dataesr/scientific-paragraphs-categorization huggingface dataset:
Data description. We combine data on institutional quality from the Quality of Governance Dataset (see  Teorell et al. (2018)  with IFS data on Current Account (CA) as a fraction of GDP. Our primary measures of institutional quality are the World Governance Indicators, and notably the Rule of Law index ("RL") and the WGI index ("WGI", i. e. the sample average of the six indexes), but we complement them with alternative indexes from the Heritage Foundation and the International Country Risk Guide, as explained in Section 2. 3 above. Our classification of exchange rate regimes comes from  Ilzetzki et al. (2017). We use the CA/GDP variable as the best proxy available to measure cross-country capital inflows, lacking a comprehensive and reliable cross-country dataset on long term interest rates for the sample period under study.
We drop countries with less than 300,000 people or exhibiting a current account deficit or surplus higher than 10% as fraction of GDP for the whole sample. Those are mostly small islands (e. g. Palau, Seychelles, Vanuatu) or countries with large current account surpluses associated to oil production (e. g. Qatar). We also drop countries that experienced a decline in GDP of more than 10% in one year as a result of a war or civil unrest (e. g. Libya, Rwanda or Iraq). After dropping these observations the dataset contains information about 95 countries.
These include: African Development Bank Country Policy and Institutional Assessments (GOV), Asian Development Bank Country Policy and Institutional Assessments (GOV), Bertelsmann Transformation (NGO), Freedom House Countries at the Crossroads (NGO), Global Insight Global Risk Service (CBIP), European Bank for Reconstruction and Development Transition Report (GOV), Economist Intelligence Unit Riskwire & Democracy Index (CBIP), Freedom House (NGO), Global Integrity Index (NGO), Heritage Foundation Index of Economic Freedom (NGO), Cingranelli Richards Human Rights Database and Political Terror Scale (GOV), IFAD Rural Sector Performance Assessments (GOV), JET Country Security Risk Ratings (CBIP), Institutional Profiles Database (GOV), IREEP African Electoral Index (NGO), International Research and Exchanges Board Media Sustainability Index (NGO), International Budget Project Open Budget Index (NGO), World Bank Country Policy and Institutional Assessments (GOV), Political Risk Services International Country Risk Guide (CBIP), Reporters Without Borders Press Freedom Index (NGO), US State Department Trafficking in People report (GOV), Yearbook Global Insight Business Conditions and.
Image alignment and shoreline mapping In addition to the drone surveys, we also used four "historic" panchromatic aerial photographs from 1952 and 1970, and satellite images from 2000 and 2011 (previously analysed by  Radosavljevic et al. , 2016). These four images had already been orthorectified in PCI OrthoEngine to minimise image distortion. We co-registered these four historic orthorectified images to the 6 July 2017 orthomosaic image in a geographic information system (ArcGIS, version 10. 5, ESRI), as we considered this orthomosaic to have the best spatial constraint and coverage of all of the available datasets. All 12 images were aligned to a common spatial framework: NAD83 UTM 7N (EPSG: 26907). Further details of all images and composite orthomosaics are summarised in Table  1. Alignment errors were estimated as the root-mean-square error (RMSE) of the control points.
While this approach to quantifying alignment error is standard practice in shoreline change analysis  (Irrgang et al. , 2018; Novikova et al. , 2018; Río and Gracia, 2013) , we note that the RMSE of control points is not a strong metric of this uncertainty, as transformation parameters (georeferencing) and the intrinsic and extrinsic camera parameters (structure-from-motion photogrammetry) are adjusted to minimise the RMSE. Consequently, for an independent assessment of image registration error, in future work it would be preferable to use the RMSE of independent check points, which were not used to constrain transformation or bundle adjustment parameters  (James et al. , 2017). Visual comparison of each dataset indicated excellent spatial agreement and suitability for further analysis  (Jones et al.
