dataesr/scientific-paragraphs-categorization huggingface dataset:
A. 2D DISK REPRESENTATION OF LEFT AND RIGHT VENTRICLES The 2D disk representation (2D BEP) is obtained following the technique we presented in Soto-Iglesias et al. [22] -  [24]  on the LV endocardium. Here we analyze not only data from the LV endocardium (EAM measurements from the RV endocardium are not available) but also from the RV and LV epicardium. Firstly, we compute a vanishing conformal Laplacian that smoothly maps every vertex of the 3D EAM mesh to a 2D disk  [22] ,  [23]. In order to get a unique solution, we need to impose some boundary conditions and identify two landmarks. Initially, the boundary points of the 3D mesh must be mapped into the circumference of the 2D disk, preserving the 3D relative distance between them. We also force the mapping to follow the 17 AHA segments definition  [21] , thus a point in the middle of the septum (Septum point in Figure  2a ) is placed at the (-1, 0) XY coordinates of the disk.
After the mapping, the apex is displaced to the center of the disk (0, 0) by applying thin plate splines (TPS) transformation, which results in a quasi-conformal mapping (QCM), as illustrated in Figure  2a. The representation of the epicardium was established in a similar way. However, the epicardium is not mapped onto a 2D disk since both ventricles share it, which may be misleading when compared to endocardial data. We then choose to map LV and RV data to separate half disks, as in  [26]. Firstly, the septum line (SL) is defined on the 3D EAM mesh. To do so, three landmarks are manually selected (Figure  2b ): one at the apex of the epicardium, and two in the boundary of the mesh, specifically on the posterior (posterior landmark PL) and anterior (anterior landmark AL) edges. In the 3D mesh the SL is then defined as the geodesic line that connects the PL and AL landmarks and goes through the septum.
In the 2D space the LV is defined as (the right) half of a disk. In order to be consistent with the endocardial mapping, the AL and PL landmarks are located at the (0, 1) and (0, -1) XY coordinates. The 2D septum line is then defined as the line that joins these two landmarks, while the LV lateral wall boundary is forced to the contour of the half (right) disk. A similar approach is applied for mapping the RV epicardial EAM onto a half-moon shape. An additional difficulty is that each EAM has a different number of acquired points. Thus, the data mapped onto the 2D BEP must be reparameterized to compare different EAMs. A common set of nodes is generated as the intersection points between 50 equidistant concentric circles of increasing radius, with 300 rays emanating from the center of the disk, resulting in 15,000 nodes with a resolution of 0.
B. HISTOGRAMS OF ACTIVATION A complementary type of representation for EAM data based on histograms computed from local activation times was developed. These histograms are built for endocardial and epicardial data and characterize the distribution of the data depending on the time of activation of every point. The total activation time (TAT), which is defined as the difference  between the earliest and latest activation points, is divided in a certain number of intervals, so-called isochrones in clinical terms or bins in statistics, which represent the percentage of the tissue activated within a certain TAT interval. We have divided the TAT into 20 bins, corresponding to intervals of 5%, since it provided enough resolution for the studied phenomena. Examples of these histograms are given in all remaining figures of the paper.
The total endocardial and epicardial (RV and LV) tissue activation are evaluated independently. Therefore, the endocardial (blue bars) activation is normalized between 0 and 100% and the epicardial is divided into RV (green bars) and LV (red bars) activation. The main advantage of this data representation is that it shows very intuitively (electrical) dyssynchrony and heterogeneities between different parts of the heart (e. g. endo-epi, RV-LV).
