dataesr/scientific-paragraphs-categorization huggingface dataset:
Characterization of cell line clusters at the transcriptomic and mutational levels. Gene set enrichment analysis (GSEA) was performed on genes modules built in step (A) of the biological network-driven process described above. We identified up-regulated or down-regulated gene modules, associated with each cell line cluster. An analysis was first performed to identify genes differentially expressed between a particular cluster and all the other cell lines, based on a linear model. For a given cluster k, cell lines were partitioned into two groups j = {Cluster-k, non-Cluster-k}. We then performed a differential analysis by comparing the mean gene expression of each group in a linear model (limma R package  47  ). The analysis was performed separately for each dataset. The results were used to rank genes in order of significance and to search for overrepresented gene modules, by pre-ranked gene set enrichment analysis (GSEA).
Identification of cell line clusters common to different studies. We studied the likeness between the clusterings for CCLE and GDSC, by clustering the cell lines with a similarity matrix (hierarchical clustering with Pearson's metric and the Ward agglomerative method). The similarity matrix contains the number of times two cell lines are clustered together in each dataset (0 = never, 1 = only in one classification, 2 = in both classifications). This similarity matrix constitutes a natural visualization tool for assessing the consistency between two clustering patterns. In particular, if we associate a color gradient to the 0-2 range of real numbers, such that white corresponds to 0, and dark blue corresponds to 2, and if we assume that the matrix is arranged so that items belonging to the same cluster are adjacent to each other (with the same item order used to index both the rows and the columns of the matrix), a matrix corresponding to a perfect consensus will be displayed as a color-coded heatmap characterized by blue blocks along the diagonal, on a white background.
Acknowledgements The authors thank  Sergio Roman-Roman  for reviewing the study and the manuscript. This work was supported by the  Department of Translational Research of the Institut Curie  and the  Fondation ARC (Association pour la Recherche contre le Cancer  Grant No. DOC20160604097 ). Funding was also obtained from the  Site de Recherche Integrée en Cancérologie/Institut National du Cancer  (Grant No. INCa-DGOS-4654 ).
The aerosol characterisation included total particle counts above 3 nm, total cloud condensation nuclei counts, mass concentration, number concentration size distribution, aerosol chemical analyses and elemental analysis. The campaign captured varied meteorological conditions, including two extreme heat events, providing a potentially valuable test for models of future air quality in a warmer climate. There was also an episode when the site sampled clean marine air for many hours, providing a useful additional measure of the background concentrations of these trace gases within this poorly sampled region of the globe. In this paper we describe the campaign, the meteorology and the resulting observations of atmospheric composition in general terms in order to equip the reader with a sufficient understanding of the Wollongong regional influences to use the MUMBA datasets as a case study for testing a chemical transport model.
, 2011 ) is likely to be especially useful for testing air quality models due to the challenges of modelling within the complex topography of coastal areas. The locations of all of the sites used in the MUMBA campaign are marked on the satellite view of the region shown in Fig. 1c. 04:00-09:00, 10:00-18:00 and 18:00-04:00 daily 04:00-09:00, 10:00-18:00 and 18:00-04:00 daily ppb 4-15 Feb campaign and their limits of detection. Note that some instruments can produce negative values when the concentrations are close to the detection limit. Negative concentration values (although non-physical) have not been removed from the MUMBA dataset because they are indicative of the instruments' true performance; removing negative values will produce small positive biases in the calculations of longer-term average concentrations.
Additional measurements Measurements were also available from the OEH air quality station at Wollongong (34. 419 • S, 150. 886 • E). Additional instruments were operated nearby at the University of Wollongong's main campus (at 34. 406 • S, 150. 897 • E)  (Buchholz et al. , 2016)  and at the nearby Science Centre (34. 401 • S, 150. 900 • E), but the observations are not included here. Data from the University of Wollongong include retrievals of total column amounts of trace gases from the Total Carbon Column Observing Network (http://www. tccon. caltech. edu/) and the Network for the Detection of Atmospheric Composition Change (http://www. ndsc. ncep. noaa. gov/) as well as in situ greenhouse gas measurements (http://doi. pangaea. de/10. 1594/PANGAEA. 848263). The instrument installed at the Science Centre was a multi-axis differential optical absorption spectrometer, and the data are available from the authors upon request.