, 2018). Pixel error refers to the spatial resolution of the digital satellite and orthomosaic composite images, and for aerial photographs is a metric of image quality calculated based on the scale factor of each image multiplied by the typical resolution of a 9 × 9 in. aerial photogrammetric camera (after  Radosavljevic et al. , 2016). Table  1. Dataset and shoreline position parameters. The approximate times of drone surveys are in local time (UTC -08:00). Total shoreline uncertainty is the root-mean-square error of (i) image co-registration (root-mean-square, rms, error of ground control points, GCPs), (ii) image quality (pixel error), and (ii) shoreline mapping (digitising) error (from Eq. 1). Observation date Shorelines were digitised manually in ArcGIS for all 12 images, at a scale of 1 : 600 for the four, older, coarser spatial resolution panchromatic images and a scale of 1 : 80 for the eight finer spatial resolution RGB orthomosaics.
Shoreline and elevation analysis Total shoreline uncertainties were calculated as U = E G + E P + E D , (1) where U is total shoreline uncertainty, E G is georeferencing error, E P is pixel error, and E D is digitising error (Table  1 )  (Irrgang et al. , 2018; Radosavljevic et al. , 2016; Río and Gracia, 2013). Additive error propagation is appropriate because these error terms are not independent. Shoreline position statistics were calculated with the USGS Digital Shoreline Analysis System (DSAS version 4) extension for ArcGIS  (Thieler et al. , 2009) , using shore normal transects at 5 m intervals. Shoreline retreat rates in this study are given in end point rates for comparison between surveys, and both end point rate and linear regression rate for the entire time period of the study. The linear regression rate uncertainty is the standard error of the slope parameter at the 95 % confidence interval.
, 2019). This region is generally experiencing longer open-water seasons and increasing wave heights  (Barnhart et al. , 2014; Farquharson et al. , 2018; Stroeve et al. , 2014) , and there is increased heat influx to the ocean during the open-water season, due to increasing discharge from the Mackenzie River and atmospheric warming. Atmospheric warming has increased permafrost temperatures and deepened the active layer at sites just 1 km from the study reach  (Burn and Zhang, 2009; Myers-Smith et al. , 2019) , lowering the energy required to thaw the permafrost, although lengthening open-water seasons is likely to be the most significant factor  (Farquharson et al. , 2018). Attribution of the rapid change in shoreline position in 2017 to a single main driver is not possible with the available datasets.
Data availability. The georectified aerial images underpinning this analysis are available for download from PANGAEA (https://doi. pangaea. de/10. 1594/PANGAEA. 901852, Cunliffe et al. , 2019). The digital elevation models are available from the corresponding author upon request. Meteorological observations are available from Environment Canada. Video supplement. A supplementary time-lapse video of the coastal erosion reported here is available at https://doi. org/10. 5446/40250 (Cunliffe et al. , 2017). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-13-1513-2019-supplement. Author contributions. AMC and GT contributed equally to this work. Conceptualisation, AMC, GT, JTK and IHM-S; Data curation, AMC; Formal analysis, AMC and GT; Funding acquisition, IHM-S, TS, and HL; Investigation, AMC, GT, BR, WFP and JTK; Methodology, AMC; Project administration, AMC; Supervision, TS, HL and IHM-S; Visualisation, AMC, GT and WFP; Writing -original draft, AMC; Writing -review & editing, AMC, GT, BR, WFP, TS, HL, JTK and IHM-S.
Acknowledgements. We wish to thank the  Qikiqtaruk Territorial Park  staff including  Richard Gordon ,  Edward McLeod ,  Samuel McLeod ,  Ricky Joe ,  Paden Lennie , and  Shane Goesen , as well as the  Yukon government and Yukon  parks for their permission and support of this research (permit number  Inu0216 ). We also thank the  Inuvialuit people  for their permission to work on their traditional lands. Drone flight operations were authorised by a  Special Flight Operations Certificate  granted by  Transport Canada. We thank  Tobias Bolch ,  James Duffy , and the anonymous reviewers for insightful feedback that helped us to refine earlier versions of this paper. Financial support. This research has been supported by the  Natural Environment Research Council  (grant no. NE/M016323/1 ), the  National Geographic Society  (grant no.