2) THERAPY-RELATED INDICES a: LEAD LOCATION We use the 2D BEP representation of EAM data to automatically identify the CRT leads in the ventricles. Although the most common CRT configuration is based on the implantation of one lead at the apical region of the RV endocardium and another one at the LV lateral wall, the optimal LV lead position is highly controversial  [31] -  [35]. Once the CRT device is operating, leads control the electrical activation at every heartbeat, to (as in our experiments) impose a zero-delay between the shocks from both leads and then force a synchronous activation between the septal and lateral walls of the LV. Hence we select the nodes within the first 10 ms of electrical activation to detect the CRT leads in the 2D BEP. As a result there are two distinguishable clouds of points (i.
e. two sets of unconnected vertices), whose centers correspond to lead locations, which are automatically detected with a simple k-means classification algorithm  [38]  (k=2). The center of each cloud can be considered as the lead location, because the electrical activation induced by a single lead will start at the point where the lead is located and uniformly propagated following the myocardial fiber direction  [6] ,  [7]. The localization of the RV lead is estimated on the epicardial EAM data since we do not have measurements from the endocardial RV. Therefore, the resulting localization mainly represents the epicardial breakthrough (BT) induced by the endocardial RV lead rather than its exact location. Nevertheless, the myocardium is relatively thin at the apical RV, thus we consider activation time differences between the endocardial and epicardial RV to be negligible comparing to the LAT range in the two ventricles.
b: LEAD APICALITY INDEX The 2D BEP represents the EAM data in a disk, where apical positions are at its center and basal ones are close to its circumference (radius equal to zero and to one in polar coordinates, respectively). In this way, the distance from any 2D BEP point to the center of the disk corresponds to its distance to the apex. The apicality index is then defined as 1 -r, where r represents a normalized version of this distancesince we map the EAM to an unitary disk, all distances are normalized according to a vanishing conformal Laplacianso that values lie with the [0, 1] interval (close to zero or one if the point is basal or apical, respectively). The apicality index is then computed for each lead location, estimated as described above, providing a quantitative measure with a common reference system to compare lead positioning in different EAM data.
A. DATA ACQUISITION PROTOCOL A total of 4 representative pigs (average weight = 34 (30/35) kg) are studied in this paper, which are a subset of a larger database  [41]. Two of them present a myocardial infarction located at the septal wall, whereas the other two do not have structural disease. A complete description of the whole dataset and its associated experimental protocol is given in  [41]. Animal handling was approved by the Institutional Review Board and Ethics Committee at the Hospital Clínic, Barcelona, Spain, and conformed to international guidelines  [42]. LBBB was induced in all animals using radio-frequency ablation, assisted by high rate pacing (160bpm) during the burning process to prevent ventricular fibrillation. All pigs were implanted with a RV apical lead (Beex RF45, Sorin Group, Milan, Italy) and a LV one (subxiphoid access or via coronary sinus, Situs OTW, Sorin Group).
Pacing was performed with a pacing system analyzer (ERA-3000, Biotronik, Berlin, Germany), and optimized after the ablation procedure  [43]. Contact mapping data were collected using an electroanatomical mapping device (CARTO-XP, Biosense Webster, Diamond Bar, CA) after introducing a 3. 5-mm tip catheter (Thermo-Cool Navi-Star, Biosense Webster Inc, Diamond Bar, CA, USA) through the femoral artery until reaching the endocardium of the LV (endocardial EAM) by the retrograde aortic access. Then, using the same catheter and system, an epicardial map of both ventricles was recorded. For this purpose, percutaneous pericardial access was obtained by a transthoracic subxiphoid puncture. EAMs were subsequently generated by reconstructing information obtained from the set of sparse acquisition points where the clinician placed the catheter to measure the electrical activity (around 2500ms at 1 kHz).
Corresponding EAM data representation and analysis for Pig #4 is provided as Supplementary material (Figure  S2 ). This is an example in which a simple index such as the Recovery fails for assessing the benefits of the CRT. It presents a high Recovery (343%) but a poor RV/LV resynchronization that is reflected on a poor inter-ventricular dyssynchrony index (IVD CRT = -12. 25ms). Furthermore, the LV-TD CRT shows a short LV transmural delay of 5. 25ms, which suggest that the LV lead is correctly located. This case presents a huge scar (86% of transmurality) located in the antero-septal area. This scar location highly influences the PS activity, making the TAT at the endocardium larger than normal in baseline. Depending on the scar size, it can happen that inducing a LBBB will not introduce an additional delay.