7 • C, which is 0. 9 • C above the long-term average of 24. 8 • C and in the 95th percentile of monthly mean maximum temperatures for January at Bellambi AWS (using data from 1988 to the present). The average wind speed recorded at the MUMBA site during the campaign was 2. 8 ms -1 , and the maximum hourly averaged wind speed recorded was 9. 2 ms -1. The 1st, 2nd (median) and 3rd quartiles of the wind speed were 1. 4, 2. 6 and 3. 9 ms -1 respectively. Figure  3  shows the composite diurnal cycles of the wind speed and wind direction as measured at the main MUMBA site. The general pattern was a relatively strong sea breeze during the day (∼ easterly winds of 3-4 ms -1 ) and calmer conditions overnight. Westerly winds were more frequent during nighttime (although northeasterly winds sometimes persisted into the night).
, 2015). In the Wollongong region, an increased boundary layer height and strong sea breezes combine to produce the low radon levels observed in the afternoon. Comparisons of the winds measured at the MUMBA site during the campaign to simultaneous measurements at the three air quality sites operated by the Office of Environment and Heritage in the area (at Wollongong, Kembla Grange and Albion Park) indicated that the wind patterns observed at the MUMBA site were generally representative of the region as a whole  (Guérette, 2016). The long-term average wind data at 15:00 each day are publicly available from the Bellambi AWS from 1997-2010, and these were used for comparison with the wind data recorded at this time throughout January during the campaign. The MUMBA site in January 2013 was characterised by slightly less frequent northerly winds and slightly more frequent westerly winds than expected from the long-term average at Bellambi, but otherwise the wind patterns were very similar in the two records.
Urban, marine and biogenic influences during the MUMBA campaign The MUMBA campaign was designed to characterise the atmospheric composition at the ocean-forest-urban interface and thereby provide a dataset that could be used to test the skill of atmospheric models within a coastal environment. In this section, the major urban, marine and biogenic sources that influence the atmospheric composition in the region are described. The dominant anthropogenic sources in the region are the Port Kembla steelworks, located approximately 10 km south of the main MUMBA site (for PM 2. 5 , PM 10 , CO, NO x and SO 2 ) and motor vehicles (for NO x , CO and VOCs) (http: //www. npi. gov. au/npidata). The ocean lies to the east of the site and large forested areas are to the west. Outflow from the Sydney basin (80 km to the north) may accompany winds from the northeast.
Summary and conclusions The combined datasets from MUMBA provide a useful case study for testing the skill of air quality models in the complex environment of urban, marine and forest influences in coastal Australia, where the majority of its inhabitants live. This overview paper aims to provide the reader with a sufficient understanding of the MUMBA campaign to use the datasets as a test case for any air quality model, including an understanding of the Wollongong urban airshed, regional topography, emissions and meteorology. During the 8 week campaign, the MUMBA site experienced some very different conditions, ranging from relatively polluted air (with local urban pollution from traffic and nearby industrial sources) to unpolluted marine air with a composition akin to that representative of the remote marine boundary layer measured at the Cape Grim station under baseline conditions.
There were two extreme heat events during MUMBA when westerly winds brought strong biogenic influences from nearby forested regions. The measurements of the atmospheric composition during these events provide data that could prove to be a valuable test of models of future air quality in a changing climate. A series of papers are in preparation that describe the main scientific findings from the MUMBA campaign, including articles focusing on (1) the drivers of urban air quality, (2) marine air at 34 • S, (3) biogenic emissions of volatile organic compounds, (4) the drivers of aerosol loading in the airshed and (5) new particle formation events. In addition, the MUMBA campaign measurements are being used in conjunction with long-term measurements from the OEH air quality network and campaign data from the Sydney Particle Study campaigns  (Keywood et al.
