dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Publicly available SNP array data. The raw data from 5,416 tumours in 16 different cancer types were downloaded from The Cancer Genome Atlas (TCGA) between 02/04/2013 and 07/07/2014. Allele-specific copy numbers (CN) were produced using ASCAT  33  after prior normalisation using the Aroma software  34  , all in the R statistical computing framework  35. Only segments of length >10 probes were considered. Cancer types and the samples in them are described in Table  1. For cancer type, the distributions of baseline CN and percentage of genome altered was calculated for further use. LogR and BAF data could not be retrieved for 8 out of 5,416 samples. Baseline copy number and percentage of genome altered. The percentage of genome altered was calculated for each sample using segmented allele-specific CN.
5-8 v1. 2 BeadChip (Illumina) run according to the manufacturer's instructions. LogR values and B allele frequencies (BAF) were extracted from the Illumina GenomeStudio software. One sample (Mixture 8 with all four clones at an equal ratio of 25%) did not pass quality control (call rate <0. 98) and was discarded. LogR values were further normalised using the genomic wave correction tool from the pennCNV software suite  36  and the CN profiles of the 15 remaining samples were computed using ASCAT. The Shannon diversity indices were calculated using the clonal frequencies of each mixture to represent their expected diversity.
TCGA samples. Description of the 16 datasets downloaded from the TCGA and the related samples. related profiles (1 clonal, 4 subclonal profiles per group). Clonal combinations were therefore order-sensitive, as mixing clones 1, 2 and 3 at respective frequencies 45%, 30% and 25% would yield a mixture different to combining clones 3, 1 and 2 at the same frequencies.
Acknowledgements The results published here are in whole or part based upon data generated by the  TCGA Research Network : http:// cancergenome. nih. gov/. This research utilised  Queen Mary 's MidPlus computational facilities, supported by  QMUL Research-IT  and funded by  EPSRC  grant  EP/K000128/1. PM and TAG were funded by  Cancer Research UK  and the  Higher Education Funding Council for England (HEFCE). PM benefitted from an  EMBO  short term fellowship ( ASTF 400-2014 ). TAG and PM are grateful to  Salpie Nowinski  for insightful discussions, and to  Dr. Sarah Martin  for the gift of the cell line used in this manuscript.
Setting and data collection Full details of the EPIPAGE 2 cohort recruitment and data collection were previously reported. (13)  In brief, all infants live born or stillborn and all terminations of pregnancy from 22 0/7 to 34 6/7 weeks of gestation in 25 French regions involving 546 maternity units were eligible. Infants were included in 2011 at 3 different periods by gestational age at birth: 8month recruitment for births at 22-26 completed weeks of gestation, 6-month recruitment for 27-31 weeks, and 5-week recruitment for 32-34 weeks. Maternal, obstetric, and neonatal data were collected prospectively by following a standardized protocol.
However, this situation seems unlikely because data on main risk factors were collected with standardized procedures and neonatal outcomes were defined by international classifications. The main limitation is inherent to all studies of PPROM. As the EPIPAGE 2 cohort was not designed to consider late-preterm births, the population was truncated for cases of PPROM occurring before 35 weeks but delivery afterwards. Therefore, we chose to include women with PPROM at 24 to 32 weeks, who delivered at 24 to 34 completed weeks. Thus, there is no truncation for the lower boundary and we were able to analyze all women giving birth within 2 weeks after late PPROM. We likely only missed the very few births with the longest latency durations and the best prognosis. (24, 25)  Overall, we can consider that the risks are slightly overestimated, particularly for long latency durations.
Interpretation According to our finding of no impact of latency duration on neonatal prognosis for a given gestational age at birth, we can assume no deleterious effect of prolonged latency that could outweigh the benefits of the fetal maturation gain. (9)  This result is consistent with those reported by Manuck et al. , who studied preterm infants with PPROM at 22 to 33 completed weeks (n=306) identified retrospectively from a bicentric database, and management for their mothers followed current recommendations. (11)  The authors used similar definitions of exposure and outcomes as in our study and found no association between latency duration and perinatal morbidity after controlling for gestational age at birth. (11)  In contrast, the analysis of a retrospective database including cases of PPROM from 1997 to 2012, from a network of 330 NICUs in the United States, showed increased risk of death and morbidity for latency duration > 28 days as compared with duration 1 to 7 days after controlling for gestational age at birth.
