dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, the following lifestyle and symptom burden questionnaires are collected: i. Patient-reported health-related quality of life (HRQOL): • Chronic Liver Disease Questionnaire for NAFLD NASH (CLDQ NAFLD-NASH)  [20, 21] , • EQ-5D-5L Health [22], • NASH-CHECK [23]. ii. Dietary questionnaires: • Audit C [24], • Mediterranean Diet Score [25]. iii. Lifestyle (exercise/sedentary behaviour assessment): • International Physical Activity Questionnaire (IPAQ) [26]. Thereafter, 'follow-up event' data are collected annually at scheduled out-patient clinic attendances. In addition, data collection may be triggered if the patient undergoes a subsequent clinically indicated liver biopsy or if a significant 'clinical event' occurs. Target events are listed in Table  4  and focus particularly on death, major adverse cardiovascular events (MACE), hepatic events (progression to cirrhosis, hepatic decompensation and/or HCC) and extra-hepatic malignancy.
Central histological interpretation & digitisation of liver biopsies Histopathological assessment of liver biopsy remains the widely accepted, albeit imperfect, reference standard for assessing severity of liver disease  [28]. As well as issues with sampling error  [29] , there remain concerns regarding potential interobserver variability in assessment. To address these issues, the European NAFLD Registry has adopted a number of measures to ensure data quality: i. Histology Processing at a Central Laboratory. To minimise variation in slide staining between laboratories, biopsies are fixed in 10% buffered formalin, embedded in paraffin and cut into 3-4 μm sections at recruitment sites. Four unstained slides are then shipped to the Central Biobank for staining, digitisation and storage. At IBBL, slides are stained with haematoxylin and eosin (H + E) and Masson's trichome for collagen according to established standard operating procedures.
In addition to histological scoring of these standard features, pathologists also assess the intensity of portal inflammation, the presence of Mallory-Denk bodies and determine a broad diagnostic category (i. e. normal, NAFL, definite NASH, fibrosis/cirrhosis without steatosis consistent/not consistent with NAFLD). All data are captured using a standardised reporting proforma. iii. The Registry Histopathology Group (RHG) comprises ten expert hepatopathologists (Table  6 ) who participate in a face-to-face harmonisation meetings to align on histological criteria and have demonstrated close interobserver concordance  [31]. Pairs of hepatopathologists from this group independently double-score liver biopsies with reference to a digital atlas of NAFLD histology that has been prepared by the group leaders (Supplementary Material 3 and via the European Society of Pathology website: https:// tinyurl.
com/LITMUS-Histology). If the two scores within a pair are found to be discrepant (by ≥2 points for steatosis or inflammation, and ≥ 1 for ballooning or fibrosis) the biopsy is referred to a consensus adjudication panel. To further ensure data quality, the adjudication panel reviews a random sample of biopsies for quality control. An administrator assigns the liver biopsies to the pathologist pairs, completes the final report for each case based on the agreed scores and uploads the histological data in the NAFLD Registry. Although semiquantitative scoring systems are used in many clinical trials  [34] [35] [36] [37] [38] , it is recognised that scoring systems provide a nonlinear, semiquantitative or categorical assessment of disease that may limit precision and granularity of data, particularly in the context of subtle changes over time or at the boundary between two categories, where misclassification may occur.
Due to the non-interventional nature of the Registry, risk-assessment has determined that the likelihood of direct harm to participants is minimal and so monitoring primarily focusses on ensuring that conduct is in compliance with the protocol and that study data are complete, accurate and verifiable. In line with the organisational structure of the Registry, monitoring activities are structured as three tiers: central oversight of data integrity, country-level source-data verification checks with external monitors visiting sites and site-level internal data checks conducted by the local research teams. Central monitoring is conducted by the central Registry Management Team (Fig. 2 ). Central data managers perform consistency checks of data entered into the Registry at least every 6 months.
