dataesr/scientific-paragraphs-categorization huggingface dataset:
non-fatal injury estimation Non-fatal injury estimation is also described in more detail in GBD literature. Key components in this process are as follows. First, data on incidence of non-fatal injury causes (eg, road injuries) is obtained from the GBD collaborator network and other injury research groups and researchers around the world. Data are cleaned and organised according to GBD study guidelines. Next, incidence of each cause of injury is modelled in DisMod-MR 2. 1, which is a Bayesian meta-analysis tool used extensively in GBD research. Incidence estimates of injuries requiring medical care for each cause of injury then stream through an analytical pipeline. During this process, injury incidence is split into.
Original research inpatient and outpatient to account for the different severity that is expected to occur. The coefficient that determines this split is derived from locations where both inpatient and outpatient data are available. After this, we measure the proportion of each cause of injury that leads to one of 47 different natures of injury using clinical data where both cause and nature are coded as well a Dirichlet statistical modelling process. Based on these steps, the incidence of each cause is also split into incidence of each cause-nature, which is the proportion of a given cause's incidence leading to some specific nature of injury being the most severe injury sustained as estimated by the Dirichlet regression. 2] [23] [24] [25] [26] [27]  For short-term injuries, incidence is converted to prevalence based on multiplying incidence by an expected duration of injury as determined by physicians and injury experts involved in the GBD study.
Dr. Rakovac reports grants from World Health Organization, during the conduct of the study. Dr Shariful Islam is funded by National Heart Foundation of Australia and Institute for Physical Activity and Nutrition, Deakin University. Dr. Sheikh reports grants from Health Data Research UK, outside the submitted work. Dr. Singh reports personal fees from Crealta/Horizon, Medisys, Fidia, UBM LLC, Trio health, Medscape, WebMD, Clinical Care options, Clearview healthcare partners, Putnam associates, Spherix, Practice Point communications, the National Institutes of Health and the American College of Rheumatology, and Speaker's bureau of Simply Speaking, owns stock options in Amarin pharmaceuticals and Viking pharmaceuticals, serves on the steering committee of OMERACT, an international organization that develops measures for clinical trials and receives arm's length funding from 12 pharmaceutical companies, serves on the FDA Arthritis Advisory Committee, is a member of the Veterans Affairs Rheumatology Field Advisory Committee, and is the editor and the Director of the UAB Cochrane Musculoskeletal Group Satellite Center on Network Meta-analysis, outside the submitted work.
ACKNOWLEDGEMENTS We are grateful to the  US CDC ,  Fort Collins , for providing the ZIKV PRVABC59 strain. We thank  Kenneth Eckels  for providing ZPIV and generously sharing his expertize on ZIKV vaccines, and  Nelson Michael  and  Kayvon Modjarrad  from  Walter Reed Army Institute of Research, USA , for critical discussion, review and edits of this manuscript. We also thank the  Sanofi Pasteur PRD  teams for producing and characterizing the ZPIV-SP vaccine. We thank  Sandrine Montano ,  Florine Guillaume ,  Sandrine Raynaud ,  Sophie Fraysse ,  Terence Villa , and  Jérémy Pontvianne  for their contributions to the animal experiments and analytical assays. We thank  Julie Piolat  for her contribution to the statistical analysis of the results. We thank  Lucie Engelhart  and the staff of the animal facilities of  Sanofi Pasteur  for help with the in vivo studies.
, 2015). However, primary production within the Arctic ice pack is still poorly understood. The Green Edge project was conceived in an effort to better understand the Arctic PSB from the level of fundamental physical, chemical and biological processes to that of their interactions within the ecosystem and at spatial scales ranging from local to pan-Arctic. Besides studying each major component of the processes controlling Arctic PSB, another objective of Green Edge was to investigate its impact on the nutrients and carbon dynamics within the ecosystem. A total of three Green Edge campaigns were conducted: two ice camp campaigns on landfast sea ice in 2015 and 2016 and an oceanographic cruise aboard the CCGS Amundsen in Baffin Bay in 2016. In this article, we present an overview of an extensive and comprehensive dataset acquired during two surveys conducted at the Green Edge ice camp.