EPIPAGE 2 was funded by the following: 1) The  French Institute of Public Health Research/Institute of Public Health  and its partners: the  French Health Ministry , the  National Institute of Health and Medical Research (INSERM) , the  National Institute of Cancer , and the  National Solidarity Fund for Autonomy  (CNSA). 2) The  National Research Agency  through the  French EQUIPEX program  of investments for the future (reference  ANR-11-EQPX-0038 ). 3) The  PREMUP Foundation. Elsa Lorthe  was funded by the following: 1) The  Société Française de Médecine Périnatale (SFMP) , 2) The  Journées Francophones de Recherche en Néonatalogie (JFRN). For both studies, the funders had no role in the study design, data collection and analysis, preparation of the manuscript or decision to publish.
To do so, we present the results of a quasi-experimental study with young voters conducted in 24 EU member states. Since we have carried out a quasi-experiment with self-selected student samples, our data are neither representative for any of the countries under investigation nor for young voters. Hence, like other lab-experiments our data lack external validity. Nevertheless, these data are very valuable as it has a high degree of internal validity. In addition, there is evidence that voluntary student samples are appropriate for experimental effect studies (see  Druckman and Kam 2011). We proceed as follows: In the next section (2. ), we will bring together some of the key arguments from the EU's democratic deficit-debate, hinted at above, and the literature on televised debates to develop expectations about the possible effects of the Eurovision debate on voters: This will allow us to analyse, in the next step, whether political competition improves voter knowledge about candidates, whether it motivates them to cast a vote, and whether competition affects EU-related attitudes.
Pioneering approaches for globally coordinated crop research (initially rice, wheat, and open-pollinated maize) emerged during the Green Revolution (3). The International Wheat Improvement Network (IWIN), part of the CGIAR system, tests new wheat genotypes at approximately 700 field sites in over 90 countries. Breeding, directed toward 12 different mega-environments that represent a range of temperature, moisture, and disease profiles, is conducted at strategic research hubs to develop around 1000 high-yielding, diseaseresistant lines. These are targeted to major agro-ecologies and are delivered annually as international public goods (IPGs). Data on adaptive responses of new lines is shared within IWIN to refine research and breeding methods  (4) (5) (6). IWIN-related varieties cover more than half of the wheat area in LDCs, giving additional value (attributable to IWIN research) of between $2.
2 and $3. 1 billion per year, spread among resource-poor farmers and consumers  (6). The benefit-cost ratio of this investment is over 100:1, without considering the added value of avoiding devastating disease pandemics by breeding for disease resistance  (5) , or the estimated 20+ million hectares of ecosystems that have been spared cultivation as a result of increased productivity  (7). IWIN has amassed a database of over 20 million phenotypic data points that are beginning to be exploited, e. g. , in modeling crop responses to climate changes (4).
DATA INTEGRATION Common standards are required if data are to be comparable across experimental variables, including germplasm, environments, and other research interventions. Highthroughput phenotyping and genotyping protocols have some agreed standards  (9, 13). Standardized protocols also add value in modeling studies where core data sets drive simulations, permitting alternative interventions to be evaluated and prioritized. Crop models have been used to estimate impacts of climate change on crop performance (4,  10 ), yet many breeding and agronomy data sets do not fulfill core needs to drive models. Research institutions and funding bodies could facilitate more timely data sharing by prioritizing publication of results linked to open-access data. Data sharing will drive standardization toward more precise descriptions of environments and experimental treatments, and more "searchable" databases  (14).