A8 Weather station Two different weather stations operated during MUMBA providing common meteorological parameters including temperature, humidity, pressure, wind speed and direction. The switch occurred on 25 January when the original (borrowed) weather station was needed for another field campaign. The Digitech system operated at a 5 min resolution and provided wind direction as 16 quadrants only, whereas the original station (Campbell Scientific Inc. , Logan, UT, USA) operated at a 1 min resolution and provided wind direction with degree resolution. Both records are available on PANGAEA as hourly averages.
Acknowledgements. The authors would like to thank all those from the  University of Wollongong's Centre for Atmospheric Chemistry and CSIRO's Climate Science Centre group , who helped with the logistics of undertaking an extensive measurement campaign, in particular  Travis Naylor ,  Graham Kettlewell ,  Christopher Caldow ,  Frances Phillips ,  Jason Ward ,  James Harnwell  and  Jenny Fisher. The  ANSTO technical  staff responsible for installing and maintaining the Warrawong radon detector were  Ot Sisoutham  and  Sylvester Werczynski. Thanks are also due to  Kids Uni  and the  Science Centre  for their helpful support and to  David Carslaw  (and all the statisticians who developed the relevant "R" code) for public access to the excellent openair package for the analysis of air quality data.
3 Description of the instruments deployed at the main measurement site ning throughout the 8 week campaign and aerosol-phase measurements added in the second half of the campaign. A few instruments operated for different time periods, and these A large range of instrumentation was deployed to enable a are distinguished in Table 1. All data are available from PAN- detailed characterisation of atmospheric composition during GAEA (https://doi. pangaea. de/10. 1594/PANGAEA. 871982) the campaign. All measurements made during the campaign as hourly averages unless otherwise specified. Further details are listed in Table 1, along with the dates of operation for of the instruments are given in the Appendix along with a each instrument. MUMBA operated in two distinct stages second table that lists the specific VOCs measured during the with most gas-phase and meteorological measurements run-.
Note that the PTRMS was run under three differing instrumental settings (due to an accidental change in the dwell time setting). Thus three different detection limits are listed. See the metadata in PANGAEA for more details. * An additional 11 data points were excluded due to analytical problems. www. earth-syst-sci-data. net/9/349/2017/ Earth Syst. Sci. Data, 9, 349-362, 2017.
Scope Hospital Episode Statistics Admitted Patient Care (HES APC) data are collected on all admissions to National Health Service (NHS) hospitals in England. HES APC also covers admissions to independent sector providers (private or charitable hospitals) paid for by the NHS. 1  It is estimated that 98-99% of hospital activity in England is funded by the NHS. 2  A hospital admission includes any secondary care-based activity that requires a hospital bed, thus including both emergency and planned admissions, day cases, births and associated deliveries. HES APC does not cover accident and emergency (A&E, emergency department) attendances or outpatient bookings; these data are held in separate HES databases. All HES databases are collated and curated by NHS Digital (previously the Health and Social Care Information Centre).
Processing cycle and frequency of data collection Upon discharge from the care of a particular consultant, the treating clinician completes a discharge summary for the patient of diagnoses made and procedures carried out during that FCE (where procedures include surgery, diagnostic imaging, ventilation and infusion/transfusion therapy). Discharge summaries are forwarded to a clinical coding department in the hospital, who enter the information onto the local electronic patient information database. Clinical coders undergo nationally accredited training programmes and follow standardized rules for translating information on discharge summaries into clinical codes. 10, 11 very month, data are extracted from local hospital databases to the Secondary User Service (SUS), a national data warehouse housed within NHS Digital.