These include review of enrolment rates against site and study targets; data validation for completeness and plausibility; and oversight of monitoring activities. Any inconsistencies, errors or omissions detected are flagged to the recruiting site to address. The Registry Management Team and Central Biobank also oversees biological sample management and cross-referencing of the biobank catalogue with the Registry database to ensure samples are received and logged appropriately. At the country level, National Clinical Leads serve as the national chief investigator in their territory and are responsible for compliance and on-site monitoring. The National Clinical Leads carry out Site Initiation visits providing information to sites on study background, protocol, and data management procedures.
Inconsistencies, errors or omissions flagged by the Registry Management Team are highlighted to the National Clinical Lead who ensures the recruiting site checks and amends errors as necessary. A key role of the National Clinical Lead is to appoint site monitors to ensure compliance with GCP, data consistency across sites in a territory and appropriateness of training. Site monitoring includes several important activities, but crucially source data verification (SDV). A first monitoring visit is conducted at each site within three months of the first subject enrolled into the European NAFLD Registry, once per year of the study and until three months after the recruitment of the last subject. Briefly, the first two participants in the study are subject to 100% SDV through the entirety of the project; if major errors/critical findings are found in these patients, the subsequent two patients recruited will follow the same monitoring procedure through the study.
Additional activities include review of signed consent forms for all participants and authority approvals are in place. The final layer of oversight comes at a site-level. Site level monitoring is carried out by an independent member of the local research team, not the individual who entered the data, and includes confirmation of consent, data collection according to study protocol and ensuring complete datasets. Protocol deviations are documented in each participant record using the protocol deviation form and reported to the National Clinical Lead. Internal monitoring tasks include checks that: all participants are consented on the most up-to-date, approved informed consent form in effect at their last visit and that all forms have been completed correctly and the consent date entered into the Registry; data being collected is consistent with what is required by the study protocol and an accurate transcription of the source documents, with special attention being paid to accuracy of reported clinical events and associated investigations.
Data management The European NAFLD Registry has a comprehensive data management plan (DMP) to allow secure management of generated data sets and plans for publications, data access and preservation policies. The DMP will be regularly updated to reflect other data sets that may be developed within the European NAFLD Registry, which aims to provide accurate and high-quality data to the research community, so that the Registry will contribute to future advancements in the field of advanced diagnostics. Dissemination of research data is planned in the form of publications in scientific journals and/or presented in scientific or other meetings for the benefit of the wider medical community. Participants will not be identifiable from such published data nor in any datasets that may be placed in the public domain.
Discussion The European NAFLD Registry represents a major international effort involving leading secondary/tertiary care centres in Europe, to prospectively recruit patients into an observational study exploring the pathophysiology of progressive liver disease, disease outcomes and associated predictive factors in patients with NAFLD. Importantly, data generated from this study will address key unmet medical needs: the European NAFLD Registry underpins the LITMUS consortium goal of establishing a defined set of biomarkers that singly or in combination, enable detection and monitoring of disease progression to/regression The European NAFLD Registry seeks to cognise the variability in NAFLD disease course by leveraging large sets of integrated '-omic' data to better understand the pathophysiological processes that underpin NAFLD disease severity and progression.
It also collates existing prospectively recruited cohorts to permit better granularity of disease outcomes. Together, the European NAFLD Registry data set will allow better correlation of biomarkers with disease diagnosis, prognosis and monitoring, permitting the advent of new pharmacotherapy, which is desperately needed. The strengths of the Registry are its large sample size, international recruitment, standardised procedures, and fastidious data monitoring plans. The Registry aims not only to collect data of the highest quality, but also to have the ability to prove that the data is of the utmost veracity. Built within the Registry is a geographical hierarchy that facilitates implementation of a clinical monitoring plan that ultimately verifies the high quality of the data collected. A unique strength of the Registry is its ability to provide integrated datasets of both cross sectional and longitudinal analysis in the same patient to ascertain causal relationships and mechanisms in disease progression.