Data quality control and data processing Different quality control procedures were adopted to ensure the integrity of the data. First, the raw data were visually screened to eliminate errors originating from the measurement devices, including sensors (systematic or random) and errors inherent from measurement procedures and methods. Statistical summaries such as average, standard deviation and range were computed to detect and remove anomalous values in the data. Following from this, data were checked for duplicates and remaining outliers. Once raw measurements were cleaned, data were structured and regrouped into plain text comma-separated (CSV) files. Each of these files was constructed to gather variables of the same nature (e. g. , nutrients). In each of these files, a minimum number of variables (columns) were always included so the different datasets could be easily merged together (Table 1).
seanoe. org/data/00487/59892/, last access: 2 December 2019,  Massicotte et al. , 2019a). In the following sections, we present a subset of these variables along with the methods used to collect and measure them. For each of these variables, time series or vertical profiles are used to describe the data. Data cleaning and visualization were per-formed with R 3. 6. 1 (R Core Team, 2019). The code used to produce the figures and the analysis presented in this paper is available under the GNU GPLv3 license (https://github. com/PMassicotte/greenedge-icecamp-data-paper, last access: 2 December 2019). The code used to process and tidy the data provided by each researcher is also publicly available (https://gitlab. com/Takuvik/greenedge-database, last access: 2 December 2019) under the GNU GPLv3 license.
Physical data Some meteorological variables were measured during both campaigns. Starting on 27 March 2015, air temperature, relative humidity, wind speed and snow depth were measured. Data were recorded using a CR1000 Campbell data logger. Field measurements were performed most days to obtain snow physical variables. These included vertical profiles of snow density and specific surface area with 1 cm vertical resolution and visual determination of snow stratigraphy. Snow spectral albedo in the 400-1100 nm spectral range was also measured during these field measurements. Snow measurements are detailed in  Verin et al. (2019). Underwater conductivity, conservative temperature and depth (CTD) vertical profiles were measured using a Sea-Bird SBE19plusV2 CTD system (factory-calibrated prior to the expedition) deployed from inside the Polarhaven tent between the surface and a 350 m depth.
The data were post-processed according to the standard procedures recommended by the manufacturer and averaged into 1 m vertical bins. During the sampling periods, absolute salinity (S A ) was generally greater than 31. 5 g kg -1 (range of 4-34. 4 g kg -1 ). Flushes of freshwater at the ocean surface due to snow and ice melt started slowly at the beginning of June with the largest peaks and pulses taking place late June when absolute salinity decreased to approximately 4 g kg -1 (Fig. 3 ). Note that the new standard of absolute salinity is used for the remainder of this paper  (Oziel et al. , 2019; Randelhoff et al. , 2019). Ocean current profiles in the water column were measured using a downward-looking 300 kHz Sentinel Workhorse Acoustic Doppler Current Profiler (ADCP, RDI Teledyne) mounted directly beneath the sea-ice bottom.
Inherent optical properties (IOPs) IOP measurements were made using an optical frame equipped with the physical and bio-optical sensors that were factory-calibrated before each field campaign. A Seabird SBE-9 CTD measured temperature, absolute salinity and pressure. A WetLabs AC-S was used for spectral beam attenuation (c, m -1 ) and total absorption (a, m -1 ) between 405 and 740 nm, and a BB9 (WetLabs) and BB3 (WetLabs) were utilized for backscattering coefficients (b, m -1 ) between 440 and 870 nm. During both campaigns, pure water calibration was performed for the AC-S sensor on each sampling day and linear regression of these calibration values as a function of time was computed for each wavelength of absorption and attenuation signals. Following from this, the offset applied during the data processing was taken on this linear regression at the exact date of the measurement.
Image acquisition was triggered by chl a in vivo fluorescence, with excitation and emission wavelengths of 635 and 680 nm, respectively. Grayscale images were processed to extract regions of interest (ROIs) and their associated features (e. g. , geometry, shape, symmetry, texture, etc. ), using a custom made MATLAB (2013b) code  (Sosik and Olson, 2007; Olson and Sosik, 2007;  processing codes are available at https://github. com/hsosik/ifcb-analysis, last access: 2 December 2019). A total of 231 features (see the full list and description at https://github. com/hsosik/ifcb-analysis/wiki/ feature-file-documentation, last access: 2 December 2019) were derived on the resulting ROIs and were used for automatic classification using random forest algorithms with the EcoTaxa application  (Picheral et al.