Synthesis of Events Recognition and Dating From This Work The data from this work account for the earthquake history of the VBFS back to ~22 kyr. As previously discussed, the Hope trench shows traces of the oldest ruptures (older than 5 kyr), while the two "antithetic" trenches, Curly and Matthew, describe the youngest history (the last ~4 kyr) providing fully consistent results. These circumstances allowed to compose a long paleoseismic data set for this fault system based on multiple direct evidence of surface faulting in the same trench and in different trenches. Figure  8a  synthesizes the paleoseismological results discussed above and reports a tentative correlation among the paleo-events recognized in the different trenches. This correlation is based on the preferred ages of individual events (see discussion on the single trench in sections 3.
Conclusion The analysis and integration of paleoseismic data sets acquired so far along the VBFS provide an original recon struction of the Late Pleistocene-Holocene seismic history of the fault system rupturing on 30 October 2016. The data sets include a total of nine trenches, three from this work and six from previous ones  (Galadini & Galli, 2003; Galli et al. , 2019). Five trenches were opened across the antithetic splays in the central portion of the 2016 surface rupture, three trenches were located within the Castelluccio plain along the 2016 secondary synthetic splay, and one more was at the southernmost tip of the 2016 rupture trace also along a synthetic splay. We performed a detailed paleoseismological analysis of the three trenches presented in this work by determining the amount of throw and timing for each paleo-event.
The development of this research benefited of the Cooperation Agreement between INGV and IRSN. Terrestrial laser scanning (TLS) point cloud of the south wall of the Matthew trench is provided by L. Gregory, L. Wedmore, and K. Mc Caffrey (data available at https://doi. org/10. 5069/ G9G15Z03). We thank Kris Vanneste and an anonymous reviewer for their comments and suggestions, which significantly improved this manuscript. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper, in the supporting information, in the cited references, and at https://doi. org/ 10. 5281/zenodo. 3355395. The main findings of this study can be summarized in the following points: 1. The 2016 surface rupture coincides with mainly normal, high-angle-dipping fault planes at depth and is directly related to the coseismic movement along these planes.
Acknowledgments We thank the  Parco Nazionale dei Monti Sibillini , the  Comune di Arquata, and Comune di Castel Sant'Angelo sul Nera  for their collaboration and for permitting the opening of the trenches. Many thanks to  Maria Elvira Speranza  and her family, to  Giandomenico Di Mattia , and to  Sabatino Riccioni  for their kindness and for having allowed the trenching of their private lands. To.
Abstract. Large wildfires across parts of France can cause devastating damage which puts lives, infrastructure, and the natural ecosystem at risk. In the climate change context, it is essential to better understand how these large wildfires relate to weather and climate and how they might change in a warmer world. Such projections rely on the development of a robust modeling framework linking large wildfires to present-day atmospheric variability. Drawing from a MODIS product and a gridded meteorological dataset, we derived a suite of biophysical and fire danger indices and developed generalized linear models simulating the probability of large wildfires ( > 100 ha) at 8 km spatial and daily temporal resolutions across the entire country over the last two decades. The models were able to reproduce large-wildfire activity across a range of spatial and temporal scales.
, 2015a, b; Ruffault et al. , 2018). However, most of these previous efforts have exploited regional datasets of burned areas across parts of southeastern France commencing in early 1970s, and little attention has been devoted to understanding processes in other regions except in the Alps  (Dupire et al. , 2017). Over the long-term, a substantial reduction in wildfire activity was observed in the 1990s across the French Mediterranean due to suppression and prevention strategies  (Ruffault and Mouillot, 2015; Curt and Frejaville, 2017) , decoupling wildfire trends from climate expectations  (Fréjaville and Curt, 2017). However, the 2003 heatwave induced wildfire-prone meteorological conditions across the region, impeding suppression efforts and putting 2003 as one of the most extreme years in terms of burned area over the last six decades  (Ganteaume and Barbero, 2018).
Such a model is still lacking due to observational inhomogeneities in wildfire detection across the country, hampering the compilation of a homogeneous database. Drawing from a global remote-sensing database of burned area, we sought here to develop a nationwide statistical model including wildfireprone regions overlooked in previous studies. The model is expected to advance our understanding of processes and drivers of large wildfires and to provide guidance on how weather and climate variability may increase the occurrence of large wildfires in France under a warmer climate. 2 Data and methods.
