dataesr/scientific-paragraphs-categorization huggingface dataset:
-A high spatio-temporal resolution continuous marine-land topography and bathymetry database, with height precision better than 20 cm is needed. However, lower resolution datasets such as those that can be produced with Sentinel 2 or other data may still be useful for understanding coastal evolution (e. g. ,  Poupardin et al. , 2016). Concerning that latter issue, present-day information on the evolution of coastal zones is currently acquired at local to regional scales by coastal observatories, which act as a means of transferring information between science, operational observations (including space-based), and coastal stakeholders  (Suanez et al. , 2012). Presently, the existing information at national, multinational, and global scales originates from these entities (e. g. ,  Eurosion, 2004).
Retracking Conventional and SAR Mode Altimetry So far, quality and quantity of altimeter data retrieved in the coastal zone have improved thanks to new specific processing applied to conventional altimetry and use of the new DDA mode, and fitting processing. For conventional altimetry, new sub-waveform partitioning and fitting have been designed to tackle waveform inhomogeneities in the satellite footprint typical of coastal waters, due to presence of land or patches of calm water. This novel approach restricts the fitting to a portion of the reflected signal  (Deng and Featherstone, 2006; Yang et al. , 2012; Passaro et al. , 2014 Passaro et al. , , 2018;; Dinardo et al. , 2017; Roscher et al. , 2017; Peng and Deng, 2018). A successful way of selecting this portion is based on an adaptive algorithm, which depends on the sea state (as in  Passaro et al.
Level 3 Altimetry Datasets The development of dedicated altimetry datasets at level 2 (COASTALT, PEACHI) and level 3 (X-TRACK, ALES/COSTA, DUACS-HR) has been advancing toward the high alongtrack resolution and/or special processing to reduce noise and provide data near coastlines or in shallow shelf waters, offering an opportunity of exploitation for scientific applications. A table is published in  Cipollini et al. (2017)  and a regularly updated version is available at http://www. coastalt. eu/#datasets. Several studies compared these data sets with independent measurements (e. g. ,  Gómez-Enri et al. , 2016; Vu et al. , 2018; Xu et al. , 2018). Quality assessments for Sentinel-3A and CryoSat-2  (Bonnefond et al. , 2018a; Fenoglio et al. , 2019)  and AltiKa data  (Bonnefond et al. , 2018b)  have also been performed.
, 2014; Chen et al. , 2014; Fenoglio-Marc et al. , 2015b)  and to improve their forecasting  (Madsen et al. , 2015; De Biasio et al. , 2016 , 2017; Bajo et al. , 2017; Li et al. , 2018). While it is opportunistic for a single altimeter to capture a storm surge, a constellation of altimeter missions especially with wide-swath altimetry could significantly enhance the chance  (Antony et al. , 2014; Turki et al. , 2015; Han, 2017; Han et al. , 2017). Study of coastal sea level variability and change is another application. Use of coastal altimetry dataset have revealed higher annual amplitudes in sea level in the presence of narrow coastal currents  (Passaro et al. , 2015a (Passaro et al. , , 2016) )  improving the determination of coastal tides  (Piccioni et al. , 2018). The coastal altimetry data also helped in improving knowledge about spatiotemporal changes of the coastal ocean circulation (e.
OPPORTUNITIES FOR INTEGRATION As mentioned in section "Monitoring Coastal Zones With Multiple Observing Systems, " the evolution of coastal zones results from several forcing factors (natural and anthropogenic). Assessing the impacts of present and future coastal evolution requires an understanding of the interactions between biophysical and socioeconomic systems and assets on land, and even in the adjacent sea. Looking at both land and sea, as well as natural and anthropogenic factors and related consequences, is the only way of pursuing the monitoring of such complex systems under various impacts (not only environmental). In addition, modeling and synthesis activities have to accompany the measurements so that the research can provide insight into the future evolution of coastal areas. To benefit a broad range of end users and a large variety of scientific and societal applications, a global database of various coastal products should be developed.