This capability will facilitate unsupervised data exploration and analysis to develop novel research hypothesis by artificial intelligence/machine learning in addition to end-user directed hypothesis testing. With approved pharmacotherapies for NASH likely to become available in the near future, the European NAFLD Registry platform also has the capability to support authority requested post-marketing surveillance studies (post-authorisation safety and/or efficacy studies); facilitating the systematic monitoring of medications while they are used in clinical practice. In summary, the European NAFLD Registry is a comprehensive study using standardised data collection practises, clinical monitoring plans and observational protocols to garner the highest quality, large datasets that can be leveraged to better understand and ultimately develop biomarkers across the spectrum of NAFLD severity that can lead to efficient drug discovery and regulatory approval-a key unmet need in NAFLD management.
The European NAFLD Registry is supported by the LITMUS (Liver Investigation: Testing Biomarker Utility in Steatohepatitis) consortium funded by the European Union Innovative Medicines Initiative 2 (IMI2) Joint Undertaking under grant agreement 777377, which receives support from the Horizon 2020 Framework Program of European Union and EFPIA. It has also received support from the EPoS (Elucidating Pathways of Steatohepatitis) consortium funded by the Horizon 2020 Framework Program of the European Union under Grant Agreement 634413, the FLIP consortium funded by the Framework Program 7 of the European Union under grant agreement 241762, and an EASL Registry Grant from the European Association for the Study of the Liver. Additional infrastructure support is provided by Newcastle University, the Newcastle Health Innovation Partners Academic Health Science Centre, the NIHR Newcastle Biomedical Research Centre and NIHR Nottingham Biomedical Research Centre, United Kingdom.
Satellite remote sensing Satellite remote sensing is a very useful tool for Earth observation in the Arctic, given the vast size of the area and constraints 515 in accessibility. The sensors that are typically applied are ranging from radar sensors of different frequency, typically X-and C-band (TerraSAR-X, Tandem-X, Sentinel-1) to optical sensors (Landsat, MODIS, Sentinel-2 and -3) and altimeters (CryoSat-2, ICESat-2, Sentinel-3). In future a hyperspectral mission (EnMAP) will survey the Arctic. In the framework of iCUPE, we utilize satellite remote sensing data on the topics ranging from Arctic vegetation, snow-cover monitoring, and supraglacial melt of glaciers. Beyond the development of products and assessment of existing approaches, also new techniques like 520 polarimetry for glaciers were assessed, which will serve for recommendations for future satellite missions.
Arctic vegetation 525 As part of iCUPE, we are working beyond the recent retrievals of satellite remote sensing data to include advanced current and upcoming optical remote sensing missions with improved spatial, temporal and spectral resolutions and their potential for characterization of Arctic regions. In particular, hyperspectral remote sensing (or imaging spectroscopy) has been shown to provide superior derivation of key biophysical surface variables in snow-free permafrost areas during the summer months based on field and airborne remote sensing data  (Buchhorn et al. , 2013; Bratsch et al. , 2016; Liu et al. , 2017; Beamish et al. , 530 2019a). The datasets compiled as part of the iCUPE project (see Sect 3. 8) consist of four years of field data including canopylevel spectral reflectance data, as well as aerial hyperspectral images (Alaska and Canada) and a dense Sentinel-2 time series (Siberia).
Additional ground-based data includes leaf-level photosynthetic pigment data from multiple phenological phases, as well as aboveground biomass and detailed species composition data. The study sites include Toolik Lake Alaska, Qikiqtaruk-Herschel Island (QHI) Canada, and Lena Delta, Siberia. All three sites represent key low Arctic research stations with the 535 Toolik and QHI sites having pre-established long-term vegetation monitoring plots. This dataset is unique and provides the potential for further work given the freely available nature of much of the ancillary data and the intensive international research being conducted at all locations. The main goal of this work was to provide an initial characterization and exploration of the application of hyperspectral remote sensing data in Arctic terrestrial ecosystems where existing research is limited.
