation decreases later in the summer and may decrease with reduced ice extent in general. Wave field observations during this year may be indicative of the new trend for waves in the emerging Arctic Ocean; with decreasing ice cover predicted, we are likely to observe more wave energy.