dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of the spatial timing of influenza epidemics in Middle East and North Africa and in Europe and Central Asia in six consecutive influenza seasons (from 2010-2011 to 2015-2016). The WHO FluNet database, 2010-2016 This includes all countries in the following influenza transmission zones: south-west Europe, northern Europe, eastern Europe and Central Asia (see [WHO Influenza Transmission Zones]). The spatial timing of influenza epidemics in the Middle East and North Africa varied greatly between seasons, both according to the longitude and to the latitude, and in most cases, there was no correspondence with what was occurring in Europe and Central Asia in the same season. A notable exception was the 2015-2016 season, Jul Jul Jun Jun May May Apr Oman Apr Oman Month primary peak Dec Jan Feb Mar Morocco Algeria Tunisia Egypt Israel Jordan Turkey Georgia Iraq Qatar Iran Pakistan Month primary peak Dec Jan Feb Mar Qatar Algeria Egypt Israel Jordan Pakistan Iran Morocco Iraq Tunisia Turkey Georgia Nov Bahrain Nov Bahrain Oct Oct Sep Sep Aug Aug Jul Jul 10°W 0°10°E 20°E 30°E 40°E 50°E 60°E 70°E 20°N 25°N 30°N 35°N 40°N Longitude Latitude Jul Iraq Jul Iraq Jun Jun Apr May Egypt Jordan Qatar Iran Apr May Qatar Egypt Jordan Iran Month secondary peak Sep Oct Nov Dec Jan Feb Mar Morocco Tunisia Israel Turkey Bahrain Georgia Oman Pakistan Month secondary peak Oct Nov Dec Jan Feb Mar Oman Bahrain Pakistan Israel Morocco Tunisia Turkey Georgia Sep Aug Jul Algeria Aug Algeria Jul 10°W 0°10°E 20°E 30°E 40°E 50°E 60°E 70°E 20°N 25°N 30°N 35°N 40°N Longitude Latitude TA B L E 3 Season a Longitudinal gradient Middle East and North Africa P-value Europe and Central Asia b P-value Latitudinal gradient Middle East and North Africa P-value Europe and Central Asia b P-value 2010-2011 -.
Evaluation criteria The only data collected at the time of pre-inclusion will be age, gravidity, and parity. (a). Intubation, ventilation > 24 h?: yes/no (b). Nasogastric tube feeding > 4 days?: yes/no (c). Intensive care > 4 days?: yes/no (d). Phototherapy in the case of hyperbilirubinemia: number of sessions (e). Clinically suspected fracture diagnosed using x-ray?: yes/no (f ). Intraventricular or cerebral hemorrhage diagnosed using cranial ultrasound?: yes/no (g). Neonatal transfusion: number of packed cells (h). Ischemic encephalopathy diagnosed using magnetic resonance imaging (MRI) scan?: yes/no (i). Department and duration of hospital stay in days (j). Perinatal death (i. e. , up to 7 days following delivery).
Data collection methods and management All information required by the protocol will be recorded in the case report forms. Data will be collected as it is obtained and will be recorded in the digital case report form. Comments will be added to justify or explain missing data or values that are outside the expected norms. The digital case report form will be compiled using an Internet-based tool for data collection. Investigators will be provided with a document to help them use this tool. Each investigator will be responsible for the accuracy, quality, and relevance of all data recorded. Any changes will be subject to an audit trail that will log all amendments made since the data was initially recorded. In the event of an amendment, the investigator may be asked to provide the reason for changing the data.