For 2017, the total budget expected for crop research-including trait discovery, variety development, and seed systems-is approximately $200 million, covering the major cereals, legumes, roots, and tubers. These crop networks would constitute the major components of a GCIN, while additional investment would underpin and improve NARS infrastructure, encompass underutilized crops-e. g. , quinoa (16)and achieve cohesion and a strategic vision. Some costs would be offset by economies of scale, including shared infrastructure, data, and best practices across crops. IWIN and other crop-testing networks rely heavily on voluntary data return, in exchange for shared germplasm and other technologies generated from initial investment in CGIAR programs. A GCIN would thus count on substantial leverage of a massive body of human, physical, and scientific capital from existing crop networks in return for shared benefits of multilateral collaboration and improved research infrastructure.
Recently established crosscutting CGIAR platforms, including one to harness big data and another for modernization of breeding methods within CGIAR centers, would also contribute to a GCIN's goals. Some multinational seed companies have invested heavily in infrastructure at key research locations, and the model is no less compelling for achieving food security through provision of IPGs (e. g. , Feed the Future, CGIAR, Bill & Melinda Gates Foundation). Some elements of a GCIN could be supported by hybrid funding mechanisms such as the Foundation for Food and Agriculture Research model that requires 50% private industry investment to receive government funding, or the Phytobiome Alliance PPP. There is an IP challenge when private entities join forces, but many are moving in the direction of cooperating at the precompetitive level to share risks and costs associated with basic research.
ACKNOWLEDGMENTS The authors acknowledge support from the  U. S. Agency for International Development (USAID) , the  Bill & Melinda Gates Foundation  ( OPP1052535 ), and the  CGIAR Platform for Big Data in Agriculture. The views and opinions in this paper are those of the authors and not necessarily those of  USAID. This work is licensed under a Creative Commons Attribution 4. 0  International  ( CC BY 4. 0 ) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit http://creativecommons. org/licenses/by/4. 0/. This license does not apply to figures/photos/artwork or other content included in the article that is credited to a third party; obtain authorization from the rights holder before using such material.
The AWS instruments include upward and downward looking shortwave radiometers on the ice caps that allow direct measurement of the albedo at the station locations (Section 2). However, the stations provide only point measurements that do not capture the spatial variability of the albedo over the whole extent of the ice caps. Optical satellite remote sensing techniques provide the opportunity to complement these data with spatially distributed observations. In particular mid-resolution sensors with a large swath, like the MODerate resolution Imaging Spectroradiometer (MODIS), have a daily repeat cycle that enables the reconstruction of the land surface albedo with a temporal resolution that is compatible with the rapid changes of the ice cap surfaces. Among the remote sensing albedo products, the MODIS Bidirectional Reflectance Distribution Function (BRDF)/albedo products are probably the most widely used  [14].
This update is of relevance for albedo studies since it is designed to remove a long-term drift in the visible and near-infrared bands caused by the aging of the MODIS sensor aboard the Terra satellite that was observed in the Collection 5 dataset  [21, 22]. Also, relevant for this study, is the improvement of the geolocation grid based on a new terrain correction and updated sensor geometric parameters  [23]. The cloud masking algorithm was also improved, but the expected gain in the number of observations mainly affects desertic and very humid tropical areas  [24]. MCD43 version V006 achieved a stage 3 validation status according to the NASA MODIS land team, which means that "its accuracy is well less than 5% albedo at the majority of the validation sites studied thus far, and even those albedo values with low quality flags have been found to be primarily within 10% of the field data.
Data for solar zenith angles greater than 70 degrees should be considered suspect. " (Validation Status for MODIS BRDF/Albedo: MCD43, Product version: Collection 5/6, November 2015). Here, we present an evaluation of the latest MODIS albedo product (MCD43A V006) in Iceland. Our first motivation is to assess the reliability of this product for future assimilation in an energy and mass balance model of the Icelandic ice caps. Therefore, given the persistent cloud coverage in Iceland, we started by analyzing the amount of missing data in the MCD43 albedo product (Section 3. 1). Then, we used continuous incoming and outgoing shortwave radiation measurements made at 10 AWSs in the period 2001-2012 on Vatnajökull and Langjökull ice caps to assess the product accuracy (Section 3. 2). In addition, higher resolution images from Landsat were used to assess whether subpixel variability can help explain the discrepancies between the in-situ and MODIS data (Section 3.