Troupin et al. (2015)  shows clear evidence during a glider mission conducted along a track from the Franco-Indian SARAL/AltiKa altimeter  (Verron et al. , 2015)  located in the Western Mediterranean close to Ibiza Island, where the SOCIB HF radar facility provided hourly surface current velocities. Surface drifters were also deployed in the studied region. Comparisons reveal a reasonable agreement between all platforms (drifter, SARAL/AltiKa, glider, and HF radar), with SARAL/AltiKa able to capture the northern edge of a meander, which lied on a shallow bathymetry less than 10 km from the coast. Pascual et al. (2015)  and  Verron et al. (2018)  show that SARAL/AltiKa data can be retrieved at a distance of only 7 km from the coast. The derived velocities reveal coherent mesoscale features with high temporal variability among the different cycles and with general reasonable agreement with HF radar fields.
New generations of SAR altimeter missions have demonstrated that compared to conventional altimetry (or low-resolution mode), this technique significantly reduces the measurements level of noise and improves the along-track spatial resolution  (Boy et al. , 2017). The comparison between altimetry, ocean gliders and ADCP showed a significant improvement, order 30% in resolution and 42% in velocity accuracy using a synthetic aperture radar mode with respect to lower-resolution mode of conventional altimetry  (Heslop et al. , 2017). Integrating the three datasets provided valuable insight into the variability of oceanographic features, in an area of the Mediterranean that remains chronically under sampled and has demonstrated benefits to improve knowledge on coastal and fine-scale dynamics. Future works will require expanded observing capabilities with new high-resolution experiments integrating multiplatform approaches with numerical simulations (see an example in  Pascual et al.
, 2014). However, as the methods allowing maximizing the efficiency of adaptation are not well established yet, there are still research needs to assess how observations can best support adaptation. In addition, in order to exploit the economic investments in space-based observing systems, the development of operational services based on EO data is a key goal. The operational services (downstream services) are based on the integration of multi-source information (EO, modeling, and in situ) with the institutional/policy, social and economic knowledge. The sustainability of these operational services can be guaranteed by their usefulness and thus their capacity to fulfill coastal user (intermediate and final) needs  (Taramelli et al. , 2014 (Taramelli et al. , , 2015a,b),b). Several coastal altimetry experiments showed that applying locally all the altimeter processing steps up to sea level anomalies at high-resolution is an efficient way to get a more accurate estimation of the coastal dynamics.
In fact, outside the last tenth kilometers near the coast, ocean retracking algorithms perform well and a dedicated high-resolution processing, particularly data selection and noise reduction, enables to monitor smallscale patterns with more details, which is relevant for regional oceanographers. Figure  13  shows across-track geostrophic velocities computed from altimetric SLA in the Gulf of St Laurence (Canada) from the global (from Copernicus Marine Environment Monitoring Service, CMEMS) and regional highresolution datasets. The statistical comparison of along-track altimeter to in situ sea level observation at three coastal tide gauges, gives better results with the HR regional product in term of correlation (between 0. 6 and 0. 9 depending on the altimeter point) and similar results in term of RMS of the difference (<0.
06 m). This confirms that the good quality of the CMEMS altimeter data is maintained in the HR regional product. In terms of communication and capacity building, the Coastal Altimetry Workshop series organized for more than 10 years by ESA  (Restano et al. , 2018)  have already attracted the regional ocean modeling community as end-users. A significant need has been expressed for continuous education of the user community on the data products and updates, as it is a daunting task for a user to determine which dataset to use. This objective can be achieved augmenting information for non-expert altimetrists and coastal oceanography users with, for example, clear statements of the time range of data availability for single altimeter platform data sets, and notes of proximity to the coast of valid data.