At the end of the study, the investigator will be asked to provide a paper copy that they have authenticated (dated and signed). The investigator must archive a copy of the authenticated document to be given to the sponsor. The following documents will be archived at the Promotion Unit of Angers University Hospital and in the buildings of the Department of Obstetrics and Gynecology of Angers University Hospital until the end of the period of practical use, in accordance with the regulations in force. These documents are: Protocol and appendices, unforeseen amendments Signed consent forms Individual data (authenticated copies of raw data) Follow-up documentation (monitoring) Records of serious adverse events (SAEs).
| DATA PRODUCTION AND DESCRIPTION The sensors deployed at the site have been changed and expanded over its 25 years of operation; Table  1  lists which sensors are used and when. All the sensors have been logged using Campbell Scientific data loggers which have been periodically replaced, as shown in Table  1. The sensors were sampled at either 1, 5 or 10 s intervals and 15 or 30 min averages (or sums for rainfall) calculated and stored in the logger. The dataset described here has been through a range of assessments and adjustments to provide a time series that is consistent in terms of instrument calibrations or other changes, and as complete as possible. The following quality control steps are applied to every time series: Periods of known downtime are removed, such as ser- vicing periods, instrument or power failures.
For modelling applications, complete time series are often requested so a set of core measurements will be gap-filled for this purpose and included in the CEDA database (ambient air temperature, barometric pressure, relative humidity, total solar radiation, rainfall, wind speed and direction) as described in a later paper. Evaporation of water vapour from the peat-bog will also be added to the database when it has been calculated from the measurements of water-vapour flux which are currently being assessed for quality.
The sensors have been sent for calibration and this information will be added to the database when it is available. In contrast, the net radiation sensors appear stable but there is a step change in the measurement range when the REBS Q7  (Campbell Scientific, 1996)  was replaced with a Kipp and Zonen NR-Lite in 2001 (minimum readings decreased from ~-92 to -175 W/m 2 ). The soil heat flux and temperature measurements show a decline in the range of observed values over the first 3-4 years of operation which may be due to the sensors taking a long period to bed-in or the build-up of the vegetation and peat layer above them. Regular measurements of the vegetation above the installation were not made but when the sensors were replaced in 2006, it was noted that they appeared to be 4-5 cm further below the moss layer than when initially installed.
Comparison with the Campbell probes showed they gave virtually identical readings (unpublished data). T A B L E 1 (Continued) T A B L E 2 Annual summary statistics for each parameter (with the exception of the relative humidity measurements the data review procedure only resulted in the exclusion of a very small percentage of the data, less than 1% on average, so the %DC values are very close to the raw data capture)     llasolutions. com/uk/en/products/met/met-environmental/ products/tbrg. aspx, Figure  3 ), as specified by the WMO. The old and new gauge gave very similar totals, 775 and 778 mm respectively from 22nd March to 31st December 2016, when scaled for data capture. From 2017, data from the new gauge will be reported and the old gauge decommissioned. The Vaisala FD12P Present Weather Sensor records weather and visibility, as well as estimating precipitation amounts, by measuring the amount of infrared light scattered at an angle of 33°.
It also includes a heated capacitive sensor to detect surface water. The precipitation amount is estimated by scaling the optical intensity while the type of precipitation is inferred using air temperature, optical intensity and capacitive signal. Its estimates of rainfall amounts do not agree well with the tipping buckets and, although they detect precipitation at the same times, data capture was relatively low for the FD12P in most years due to data communication and logging issues. The 'present weather' and 'past weather' codes are included in the database, where they represent the most frequently recorded weather type over the last 15 min and one hour respectively. The FD12P can report either WMO or National Weather Service (NWS) codes, in this case the WMO 4,680 codes are recorded (Appendix D).
Measurements then appeared quite consistent until the end of 2013 when the system developed a logging fault and measurements below 73% were not recorded properly (in 2015 this cut-off increased to 78%). In 2016, a new sensor and serviced data logger were installed and measurements returned to a more normal range. From 23/03/2016, measurements from the Rotronics HC2S3 are reported in the database and the HMP50 was logged alongside it until 2017. Comparison of the two show they were very closely matched, giving an average of 87%, and maximum 100%, although their minima were slightly different with the Rotronics reading 29% and the Vaisala 27%. Despite the uncertainty on the measured values for some periods, all the data are included in the database because the measurements correlate well with those from the other sites, giving an indication of variations   In the gap-filled dataset, these periods will be removed and replaced with adjusted data from the other sites.