The 22 785 MODIS pixels extracted from 2001 to 2016 across France were found to form 894 distinct wildfire events. We then defined large wildfires as exceeding 100 ha (N = 156 large wildfires) following  Ganteaume and Barbero (2018) , a threshold corresponding here to the 83th percentile of the distribution of wildfire area. The average large-wildfire area was found to be 398 ha, with the largest wildfire reaching 7675 ha. Finally, we regridded this information onto an 8 km resolution grid to facilitate the comparison with meteorological data (see Sect. 2. 2).
Figure7. Composites of local simulated probabilities (expressed as percent with respect to the mean seasonal cycle, i. e. , 100 % indicates that probabilities are two times higher than normal) relative to the large-wildfire (> 100 ha) days. The 95 % confidence intervals of the composite means are computed using 1000 bootstrapped datasets. The envelope of confidence indicates the 2. 5 and 97. 5 percentiles of the composite means obtained from the bootstrapped datasets.
Participants will be free to withdraw from the medication at any time. Together with the study medication, participants will be provided with a contact card so that any treating clinician can be unblinded to treatment allocation in case of a medical emergency ('emergency unblinding') or early unblinding to enable treatment decisions. If unblinding is required, a formal request by a clinician will be made to the trial pharmacy (through the 24-h contact number provided on the contact card) that has a list of the participants' treatment allocations. The treating physician will manage the medical emergency as appropriate upon receipt of the treatment allocation. The PI or delegate will record any breaking of the code and reasons for doing so on the Case Report Form (CRF)/data collection tool and in the site file.
Follow-up assessment schedule At 6 weeks post randomisation, the participants will be asked to complete a postal questionnaire. At 12, 26, 39 and 52 weeks after randomisation, the participants will be asked to attend an appointment with the researcher. After 52 weeks, primary healthcare use data (prescribed medication, primary care visits) for the time period of the trial and for 6 months preceding the trial will be extracted from GP electronic health records. The follow-up schedule is summarised in a flowchart (Fig. 1 ). We will examine the test-retest reliability of the PHQ-9, GAD-7, retrospective CIS-R, adverse effects, withdrawal symptoms (we have included a scale consisting of 15 items) and adherence questionnaires. The participants will be asked to repeat those questionnaires at one of the follow-up appointments.
Withdrawal of trial participants Participants can withdraw from the trial at any time for any reason, without their medical care being affected. Where possible, data already collected will continue to be used in the trial and participants who stop taking the trial medication will be asked if they are still willing to meet with the researcher and provide follow-up data. Once participants have stopped their trial medication, they may not resume trial treatment. If participants withdraw, the reason for and type of withdrawal will be documented. If the PI is concerned about the clinical condition of a patient such that they should not be on a placebo, we will withdraw that patient from the trial and will advise them to see their GP to receive appropriate treatment outside of the trial. The decision to withdraw will be based on factors such as depressive symptoms and suicidality and any other factor which the GP or PI thinks makes withdrawal in the best interests of the patient.
Statistical analysis We will follow CONSORT guidelines in analysing the data and reporting the trial findings (http://www. consort-statement. org/). We will also prepare a CONSORT flow diagram. This will include the number of patients randomised to each arm of the trial, and the numbers who have follow-up data available. Initial analyses will look at summary statistics for all variables, both overall and by randomised group. Summary statistics for continuous variables will be the mean, median, SD, lower quartile and upper quartile, and will be reported appropriately according to distribution. The primary outcome will be the time in weeks to the beginning of the first episode of depression after randomisation. The primary outcome will be assessed using a modified and shortened standardised psychiatric assessment (CIS-R) that will ask the participants retrospectively over the previous 3 months and will be used at follow-up points of 12, 26, 39 and 52 weeks.
Client Service Receipt Inventory (modified) for healthcare and other resource use. Secondary outcomes will be analysed for each followup point using mixed-effects linear regression in which the baseline value and follow-up value will both be outcomes. The interaction between time and group will be estimated. Variables indicating time and the randomised group will be included in the models as fixed effects. We will conduct a supportive analysis using all observations for a participant in a further mixed-effects regression model for each outcome. A detailed statistical and health economic analysis plan will be written and signed off by the IDMC for the trial before the database is locked. The plan will be logged in the UCL repository and/or on the ANTLER website.