3). Finally, albedo maps for Iceland are generated to quantify the effect of dust deposition on the albedo of the ice caps in the aftermath of the two aforementioned volcanic eruptions (Section 3. 4). The previous version of the 16-days MODIS albedo (v005) albedo was evaluated in the period 2000-2008 using two stations on Tungnaárjökull, the western outlet glacier of Vatnajökull (AWSs tunef and tunmi, Figure  2 )  [8]. The RMSEs with respect to the in situ albedo for the two stations were 0. 12 and 0. 09 with no systematic deviations. Since then, a new version of the MCD43 product (V006) has been generated based on the reprocessed collection 6 level 1 MODIS data (released in 2015). Collection 6 includes the latest improvements made by the MODIS science team since the release of collection 5 in 2008  [20].
2). Missing annual coordinates values (8%) were filled by the average of the other years' coordinates for each AWS. As recommended by the data provider  [15, 26] , the data were discarded if the solar zenith angle is higher than 70° at the time of the latest MODIS acquisition. Before 02 July 2002, the MODIS sensor was only onboard the Terra platform, hence the acquisition time is around 10:30 in local solar time (LT). After that date the acquisition time is given by Aqua overpass time  (13:30 LT). The data that were flagged as bad quality retrievals in the MODIS product ancillary files were not removed to maximize the number of observations. These quality flags are very conservative and lead to a strong reduction of the number of observations if flagged data are removed (about ten times less values, up to zero data at one station).
2). Missing annual coordinates values (8%) were filled by the average of the other years' coordinates for each AWS. As recommended by the data provider  [15, 26] , the data were discarded if the solar zenith angle is higher than 70 • at the time of the latest MODIS acquisition. Before 02 July 2002, the MODIS sensor was only onboard the Terra platform, hence the acquisition time is around 10:30 in local solar time (LT). After that date the acquisition time is given by Aqua overpass time  (13:30 LT). The data that were flagged as bad quality retrievals in the MODIS product ancillary files were not removed to maximize the number of observations. These quality flags are very conservative and lead to a strong reduction of the number of observations if flagged data are removed (about ten times less values, up to zero data at one station).
This was done with the NASA's MODIS reprojection tool without changing the original resolution (approximately 500 m) using the nearest neighbor resampling method. We did not use the bilinear or cubic resampling method to avoid smoothing the albedo transition between the snow cover and the bare ice surfaces. Also, the implementation of these methods increases the number of no-data values since a pixel will get no-data value if at least one adjacent pixel is no-data. From these data we built annual summer albedo composite images by calculating, for each pixel and for each year, the mean values of all available observations between 1 July and 1 August (Section 3. 4). During this period the glacier surfaces are the least affected by recent ash or snow falls. A two-month period of integration was needed to sufficiently reduce the large number of missing data due to cloud obstruction.
The mean and standard deviation of the annual summer albedo values within the area of the six main ice caps was computed from each annual summer composites to study the temporal evolution of the albedo for each ice cap (Figure  2 ). The ice caps polygons were obtained from the Corine Land Cover 2006 in Iceland, which was performed by the National Land Survey of Iceland. We have used this database because it gives the ice caps area for the year 2006, which is in the center of our study period  (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012). Last, we mapped the effects of the 2010 and 2011 eruption using the 2009 composite image as a reference. An albedo anomaly map was computed for both events by taking the difference between the reference and the post-eruption composites.
The daily albedo was computed as the ratio of the upward to downward daily incoming and reflected shortwave radiation totals (daily integrated albedo as in  [16] ). Data in the late autumn and winter when the solar elevation is low cannot be used to calculate the surface albedo and were therefore excluded (Table  1 ). The radiative fluxes were measured using Kipp and Zonen CM14, CNR1 or CNR4 sensors (Table  1 ), all with relatively uniform spectral response over the range 0. 3-2. 8 µm, and uncertainty that has been estimated to be 3-5% on an ice sheet surface  [28, 29]. Exceptions are the stations hoffne, where the shortwave radiation was measured using LI-COR SPLite (incoming) and LI-COR (reflected) radiometers, and hoffef from 2001 to 2008 where LI-COR was used for both the incoming and reflected short wave radiation.