Model users have also requested global unified multi-platform high along-track resolution products, such as what the TAPAS initiative (Tailored Altimeter Products for Assimilation Systems) is delivering for European seas. The TAPAS working group set up within CMEMS is a very good example of the way to proceed to engage end-users by establishing a strong link between Data Assimilation teams and Sea Level production center. Both communities are strengthened by the exchanges and more efficient research and development driven by data usage. A similar initiative called ARCOM for Altimetry for Regional and Coastal models was proposed in 2015 for extending the TAPAS experience to a larger community, targeting the GODAE OceanView Coastal and Shelf Seas Task Team (COSS-TT) group. A pilot workshop was conducted in 2015 in association with a COSS-TT international coordination meeting.
The Coastal Altimetry community brought tutorials about altimetry processing and products to coastal modelers in order to feed discussions on the main issues (reference surfaces, geophysical corrections, observation errors). In 2017, a dedicated session was re-conducted in the COSS-TT meeting. The outcome of this last ARCOM event was a claim for more details to help making a choice between the available datasets (coverage in time and spatial, strength, etc. ). An example of end-user engagement is the ESA eSurge-Venice project that brought together research institutes and stakeholders (sea level forecast center of the Venice Municipality) and that has been set up to help improve the modeling of storm surges around city of Venice using coastal altimetry and scatterometry  (De Biasio et al. , 2017).
g. ,  Liu et al. , 2015). We argue that a timely challenge for increasing the salience of coastal information is to connect global providers of EO data (such as space agencies) with the emerging networks of coastal observatories. In other fields of geosciences, several previous initiatives have efficiently established such links and stimulated the use of satellite-based observations in scientific and operational activities. This is the case for the ESA "supersites" or NASA "natural laboratories, " providing access to all geophysical data for key priority sites affected by earthquakes and volcanic eruptions. This could be an example to follow for coastal zone evolution. Among the forcing agents, we have focused on coastal sea level monitoring by satellite altimetry in this paper. The spatial and temporal complexity of coastal sea level requires a finescale monitoring.
Satellite altimetry is capable of providing a unique and global long-term observational dataset to characterize how sea level variability evolves from the open ocean to the coastal zone. It can help to fill the gap between open ocean and tide gauges. The extension of the satellite-based sea level record toward the coast with quality comparable to open ocean is a key requirement for a proper understanding of the variability of sea level at all temporal and spatial scales. Conventional altimetry fails to provide sea level data within 10 km from the coast. But efforts performed during the past decade by the international coastal altimetry community with support of space agencies, in particular ESA, have clearly demonstrated that retracking of radar echoes collected by the satellite as it approaches the coast and computation of new geophysical corrections optimized for the coastal areas (e.
For scientific and societal applications, the next step is now to undertake systematic exploitation of altimetry-based data in order to deliver worldwide coastal sea level products, easily usable by a broad variety of end users. There is strong demand for such information from both the scientific community and other stakeholders. On a longer perspective, we have also discussed promising new technology based on W-SoOp reflectometry to complement the information provided by satellite altimetry on sea level in the coastal zone. This technique can potentially get closer to the coast than conventional altimetry and with the use of constellation provide data at higher temporal resolution that can be used to study the dynamic coastal zone. The next step on this technique is to study the impact of POD uncertainty in the final altimetric retrieval, as this is currently the limiting error source for this technique.
ACKNOWLEDGMENTS This community white paper is the outcome of the merging, suggested by the OceanObs'19 organizers of three community white paper abstracts dedicated to the coastal zone, submitted by AC, SV, LF-M and  RSh. JBe  was designated as coordinator during the merging process. Part of the work that was carried out by the  Jet Propulsion Laboratory, California Institute of Technology  personnel in this manuscript was under a contract with the  National Aeronautics and Space Administration.
FUNDING Part of this work has been carried out with the financial support of the following research projects:  Spanish  research grant  ESP2015-70014-C2-2-R (MINECO/FEDER ) and  Spanish Ministry of Science, Innovation and Universities :  RTI2018-099008-B-C22 ,  ESA contract Sea_Level_cci  (No. 4000126561/19/I-NB ).