This work was supported in part by the  Spanish Ministry of Science and Innovation  ( TIN2011-28067 ), the  Spanish Industrial and Technological Development Center  (cvREMOD-CEN-20091044), the  Seventh Framework Programme  ( FP7/2007-2013 ) for research, technological and demonstration under grant agreement  VP2HF  (no. 611823 ), and the  Spanish Ministry of Economy and Competitiveness  under the  Maria de Maeztu Units of Excellence Programme  ( MDM-2015-0502 ). http://ieeexplore. ieee. org. , provided by the author.
In a number of cases, selective (face to face or phone) interviews were organised. Information was collected between January and May 2016 and data analysed in June 2016. The questionnaire was structured in 4 sections. A general section meant to collect information on the interviewee: the company, its business and the general approach to open data (both in terms of using open data and contributing to the movement as a ' donor' of data). Section 2 and 3 focussed on the use of open data, in particular open data for research activities or open data for strategic monitoring. The last section (4) allowed to collect information on the exploitation of open data, its advantages and disadvantaged and the impact of open data on intellectual property strategies. The number of open questions was reduced to the minimum and multiple choices were preferred in order to ease the treatment of data by statistical analysis.
Four companies (company b, d, j and k) defined themselves as ' aggregators', that collect and aggregate (open) data to identify biomarkers and develop drugs, that develop molecular models for the design and optimization of drug candidates, that analyse and extract value from different datasets for clients in different fields (chemistry, cosmetics, pharmaceuticals, but also luxury, finance, etc. ); one company (company i) falls under the category ' developer' as it commercialises apps based on algorithms developed and tested using (open) data. Six respondents (company c, e, f, g, h and m) answered as ' enrichers', and these were biotech and pharmaceutical companies exploiting open data to support internal R&D activities (drug/vaccine development, molecular and in vitro diagnostics, etc. ). Two companies (company a and l) are ' enablerers', providing access and facilitating the exploitation to medical data (e.
As an example, the Company Expert System (previously Temis) developed Luxid® Navigator http://community. temis. com/home, a navigator that allows the exploitation of information from articles, patents, meeting proceedings and databases. As indicated in the company website, a number of (leading) pharmaceutical companies benefit of the semantic technology to support information management strategies. If the companies interviewed use extensively open data, their attitude towards the sharing of internal data is more conservative (Figure  1 ). This is not surprising, pharmaceutical and biotech companies have inherited the business model of the chemical industry and patented pharmaceutical products account for 80 to 90 percent of sales  (Friese et al. , 2006). But these same companies are users of open data, we can then extrapolate that these data are originating mainly from activities of the public sector.
The use of open data by human resources departments (e. g. to identify key expertise and hire staff) is the least common of the uses mentioned in the survey. Currently, the classical tools used by companies for strategic monitoring are the patents and the publications; for their nature, patents provide information useful in the short term, while publications cover the medium and long-term (especially for the prospective ones). Most of this information is not available for free, as the access is subject to fees to the journals (in the case of articles) or to databases (for patents): Lexisnexis 3  or Questel 4  are just a few examples. Half of the respondents still take advantage of information from articles and patents (from 70% to 95%) compared to information from open data (from 5% to 30%).