A secondary analysis from the societal cost perspective will also be conducted. QALYs will be calculated as the area under the curve using utility scores calculated from the EQ-5D-5L  [22]  collected at each time point, adjusting for baseline values. Incremental costs and QALYs will be calculated using ordinary least squares (OLS) regression and adjusting for baseline depressive symptom score, baseline costs for the cost analysis and utility scores for the QALYs. We will conduct one and two-way sensitivity analyses for any assumptions made and sub-group analyses as identified. Missing data will be handled in the same way as for the statistical analysis, with the primary analysis being an intention-to-treat analysis and supportive analyses taking into account assumptions about missingness. Bootstrapping will be used to construct confidence intervals and a cost-effectiveness acceptability curve of the probability that antidepressant maintenance is cost-effective for a range of values of willingness to pay for a QALY gained.
Data handling and quality assurance The trial Sponsor is University College London and takes primary responsibility for ensuring that the design of the study meets appropriate standards and that arrangements are in place to ensure appropriate conduct and reporting. A monitoring plan has been agreed with the Sponsor. Local PIs will be responsible for the data quality at their centre. The trial will be run in accordance with Good Clinical Practice (GCP) and current regulatory guidance. All data will be handled according to the General Data Protection Regulation (GDPR) 2018 as well as UCL Information Security Policy and Trust Information Governance Policy. The investigators have full access to all of the data and are under no restrictions in their use of the data within the constraints of the relevant legal framework.
Discussion To our knowledge, the ANTLER trial will be the first large trial with a long follow-up of 12 months to investigate the effectiveness of antidepressants as maintenance treatment for patients who are already on long-term maintenance. Prof. Dee Mangin and the team carried out a similar trial in New Zealand in 2008, looking at maintenance vs gradual withdrawal of antidepressants in prevention of depression recurrence in primary care in patients with unipolar depressive disorder. The trial's medication was fluoxetine and included patients (N = 263) who had been on antidepressants for 1 year or longer. It was registered on the Australian and New Zealand Clinical Trail Registry: ACTRN12608000613303. The sample size was reduced after the Christchurch earthquakes in 2010-2011 when a decision to stop recruitment at the primary site (Christchurch) was made in consultation with the Data Safety Monitoring Board-all patients enrolled continued in the trial in Christchurch and other sites.
Trial status The trial began recruiting participants in March 2017 and recruitment will be ongoing until the end of February 2019. At the time of writing (September 2018), 370 participants have been randomised into the study. It is expected that data collection will be completed by February 2020. This article describes protocol version 6 dated 3 August 2017; when all initial approvals (REC, HRA and MHRA) were received, the protocol was version 2 dated 22 February 2016. Following a substantial amendment, the protocol was amended with changes in the pharmacy arrangements, changes in Sponsor representative and a 6-week follow-up added, and it became version 5 dated 18 November 2016. As a result, when recruitment started the trial was following protocol version 5; since then it has been amended once to version 6 dated 3 August 2017: PHQ-9 assessment was removed from the eligibility criteria and instead is being used more flexibly at the screening stage to identify potentially eligible patients.
Acknowledgements The authors would like to thank all members of the  Trial Steering Committee , the Data Management Committee and the  Public and Patient Involvement representatives. They would also like to thank colleagues who have contributed to the study through recruitment, administrative help and other advice. The authors would like to acknowledge the following colleagues:  Carmen Sinclair ,  Nomsa Chari ,  Anna Hunt ,  Mahsa Rezaei ,  Vivien Jones ,  Alison Burns ,  Catherine Derrick ,  Jodi Pervin  and  Sally Brabyn. The authors have been supported by the following Clinical Research Networks: North Thames CRN,  CRN North West London, CRN South London, TVSM CRN, Wessex CRN, West of England CRN, Yorkshire and Humber CRN. In particular, they thank the following  CRN  staff:  Debbie Kelly ,  Claire Winch ,  Andrei Gabzdul ,  Zara Perm ,  Khatiba Raja  and  Lynsey Wilson.