(a) Adaptive plasticity In order to test whether LHt exhibit lower plasticity than N-LHt, we conducted a meta-analysis on studies reporting reaction norms in different trait types across environments and species. We selected publications employing the keywords 'life history & morphology & plasticity' between 1991 and 2006 from all databases in Web of Knowledge (WoK-ISI) and between 2007 and 2011 from Web of Science (ISI). We identified 583 papers in total, and added a few studies published between 1987 and 2011 known to us that also met the criteria. From this initial set, we extracted data from 213 studies reporting reaction norms as a measure for plasticity. The data extracted were the mean values for each environment evaluated in each study and the independent sample sizes. Quantification of the reaction norms was achieved through the computation of the coefficient of variation (CV) of the trait expression across environments, a dimensionless parameter allowing the evaluation of proportional responses as a mean-standardized measure  [41].
We computed 5885 CVs for 211 species exposed to 2 -11 environment levels. As a visual validation for reporting and publication bias  [42] , we plot the number of studies through time and a funnel plot in the electronic supplementary material, figures S1 and S2. Traits were categorized as: LHt: survival and reproduction; and N-LHt: behaviour, morphology, metabolism and physiology, and development. When available, we extracted data directly from tables or the text; alternatively, we extracted quantitative information from figures using the software IMAGEJ  [43]. Environmental variation was grouped into six categories: (i) environment quality, (ii) interspecific interactions, (iii) intraspecific interactions, (iv) intrinsic resources, (v) photoperiod and light; and (vi) temperature. When genotypes or families were included, taxonomic groups were classified based on species identity (ID) as defined by the NCBI taxonomy database  [44].
We clustered organisms according to NCBI at the taxonomic level of Phylum (referred as taxa in table  1 ). Electronic supplementary material, S1 and table  S1  show details of the categorization. We analysed the data by generalized mixed-effect models in software R (  [45] ; R Core team, version 3. 2. 3, package lme4. ) CVs were log transformed and used as the response variable. Reference ID was included as a random effect accounting for confounding effects within the same publication. We also included the number of data points retrieved from a single study-Repetitions (electronic supplementary material, table  S1 )-as weights to assess potential bias towards a few studies or species. The focus of our study was the evaluation of the explanatory variables trait type, environment type and taxonomic group (electronic supplementary material, table  S1 ).
Also, we re-analysed the data in a collapsed dataset that included the average CV for all means reported in the same publication, that reported the same species, environment, environment type, location, measurement, number of environments and breeding (see factors in the electronic supplementary material, table  S1 ). Because variability in sample size and error structure might bias our results, we examined the residuals from the selected models in both the original and collapsed datasets. When comparing these models, we did not find any quantitative difference, which suggests that our results are robust regarding potential sample size bias and biased estimates related to number of environments evaluated. To gain a better quantitative understanding of how frequently our prediction was met, we computed, for each pair of LHt and N-LHt, the differences in CV within studies (n ¼ 3939), limiting our analysis to the 103 studies that measured both categories of traits.
The average sample size reported from the 5885 CVs was 119. 15. The collapsed dataset comprised 1868 CV averages. When testing those two as independent variables, we found that the best-fit models corresponded to the models selected from the original dataset (data not shown). In both cases, we did not find any major biases in the residuals of the models (electronic supplementary material, figure  S3 ). Including the number of environments worsened the models in all three cases. Together, these results support the robustness of the data provided by the extraction of the trait means from previous publications-we are confident that the CV is capturing plasticity even when accounting for only two environments. Our results were robust to explorations of other factorsthe type of experiment (laboratory or field), whether natural populations or laboratory lines were used, how many environments per study were explored, the number of data points per study, the study year or whether only LHt, only N-LHt or both types of traits were reported within a study (electronic supplementary material, table  S2 ).
(e) Study limitations We realize there is, as for most studies, a potential bias caused by the selected methodology. For instance, the scaled plasticity (CV) could introduce noise, but it is an accepted method to standardize variability among traits with fundamentally different units  [63]. Our sample size should be sufficiently large for generalizing our overall findings, supported by the absence of a major publication bias (electronic supplementary material, figure  S1 ), although relevance of findings for specific groups might suffer from low amount of data (e. g. green algae), and trait types might differ in their measuring error. Trait types are also expected to differ in their variance owing to biological characteristics, which could influence CVs. For example, behavioural traits might be measured with less accuracy and precision compared to morphological traits.