, 2019). To accomplish the ground 545 spectral characterization, we collected canopy-based spectral reflectance in five dominant low Arctic vegetation communities at three major phenological phases of leaf-out, maximum canopy, and senescence (Fig. 16 ) and simulated upcoming EnMAP and Sentinel-2 spectral reflectance. We then examined within and between community variability to determine the most discriminative wavelengths and phenological phase for the ground-based, EnMAP, and Sentinel-2 reflectance. The results suggest that for imaging spectroscopy (ground-based and simulated EnMAP) the senescent phase imagery is 550 superior to leaf-out and maximum canopy for differentiating vegetation types, while for Sentinel-2 maximum canopy was superior. The difference between the narrow and broadband data is likely due to the extreme color differences observed during senescence that are well captured by imaging spectroscopy but not by broadband data.
These results provide important information for better interpreting current broadband and future narrowband spectral reflectance data for more accurate estimation of vegetation composition, vigor and biomass  (Beamish et al. , 2017). 555 As a second step, we explored the relationship between spectral information from imaging spectroscopy and digital cameras data to biochemical variables at Toolik Lake, Alaska to assess the utility of low-tech cameras as a ground-based validation tool. To do this we looked at the relationships between narrowband and RGB indices and in turn the relationship of these indices to changes in photosynthetic pigments of chlorophyll and carotenoids. We were again interested in how these relationships vary by phenological phase and vegetation type. The results showed that vegetation color contributes strongly to 560 changes in both narrowband and RGB indices and that these changes are related to changes in photosynthetic pigment concentration (Fig.
org/10. 5194/acp-2019-1217 Preprint. Discussion started: 3 March 2020 c Author(s) 2020. CC BY 4. 0 License. satellite image  (Solberg et al. , 2006) , which is the core of the GlobSnow Snow Extent (SE) data chain  (Metsämäki et al. , 2015). The occurrence of different interferences (cloud cover or snow metamorphism for example) requires the availability of additional proxies, like webcam networks  (Salzano et al. , 2019) , that can improve the calibration and validation processes of 630 remotely sensed products. Several satellite products are available for the remote sensing of the cryosphere and for this study we considered products obtained by optical sensors, characterized by different spatial resolutions: high (< 100 m); intermediate (100 m -1 km); and low (> 1 km). The integration between those products and ground-based imagery will be tested, in order to improve the dataset concerning the snow cover over a decade.
From this perspective, we carried out a first field campaign aimed to survey the available webcams in the study site and to deploy a new webcam on the CNR Climate Change Tower. This site has proved to be the most suitable for the collection of 645 ancillary data necessary for a correct interpretation of both satellite and terrestrial images. This CNR infrastructure is equipped with different meteorological sensors as well as instruments focused on detecting the radiative properties of the atmosphere and of the snow-covered surface. The core of this activity was the development of a procedure suitable to collect remotely-sensed data and match these imagery to FSC estimations obtained using terrestrial photography. The resampling of snow -no snow pixels, provided by webcams 650 (Fig. 22 a  and c ), using grids associated with satellite imagery, provided a continuous dataset of FSC to NDSI relations (Fig.
Anthropogenic emissions from oil/gas extraction over northern Russia: a combined approach using aircraft, satellite data and modelling 735 Aircraft data collected for the first time over northern Russian oil and gas producing regions was used to examine the validity of available emission inventories. The data was collected in October 2014 and July 2017 as part of the French-Russian YAK-AEROSIB program  (Paris et al. , 2010; Antokhina et al. , 2018). A combination of different approaches was applied involving joint analysis of aircraft and satellite data together with source-receptor modeling using FLEXPART/FLEXPART-WRF and regional WRF-Chem modeling  (Marelle et al. , 2017 (Marelle et al. , , 2018) )  to investigate discrepancies in both black carbon (BC) and methane 740 (CH4) anthropogenic emissions over Russia, from where only very little data is available to date.