The site has proved very useful in the study of greenhouse gases as it represents a significant ecosystem type for NW Europe. A full list of published papers that have used Auchencorth data can be found at http://www. auchencorth. ceh. ac. uk/biblio. The site is one of a few rare examples of long-term flux measurements over any ecosystem, comparable to sites such as Hyytiälä in Finland and others in the Fluxnet database, http://fluxnet. fluxdata. org/. At present, the meteorological measurements continue to be an essential adjunct to all the pollutant and trace-gas measurements, allowing researchers to make full use of the results and enhance our understanding of the land-atmosphere interaction and atmospheric chemistry. The long time series has the potential for many studies into the changes in the pollution climate and interactions between factors such as SO 2 to NH 3 ratios or climate and greenhouse gases, as well as the climate itself.
| DATASET LOCATION AND FORMAT The data are stored by the Centre for Environmental Data Analysis (CEDA, http://www. ceda. ac. uk/) in BADC CSV format, as annual files containing all variables. It is publicly available under the licence: http://www. nationalarc hives. gov. uk/doc/open-government-licence/version/3/. When using these data, you must cite them correctly using the citation given on the CEDA Data Catalogue record. Annual updates will be made, with a year's new data being added as soon as it has been fully quality controlled and checked.
ACKNOWLEDGEMENTS The measurements presented in this paper which underpin the air quality and greenhouse gas (GHG) observations at the  GAW Regional Site and EMEP Supersite  (The European Monitoring and Evaluation Programme (EMEP), http://www. emep. int/)at Auchencorth Moss were supported by the  UK Natural Environment Research Council  national capability funding under the  CEH Atmospheric Chemistry and Effects Science Area  (Award reference ceh020011). Several other organisations and projects that have provided support over the years are listed in Appendix B. The authors are grateful to the local landowners for providing access to the site ( A. C Stoddart & Sons ,  Halls Farm  and  Springfield Farm , www. springfieldmeats. co. uk, www. spring fieldfarmlivery. co. uk, www. lazylawn. co. uk/scotland.
06 ± 0. 05, 2. 68 ± 0. 05, 3. 18 ± 0. 13, and 4. 40 ± 0. 24 µm, and density 1850 kg m -3 , available in aqueous solutions (5% wt of particles). Microscopic Observations. All observations were made using a microscope (Leica DM 2500P) flipped horizontally so that the plane of observation is vertical and contain the gravity vector. Samples are held on a rotational stage (M-660 PILine®) with a maximal velocity of 720 • s -1 and controlled by a C-867 PILine®Controller. Small magnifications (10×, 20×) are used to visualize piles of particles with a good contrast; bigger magnifications (40×, 100×) are used to resolve single trajectories of particles. Images (3696 × 2448 pixels, with an acquisition rate up to 1 fps) and movies (1920 × 1080 pixels at 24 fps) are taken with a Nikon D7000. Image and Data Processing.
All image analyses were done with customdeveloped Python scripts, based on usual scientific libraries (Numpy 1. 11. 1 and Matplotlib 1. 5. 1) and image processing tools from Scikit-image (version 0. 12. 3) and OpenCV (version 2. 4. 11). Pile Angle. Statolith pile angles were measured using the following method. First, potential image drifts are corrected over the whole movie using image correlation on a reference pattern. Then, the edges of the statocyte cells are manually delineated for each cell where a pile is visible on the first frame of the movie (yellow line in Fig. 1B ). The image is cropped along the cell edges and a local minimum filter is applied to enhance the contrast of the pile, whose contour is extracted using a contour finding algorithm (blue line in Fig. 1B ). Finally, the pile angle is defined by linking the two points where the pile ceases to be in contact with the edges of the cell (purple line in Fig.
The cut coleoptiles were then placed in the PDMS container described in Plant Material. Statoliths leaking out of the cut cells were let to sediment 2 h under gravity in the clear sorbitol solution. Measurement of vertical fluctuations was performed on individual statoliths that had fallen to the bottom of the container (black squares in Fig. 4B ). Data are available on the open-source database Zenodo, https://zenodo. org/record/1186833.