Acknowledgements. We would like to thank the  Bettencourt Foundation  for its support, including the fellowship appointed to C. M. M. Special thanks to  Alice Demarez ,  Josh Van Buskirk ,  Josh Auld ,  Anurag Agrawal , and  Rick Relyea. Miguel Tejedo ,  Federico Marangoni ,  Cino Pertoldi ,  Alex Ritcher-Boix ,  German Orizaola ,  Alfredo G. Nicieza ,  David A ´lvarez ,  Ivan Gomez-Mestre ,  Christer Bro ¨nmark ,  Thomas Lakowitz ,  Johan Hollander ,  Wilco C. E. P. Verberk ,  David T. Bilton ,  Anssi Laurila ,  Beatrice Lindgren ,  Ane T. Laugen ,  Marjan De Block ,  Mark A. McPeek  and  Robby Stoks  for their direct contributions to the database.
Introduction Sensors are slowly becoming an integral part of our life, monitoring our heartbeat  [1] , counting our steps  [2] , customizing our habitat according to our preferences  [3] , or detecting the presence of intruders in areas we wish to protect  [4]. For ease of deployment and economic reasons, most of these sensors are integrated into wireless devices which transmit the collected information through wireless sensor networks (WSN) to a central server. As a consequence, multiple WSN platforms appeared on the market (e. g. , Waspmote  [5] , NI WSN  [6] , NODE+  [7] , Zolertia Z1  [8] , etc. ), providing generic hardware and software on the networking side, and a variety of interchangeable physical sensors, fitting numerous use cases. Several applications require sensors to be deployed at the ground level: vehicular traffic monitoring on a road segment  [9] , bridge and tunnel monitoring  [10] , or dam monitoring  [11]  are some examples among others.
Sensors Characteristics and Deployment For all experiments discussed in this paper, we used the TelosB platform [28]. This platform is equipped with a 8 MHz microprocessor, a 10 KB RAM, a 1 MB external flash memory and a set of sensors (temperature, humidity and light). In all our experiments, the collected data were stored in the external memory. For communication, TelosB uses the CC2420 radio module. This module is IEEE 802. 15. 4 compatible and it is widely used for industry and research in the field of low power wireless sensor networks. The CC2420 radio shares with other technologies, e. g. , IEEE 802. 11 (WiFi) and Bluetooth, the unprotected ISM frequency band (2400-2483. 5 MHz). In other words, if a WSN were deployed in an area covered by equipments using these different technologies, the communication of nodes in the network would interfere with these other devices.
Communication Protocol In all our experiments, each sensor executed the same protocol, which we implemented in Contiki OS 2. 7  [34]. For a deployment using N sensors, all nodes were initialized with identifiers ranging from 1 to N. To avoid internal interferences, nodes communicated using a round-robin algorithm. When it was a node's turn to send messages, it broadcasted M messages at a frequency of one message per 500 ms. This upper layer synchronization allowed us to disable the carrier sense mechanism and practically use a transparent MAC protocol. The transmitted messages contain the identifier of the sender, a sequence number, the transmission power used by the message sender and a dummy application payload. Upon message reception, a node stores in its external flash memory all the information found in the message, as well as information on the RSSI and the link quality indicator (LQI) provided by the receiver radio module.
TP53 mutation spectra generated can be compared with those of human tumours recorded in the International Agency for Research on Cancer TP53 mutation database. Environmental mutagens that have demonstrated and validated the utility of the HIMA include ultraviolet radiation, aristolochic acid, and benzo[a]pyrene. The TP53 mutation patterns induced by these mutagens in the HIMA corresponded to those found in human tumours from patients exposed to these mutagens. The approach presented helps to deepen our understanding of human cancer aetiology.