Other problems are the lack of standardization of data (what is shared, how it is presented, dependent by both the data producers and the infrastructures where data is hosted), the lack of data intentionality (for what purpose data was collected) and the lack of acquisition protocols (how data was produced), all essential information for the analysis of the data itself. The main consequence of all these gaps is that any user interested in exploiting open data must spend time and resources to analyse the datasets, homogenise them; in a word, curate them, before they can be used. Also, the success of open data could bring it to death; as more and different types of data become available through the internet, it becomes more and more complicated to find them and to exploit them in a way that takes advantage of their complexity and quantity.
the data made open by pharmaceutical companies molecular data (genomic, proteomic, metabolomic data) represents the large majority, because of the availability of well recognised infrastructures to host it; data on clinical trials records (World Health Organization International Clinical Trials Registry Platform and ClinicalTrials. gov registry 1 ) is also open as since 1997 the Food and Drug Administration Modernization Act requires that data on the effectiveness of drugs clinical trials under clinical trials is made public. The adverse events reporting data 2 is another database that support the postmarketing surveillance on the safety of drugs or biological products.
Children were eligible for inclusion in the analysis of the trial if they were resident in an eligible village, they were recorded in the enumeration conducted in January 2008 as planning to be enrolled in class 2, 3, or 4 at the government school located in their village in the 2008-2009 academic year, and if, after hearing an explanation of the trial, their parent(s) or guardian (s) did not choose to opt out of the trial. The remaining 125 CHAMPION control villages did not meet the eligibility requirements for STRIPES but were nevertheless offered ECL outside the trial. A key purpose of implementing ECL in the CHAMPION control villages was to give Naandi a presence in the control villages, hence helping to ensure that CHAMPION data collection was carried out to the same standard in intervention and control villages.
Trial management The trial was managed by a TSC and a trial management group (TMG). The TSC met periodically to discuss overall progress. The TMG met monthly to ensure efficient day-to-day running of the trial. The Naandi Foundation was the local trial coordinating centre and was responsible for the research components and data management in the field. The trial database was designed and supported by Effective Intervention. LSHTM was responsible for all statistical aspects. The NICE Foundation was subcontracted to implement the interventions.
Data collection To reduce bias, the research/data collection team was selected and managed by the Naandi Foundation and was independent of the intervention teams. While the research team could not be masked to the intervention status of a village, they were trained to collect data in an identical manner in the intervention and control villages. No data from the research team were used by the intervention team. The research team monitored and collected data about pregnant women and their babies in both intervention and control villages. The team included a research coordinator who led the team and was responsible for timely data entry; a research officer who validated the collected data and monitored field staff; 19 data supervisors who monitored enumerators, supervised the data collection process, obtained detailed follow-ups on pregnancy outcomes, and collected information for VAs when necessary; 432 enumerators, to cover the 464 villages, who identified eligible women and tracked pregnancies; and data entry operators who were responsible for data entry and generation of monitoring forms.
In each village, the enumerator conducted a monthly pregnancy check. The data were then transported to Hyderabad, where all data were double-entered into a database, from which forms were printed for the next monthly monitoring and taken to the field. If a woman was identified as pregnant, the database triggered a follow-up form after the EDD. Data supervisors conducted a follow-up interview 6 weeks after delivery. Information about women who could not be located or were known to have migrated beyond the trial area was sought through their relatives in the village. If a live-born baby was suspected to have died aged 28 days or less, or a woman known to be pregnant was suspected to have died during pregnancy or within 42 days of the end of pregnancy, a specially trained data supervisor interviewed the family and completed a VA form to enable independent assessors to assign the primary cause of death.
Data about health knowledge, health practices including health service usage, and satisfaction with care were collected by the research team for women with an eligible pregnancy. Data supervisors visited enumerators monthly and cross-checked a sample of their reports. The supervisors and enumerators met monthly at regional headquarters to deposit monthly survey results, check that forms were properly completed, and discuss any problems in the field. Flags were created in the database to check on inconsistencies or missing data in a timely manner.
Results Enumeration was carried out between August and November 2007, with randomisation on 18 February 2008. After an inception period, the intervention was initiated from 1 August 2008. The assessment start date (see Methods) was 1 December 2008. The DMC met by teleconference to review interim analyses on 2 occasions but did not recommend early stopping. The intervention ended on 31 May 2011. Data collection was completed on 30 November 2011. In the 464 villages, 20,282 enumerated women met the eligibility criteria (9,871 in control and 10,411 in intervention villages, a difference attributable to chance). Non-eligibility was mainly due to sterilisation. A further 9,387 newly married women were enrolled in the trial villages-more in intervention villages (4,266 control, 5,121 intervention).