Igor Ozerov for access to the clean room facilities for the fabrication of the biomimetic microcast, Othmane Aouassar for his help on experiments with wheat cuts made during his undergraduate internship, and Nicole Brunel for experiments on statolith visualization. This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant 647384) and by the French National Agency (ANR) under the program Blanc Grap2 (ANR-13-BSV5-0005-01), Labex MEC (ANR-10-LABX-0092), and A * MIDEX project (ANR-11-IDEX-0001-02) funded by the French government program Investissements d'avenir.
Every year in France, recurring flood events result in several million euros of damage, and reducing the heavy consequences of floods has become a high priority. However, actions to reduce the impact of floods are often hindered by the lack of damage data on past flood events. The present paper introduces a new database for collection and assessment of flood-related damage. The DamaGIS database offers an innovative bottom-up approach to gather and identify damage data from multiple sources, including new media. The study area has been defined as the south of France considering the high frequency of floods over the past years. This paper presents the structure and contents of the database. It also presents operating instructions in order to keep collecting damage data within the database. This paper also describes an easily reproducible method to assess the severity of flood damage regardless of the location or date of occurrence.
Damage data collection Although the word "damage" is usually defined as physical harm caused to an object, the concept of "flood damage" may refer to a wide range of items. There is a common understanding that flood damage can be described as direct or indirect as well as tangible or intangible  (Parker, 2000). We may refer to the notion of direct damage if caused by contact with flood water. On the other hand, tangible damage results if damage can be measured in monetary value and thus objectively quantified  (Parker et al. , 1987; Smith and Ward, 1998). This paper only focuses on direct and tangible damage data to enable the assessment of damage severity. The need for a comprehensive and inclusive flood damage database has been underlined by many authors over the years  (Gerl et al. , 2016; Mileti, 1999; NRC, 1999; Dilley, 2005; Bubeck et al.
, 2011; Elmer, 2012). The most prominent disaster-related damage databases used today are EM-DAT and NatCat  (Kron et al. , 2012). The emergency events database (EM-DAT) is a free and worldwide database on disaster-related damage using multiple sources  (Guha-Sapir et al. , 2015; Hoyois and Guha-Sapir, 2003). It contains information on fatalities and costs resulting from more than 20 000 disasters which have occurred since 1900. However, EM-DAT is only available at a country level and does not offer information on the type of damage. The Munich RE NatCatService private database also provides information on the natural disasters that have taken place since 1980 around the world. It also provides information on damage costs and fatalities per event at a regional scale, which is more precise than the EM-DAT database  (Guha-Sapir and Below, 2002).
However, information on the type of damage is still lacking. At the European scale, the HyMeX project has led to the creation of a database recording the societal impact of Mediterranean floods  (Llasat et al. , 2013). This database has gathered 385 flood events which occurred between 1981 and 2010. This HyMeX database is more accurate and comprehensive than EM-DAT and NatCat databases. But unfortunately it does not provide details about damage due to flood events. It is fair to say that the United States is years ahead of the rest of the world with regard to disaster-related damage data collection  (Tschoegl et al. , 2006). For instance, the American National Oceanic and Atmospheric Administration (NOAA) provides a database for flood fatalities and costs caused by weather events since 1903 at a county level, called the Storm Events Database  (Downton et al.
, 2005; National Climatic Data Center, 2015). SHELDUS is another county-level hazard database for the United States  (Hazards and Vulnerability Research Institute, 2013). Unlike NOAA's Storm Events Database, which is foremost interested in weather events independent of loss, SHELDUS focuses on loss data. Between 2008 and 2010, an experiment was also conducted by one of NOAA's laboratories to collect specific data on flash floods called the Severe Hazards Analysis and Verification Experiment (SHAVE;  Gourley et al. , 2013; Calianno et al. , 2013; Ortega et al. , 2009). In Europe, several countries have implemented their own databases on flood damage at the national scale. Germany has the HOWAS21 database  (Kreibich et al. , 2017) , Switzerland the Swiss Flood and Landslide Damage Database  (Kron et al.