12 Data from the SUS are extracted both for purposes of hospital reimbursement under PbR, and separately to create a provisional monthly HES extract. NHS Digital carry out basic data checks and cleaning, add geographical fields based on patient postcodes, and attach pseudonymized patient identifiers ('HESIDs') to each episode. 13, 14 At the end of each financial year, NHS Digital allow hospitals one further data submission to HES (the 'Annual Refresh'), after which a provisional annual HES extract is produced for final review by hospitals. Once the Annual Refresh has been checked, a final annual HES dataset is made available. 12 nkage within HES APC From 1997/98 onwards (when patients' NHS numbers became a mandated return from hospitals), HES APC episodes have been linked longitudinally to the same patient by tagging episodes with the HESID.
This alphanumeric variable allows patient follow-up, yet avoids the need for supplying patient identifiers to researchers. The methods used to generate the HESID have been described elsewhere. 15 Each HES APC extract contains a unique set of HESIDs to reduce the risk of individual disclosure through merging separate data extracts supplied to different research teams. HES APC is also routinely linked to a number of external datasets. The Clinical Practice Research Datalink,  16  a large UK primary care database, is linked to HES APC on a monthly basis. HES APC is linked to dates and causes of non-hospital deaths from the Register of Deaths in England and Wales held by the Office for National Statistics (for deaths registered since 1 January 1998), also on a monthly basis. 17 Only deaths of patients recorded in HES APC are available through this linkage (i.
Linkage to other datasets NHS Digital also provides a trusted third-party bespoke linkage service, through which secondary users can request that HES APC data be linked to other external datasets. For example, both national disease registries (such as the National Joint Registry  18  and the UK Renal Registry  19  ) and well-established cohort studies including Whitehall II  20  and the Hertfordshire Cohort Study 21 have been linked to HES APC. Secondary users need to obtain the appropriate approvals to enable these linkages.
Measures Clinical and patient data HES APC provides detailed clinical, demographic and organizational information for each FCE (see Table  1 ), with 270 variables available in the core dataset. Apart from data on diagnoses and procedures, HES APC contains information on dates of admission, operations and discharge, admission method (e. g. emergency or planned), care provider and many geographical variables mapped from a patient's postcode. The local health geographies and hospital providers in England have changed several times since 1997, and thus care needs to be taken to ensure continuity when carrying out local or provider level analyses that use HES APC data covering many years. Socioeconomic status is measured by the Index of Multiple Deprivation 2004 (IMD), a small area-based indicator constructed from several different measures of deprivation.
22 IMD is measured at Lower Super Output Area (LSOA) level, where an LSOA contains between 400 and 1200 households. 23 Individual-level measures of socioeconomic status (e. g. education level or income) are not available. Detailed information on variables available, specific cleaning rules and coding used are available in the HES APC Data Dictionary provided by NHS Digital. 24 6 This has evolved over time as new techniques and technologies have been introduced. A history of versions in use is available from the NHS Digital coding standards website. 26 Each FCE may have up to 24 operations recorded (up from 4 before April 2002 and 12 in April 2002-March 2007), but procedure fields are left empty if patient management did not require an intervention covered by OPCS (e. g. where the primary treatment was a drug regimen or observation).
Birth and delivery information Each birth event in HES APC generates at least two FCEs: one delivery episode and one or more birth episodes. Each delivery and birth episode includes an additional 'maternity tail', with detailed fields including the baby's birthweight, gestational age, birth order (for multiple births), mode of delivery and maternal age (Table  1 ). The maternity tail is based on information entered via local maternity databases. Unlike the diagnostic and procedure fields, the maternity tail data fields use HES-specific categories rather than standardized classifications, and it is not a mandated return to NHS Digital. This leads to large variations in data completeness and quality. 27, 28 It is not possible to directly link a mother and a baby in HES APC; that is, the mother's HESID is not copied to the baby's birth record.