Landsat Data To study the effect of the subpixel variability on the accuracy of the MODIS retrieval, we used the Landsat surface reflectance data from the United States Geological Survey. These images provide the surface reflectance at 30 m resolution for each Landsat spectral band after atmospheric correction. The surface reflectance values were converted to broadband albedo values using a narrowband-to-broadband albedo conversion for glacier ice and snow based on band 2 and band 4  [31]. This empirical formula was obtained from airborne measurements performed over Vatnajökull and Greenland  [31]. A collection of 15 Landsat 5 and Landsat 7 scenes of the study area, that have a limited cloud cover based on the inspection of their quicklooks in the USGS Earth Explorer portal  [32] , were downloaded.
Comparison within Situ Data There is an overall good visual and statistical agreement between the MODIS shortwave broadband black-sky albedo and the in-situ albedo AWS measurements (Figures  4  and 5 , Table  3 ). The seasonal variability in the in situ dataset is very high, ranging from 0. 05 to 1 and is well captured by the MODIS product, as demonstrated by the high correlation coefficients (Table  3 ). When considering all the values, including low quality retrievals, the median of the correlation coefficients is 0. 84, close to the value of 0. 82 reported in Greenland  [16]. The low consistency between the AWS and MODIS albedo at AWS hoffne and partly at AWS hoffef is probably partly due to the poor quality of the LI-COR radiometers used at those sites. Excluding AWS hoffne, there are still rather large differences during the summer at the lowest ablation stations breid, brune and langne.
Subpixel Variability Some combinations of Landsat scenes and AWS site (Table  2 ) returned no-data in the computation of the standard deviation of variability within a MODIS sized pixel because all the pixels within the extraction mask were saturated for at least one of the bands (band 2 or 4) used to compute the broadband albedo (Figure  6 ). The number of values that were used to compute the standard deviation per AWS site ranged between 3 and 10. The RMSE of the difference between the MODIS and in-situ albedo (Section 3. 2) was plotted against the mean of the standard deviations (STD) obtained from the extraction of the Landsat albedo (Figure  7 ). The results do not show a clear relationship (Spearman correlation is 0. 48, p-value is 0. 17), but the plot suggests that the RMSE of the difference between the in situ and MODIS data tends to increase when the corresponding Landsat subpixel spatial variability is higher.
Conclusions The large variability in the albedo of Icelandic ice caps is successfully captured with the low resolution MODIS albedo product MCD43A3, in spite of frequent cloud obstructions. We found a good agreement with in situ albedo data, even by including the retrievals flagged as low quality. A limitation of this comparison is the scale mismatch between a MODIS pixel and the AWS footprint. Field knowledge and Landsat data indeed suggest that there is a link between the variability of the albedo within a MODIS pixel and the discrepancy between AWS and MODIS data, however we did not identify a strong relationship. Higher resolution imagery might help resolve small scale   4 , Figure  9 ). The albedo drop is larger in the accumulation areas where the "normal" summer albedo is typically higher than in the ablation areas due to the presence of snow or firn (Figure  9 ).
Conclusions The large variability in the albedo of Icelandic ice caps is successfully captured with the low resolution MODIS albedo product MCD43A3, in spite of frequent cloud obstructions. We found a good agreement with in situ albedo data, even by including the retrievals flagged as low quality. A limitation of this comparison is the scale mismatch between a MODIS pixel and the AWS footprint. Field knowledge and Landsat data indeed suggest that there is a link between the variability of the albedo within a MODIS pixel and the discrepancy between AWS and MODIS data, however we did not identify a strong relationship. Higher resolution imagery might help resolve small scale variability around the station that is not resolved in Landsat 30-m data but that is known from the experience of the field visits.