, 2012)  and Italy the FloodCat database  (Molinari et al. , 2013). Italy also has developed several projects to enhance the collection of flood damage at the national scale. Among these projects, the AVI (Italian vulnerable area or Aree Vulnerate Italiane) project may be mentioned. The AVI project aimed to create a bibliographical and archive inventory of landslides and floods in Italy based on newspaper screening from 1918 to 1990  (Guzzetti et al. , 1994). More recently, the LAND-deFeND (LANDslides and Floods National Database) project has developed a new database structure to store and organize information on landslide and flood events  (Napolitano et al. , 2018). Both of these projects are very similar initiatives to the work presented in this paper. In France today, neither an easily accessible nor a complete database on flood-related damage is available at the local scale, that is, at the scale of the municipality.
However, an initiative was launched to this effect in 1976 by the French Ministry of Environment, although it did not yield a concrete outcome, as suggested in  Molinari et al. (2017). The French GASPAR database is available at the municipal scale. However, it only contains information on whether or not a municipality has been damaged by a flood and provides no information on damage type or severity. Although insurance companies have been collecting accurate data on flood damage, they only disseminate synthetic data at the department level (through ONRN http://www. onrn. fr/site/, last access: 2 February 2018) or for major events only (through the web site https://erisk. ccr. fr/faces/erisk-accueil. jsp, last access: 2 February 2018). The respect of privacy rights and/or commercial confidentiality makes it difficult to openly communicate information such as damage to private housing.
However, even insurance companies do not have access to complete flood damage data. Their data only concern private households and business premises, excluding all information on public infrastructures. Emergency services also have time-stamped and localized records of their operations during flood events such as rescue services or emergency pumping. However, there are no established standards on how to collect and organize those data at the national scale. This might make them difficult to aggregate but also to use and analyse from one location to another. Moreover, this heterogeneity as well as the confidentiality of these data add to the complexity of acquiring them. This review of the existing databases on flood-related damage at different scales has made it possible to identify the shortcomings and the requirements that need to be addressed in order to implement a new database.
Structure of the paper Although it is not fully comprehensive, DamaGIS provides a detailed database for flood-related damage combined with an assessment of damage severity. This database has only been implemented in the south of France for now, but this paper initiates a new approach to characterize, organize and share flood damage data at a fine spatial scale. This paper contains the necessary information to understand, use and continue completing the DamaGIS database. First in Sect. 2, the characteristics of the DamaGIS database are introduced along with its content and organization. Section 3 presents and explains the damage rating method used within the database. In Sect. 4, an analysis of the damage contents is provided. Section 5 presents the benefits, limitations and perspectives of the DamaGIS database.
Sources of information DamaGIS is a community-based and multisource database for object-specific flood damage caused by river floods. Since 2011, direct and tangible damage caused by flash floods in the south of France have been systematically collected in the DamaGIS database. To this aim, numerous sources of information have been used for comprehensiveness purposes: corporate websites, personal blogs, local authorities, public administration, on-site observations, social network media (Facebook, Twitter) and videosharing websites (Youtube, Dailymotion), and online media. To ensure data reliability, a direct web link to the sources of information is added to the database for each damage occurrence. For now, DamaGIS data have been manually entered by a small group of people, a very time-consuming process.
This bias will be corrected by means of multiple contributors. The objective for the database is to evolve it towards a participatory functioning. However, data may also be directly bought from social media companies using keywords and an area with a given radius. In this case, substantial extra costs should be taken into consideration. The various sources of DamaGIS damage data were analysed (Fig. 1 ) to highlight the prominence of new media, accounting for three-quarters of the data collected (78 %). New media can be defined as the sources of information shared using digital technology such as blogs, social media, online media and websites. Using these new media makes it possible to obtain information at a fine spatial scale where traditional media are not always present. Among the new media, social media holds a prominent place.