, 2019, in prep. ). The data is being further analysed using daily VIIRS (Visible Infrared Imaging Radiometer Suite) satellite night-light data (provided courtesy of Earth Observation Group, 750 https://doi. org/10. 5194/acp-2019-1217 Preprint. Discussion started: 3 March 2020 c Author(s) 2020. CC BY 4. 0 License. Payne Institute for Public Policy, https://eogdata. mines. edu/download_dnb_composites. html) to examine the sensitivity of modelled BC to daily variability in flaring emissions. Preliminary results suggest that the simulated BC is highly sensitive to significant observed variability. Methane emissions from oil and gas activities are similarly poorly known, and their spatial and temporal distribution is related to that of BC (e. g. Gvakharia et al. , 2017). Inventories are affected by 1) highly variable emission factors, 2) highly variable 755 scheduled or unscheduled operations leading to release of methane and 3) the uncertain spatial distribution of regional totals (Fig.
, 2014) ), forest and desert maps (Advanced Very High Resolution Radiometer (AVHRR) database  (Sertel et al. , 2010) ), and gas flaring emission map (ECLIPSEv54 database (Evaluating the CLimate and air quality ImPacts of Short-livEd pollutants) described in  Klimont et al. , 2017). 780 An example of lidar aerosol vertical profile is shown in Fig. 27a  for a mixture of gas flaring emissions from the Ob Valley below 3 km (Fig. 27e ) and a dust layer transported from Kazakhstan above 3 km (Fig. 27c ) during a time period with no forest fires in Siberia. Aerosol scattering ratios at 808 nm increased by a factor of 3 due to this event while the corresponding AOD reached 0. 3 in good agreement with the Tomsk AERONET sun photometer observations and the MODIS hot spot near TOMSK (Fig. 27b ). Elevated CO columns seen by IASI above the Ob valley and Tomsk also support gas flaring emissions 785 during this period.
Deriving proxy variables from in-situ and satellite data In iCUPE, we developed proxies for atmospheric variables from the in-situ data gathered in Finnish measurement sites. In 830 practice, this means that we derive formulas that describe the proxy variables as functions of more commonly measured parameters. Previously, the proxies have been derived mainly for concentrations of gas phase molecules, such as sulfuric acid  (Petäjä et al. , 2009; Mikkonen et al. , 2011)  and monoterpenes  (Kontkanen et al. , 2016). In iCUPE, we are developing proxies for condensation sink (CS), boundary layer height (BLH) and ecosystem level gross primary production (GPP). The aim is to produce proxies that rely on variables that can also be retrieved from the satellite data in order to produce proxies with good 835 spatial coverage (e.
iCUPE data flow One of the main outcomes of iCUPE are new data products developed based on the comprehensive use of in situ and satellite remote sensing. The new iCUPE data products, which will fill e. g. observational gap of the key variables of Persisting Organic Pollutants (POPs), Chemicals of Emerging Concern (CECs), Short-Lived Climate Forcers (SLCFs), and atmospheric trace 865 gases in the polar context, are being co-designed together with different end users. The list of data products is presented in Table  1. End-users for the iCUPE data sets include European Environment Agency, geoengineering bodies, decision makers, intergovernmental organizations, local government and environmental administration (ministries), citizens and National and European weather services. The data can be used to improve models, to advance our scientific understanding of the Arctic regions, support policy making and improve weather predictions and mechanisms in risk control of natural hazards, in sea 870 traffic control, in tourism and in chemical weather prediction.
iCUPE will provide in total more than 20 datasets as products usable for researchers, decision-and policy makers, stakeholders and end-users communities. These will be a valuable and important contribution from the iCUPE project activities in piloting open access to Arctic data. All these datasets will be publicly available for different applications and services. Focusing on the https://doi. org/10. 5194/acp-2019-1217 Preprint. Discussion started: 3 March 2020 c Author(s) 2020. CC BY 4. 0 License. Arctic region territories, the planned datasets will include e. g. novel data on anthropogenic contaminants in snow and ice cores 875 and organic contaminants in the air-snow-water and concentrations of different chemical species and aerosols as well as their characteristics including vertical profiles.