Also, if there was a bias in favour of the intervention villages, this might be expected to apply to both mortality outcomes, not only neonatal mortality. A second limitation is that fetal losses were not divided into stillbirths and miscarriages based on gestational age, both because gestational age was not reliably reported and because of potential biases, as women in the intervention arm may have reported pregnancies earlier than control women. However, rigorous training was conducted to help data collectors to distinguish between neonatal and fetal losses. Proportionately more women were recruited in the trial post-randomisation in the intervention arm, which might reflect women wishing to access the intervention package. While it seems implausible that women would marry into a village purely for that reason, the implications were nevertheless explored by an analysis of the interaction of arm with timing of enrolment, which was found to be significant.
Acknowledgments We would like to thank all the women and their families who took part in the trial; the independent DMC ( G. Piaggio ,  L. Wright ,  S. Goudar );  Rakhi Nair  and  M. J. Wesley  ( NICE Foundation ) for coordinating delivery of the intervention and human resource management;  Raja Ram Mohan Reddy  ( Naandi Foundation ) for management of field research staff;  Mark Fisher  ( Effective Intervention ) for database design and support; Josede  Sousa Ramos  ( J Ramos Consultoria ) for help with statistical programming; and  Nadine Seward  for support with updating references.
provided funding for this study. URL: http://cep. lse. ac. uk/_new/research/effective_ intervention/default. asp. The funder was represented on the Trial Steering Committee but health centres [NPHCs]). was not shown the interim unblinded analyses. After the final analysis, the funder took part in interpreting the data and writing the paper. AE gratefully acknowledges the financial support of the  US National Science Foundation. to the funder,  Effective Intervention (EI)  (PB is the Executive Chair; CJ, AE, RL were paid employees of EI; GP received consultancy from EI; and  DE, CF , and VM received research grants funding from EI), but had no competing interests. RM and PR declare potential competing interests due to the involvement of the  Naandi Foundation  and the  NICE Foundation  which are together involved in health programs intervening with women and children in rural and urban Telangana (previously AP) Approvals were obtained from the institutional review board of the L V  Prasad Eye Institute ,.
As previous works have unequivocally revealed the importance of rigorous validation of operational wave forecasting systems  [3, 4]  and the analysis of extreme wave height events using models  [5, 6]  and shore-based high-frequency radar (HFR)  [7, 8] , the objectives of this paper were twofold. The first objective was to analyze the main features of extreme wave height events in Galicia over the wintertime of a two-year period (2015-2016) by using HFR remote-sensed hourly wave parameters in tandem with predictions from an operational wave (WAV) forecasting system running in the Iberia-Biscay-Ireland (IBI) area, denominated (IBI-WAV, Figure  1b ). The second objective was to showcase the application of satellite-derived Hs estimations for the daily skill assessment of the IBI-WAV wave forecast system and how the use of wave altimetry can effectively increase the possibility of obtaining better spatiotemporal coverage of wave data collection.
Attention was focused on monitoring Ophelia-one of the major hurricanes on record in the easternmost Atlantic-during its 3-day track over Ireland and UK  (15-17 October 2017). Although weakened and downgraded to a post-extratropical cyclone, it brought impactful weather in terms of heavy rain, damaging gusts (up to 150 km/h), large waves, and storm surges with subsequent coastal flooding. In this context, the ability of the IBI-WAV model to capture basic synoptic details of the wave height field during these unusual circumstances has been evaluated. In this paper, the model setup of IBI-WAV and the datasets are described in Section 2. Results are presented and interpreted in Section 3. A detailed discussion and future work are addressed in Section 4. Finally, the main conclusions are summarized in Section 5.