Data resource use Although no up-to-date bibliography of published research based on HES APC is curated by the data providers, a 2013 systematic review identified 148 articles using HES APC data published between 1989 and July 2011. 30 We carried out a subsequent search on PubMed on the 8 June 2016 using the search term 'Hospital Episode Statistics' for article abstracts published since July 2011. We identified 264 relevant publications where the primary analysis involved the use of HES APC data, and a further 130 papers where HES data had been linked to cohorts created in other datasets. The annual number of publications using HES APC data has increased from 2 in 1993  30  to 88 in 2015. Published studies using HES APC data have covered a diverse range of topics. They have explored the incidence of conditions across regions and over time.
Coding variation One of the key challenges in interpreting HES APC is the reliance on diagnostic and procedure codes for identifying study participants and outcomes. Despite centrally issued coding rules, clinical coders rely on the quality and detail of completed discharge summaries to enter data consistently. Consequently, diagnostic coding practices vary between hospitals, particularly for comorbidities. 65 ince the roll-out of PbR, financial incentives now exist for hospitals to improve coding depth in order to ensure accurate reimbursement. This has led to an increase in the number of diagnostic codes used and improvements in coding accuracy. 7, 66 The introduction of PbR therefore poses challenges for interpreting time-series studies using HES APC data, and care must be taken to not overinterpret results identifying increasing complexity of cases admitted.
Sensitivity to admission thresholds Since HES APC covers only admitted patients, it is sensitive to variation between hospitals or over time in admission thresholds. The introduction of the four-hour waiting target in A&E departments in 2004 has been suggested as a contributing factor for the increase in rates of emergency admissions in children during the 2000s. 67, 68 Changes in thresholds for emergency admissions can be examined using linked HES A&E data; 69 however, variation in admission thresholds for planned procedures cannot readily be determined using HES datasets.
Missing data Although age, sex and clinical characteristics are well completed in HES APC (see Table  2 ), data on ethnicity are not. Ethnicity has been a mandated return for all NHS contacts since 1991. Although ethnicity recording has improved over time, the proportion of patients with a known ethnicity recorded was still only 85% in 2011, up from 41% in 1997. 70 urther, there is a high proportion of missing data in the maternity tail fields (see Figure  2 ). Postcodes were not extracted from the SUS for birth episodes prior to 2013/14, which means earlier birth episodes cannot be mapped to geographical variables, including the Index of Multiple Deprivation (IMD). 71 As an example, completeness of the IMD decile variable for singleton birth episodes in 2012/13 was 7. 8%, compared with 81.
Quality of internal linkage The HESID linkage algorithm relies heavily on the accurate recording of NHS number across all hospital episodes to avoid missed matches (FCEs that have failed to link to a patient). Consequently, there is a substantial proportion of missed matches in HES APC. A recent estimate puts the HESID missed-match rate at 4%,  72  leading to an underestimation of readmission rates by 3. 8%. NHS numbers were not provided at birth until 2002, meaning that linkage within HES APC and to other HES and external datasets is not reliable for births before 2002/3. 73.
Scope limitations HES APC covers higher dependency (HDU) or intensive care unit (ICU) periods, but it does not contain 'flags' to identify such stays, nor detailed information on level of care or HDU/ICU interventions. A separate HES dataset covers adult critical care from 2008/09,  74  whereas data relating to neonatal or paediatric intensive care are collected through systems external to NHS Digital. Data on drugs prescribed through hospital pharmacies to inpatients are not available in HES APC. There is currently no national individual-level hospital prescribing database for England.
Opt-outs Patients who do not wish their records to leave NHS Digital can lodge a 'type 2 opt-out' with their primary care practice. 75 From 29 April 2016, any records (including in previous financial years) relating to persons who have opted out in any NHS Digital dataset (including HES APC) will therefore be removed before supply to secondary users. Overall, for the 2014/15 HES APC annual extract, 2. 3% of episodes will be removed, with substantial geographical variation in opt-out rates. 75.