, 2017). A learning set was manually prepared for each year, with ca. 20 000 images annotated and used for automatic prediction. Each automatically annotated image was further validated by visual examination and corrected when necessary. The final 2015 and 2016 datasets consist of 124 247 and 57 397 annotated images and their associated features in 39 and 35 taxonomic categories, respectively (Fig. 12 ). As it was impossible to count the number of cells in each image, we assumed one cell per image. To account for potential underestimations of cell abundance when colonies or chains were imaged, the biovolume of each living protist in the images was computed during image processing according to  Moberg and Sosik (2012). Using carbon to volume ratios from  Menden-Deuer and Lessard (2000) , biovolume was converted into carbon estimates, as described in  Laney and Sosik (2014).
Code and data availability The raw data provided by all the researchers, as well as metadata, are available on the LEFE-CYBER repository (http://www. obs-vlfr. fr/proof/php/GREENEDGE/ greenedge. php, last access: 2 December 2019, LEFE-CYBER, 2019). The data presented in this paper and in Table 2 are hosted at SEANOE (SEA scieNtific Open data Edition) under the CC-BY license (https://www. seanoe. org/ data/00487/59892/, last access: 2 December 2019,  Massicotte et al. , 2019a). Detailed metadata are associated with each file including the principal investigator's contact information. For specific questions, please contact the principal investigator associated with the data (see Table  2 ).
Conclusions The comprehensive dataset assembled during both Green Edge ice camp campaigns allowed us to study the fundamental physical, chemical and biological processes controlling Earth Syst. Sci. Data, 12, 151-176, 2020  www. earth-syst-sci-data. net/12/151/2020/  NM was at the LOCEAN when he participated to GreenEdge. He contributed to field operations by installing the Ice-T mooring and measuring sea ice and under ice physical properties. Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. This project would not have been possible without the support of the hamlet of Qikiqtarjuaq and the members of the community, as well as the Inuksuit School and its Principal Jacqueline Arsenault. The project was conducted under the scientific coordination of the Canada Excellence Research Chair in Remote Sensing of Canada's new Arctic frontier and the CNRS and Université Laval Takuvik Joint International laboratory (UMI3376).
Financial support. The Green Edge project is funded by the following French and Canadian programs and agencies: ANR (contract no. 111112), CNES (project no. 131425), IPEV (project no. 1164), CSA, Fondation Total, ArcticNet, LEFE and the French Arctic Initiative (Green Edge project). Review statement. This paper was edited by Giuseppe M. R. Manzella and reviewed by two anonymous referees.
The setup was designed by SM, EL, SB and ClG. They also processed and analyzed the data. CatS, the LEFE-CYBER database manager is acknowledged for her help in gathering the data presented. FlD designed the snow-specific surface area measurements and participated in the 2015 campaign, along with GV, who performed the 2015 and 2016 snow-related measurements. DV, ALdS, IP and PrG sampled at the ice camp for flow cytometry, phytoplankton cultures and molecular biology. CGR, ALdS, PrG and FLG established phytoplankton culture isolates. DoM and MT performed flow cytometry measurements for the 2015 and 2016 ice camp samples. DaM analyzed and plotted the flow cytometry data. FJ and VG measured the bacterial production during the 2016 ice camp.
In addition, the number of specimens per species required for MALDI-TOF MS database creation was determined. The first MALDI-TOF database of New Caledonian mosquitoes was assembled and the optimal threshold for mosquito species identification according to the sensitivity and specificity of this technique was determined. Results: This study showed that the identification scores decreased as the trapping duration increased. High identification scores were obtained for mosquitoes preserved on silica gel and cotton at room temperature and those frozen at -20 °C, even after two months of preservation. In addition, the results showed that the scores increased according to the number of main spectrum patterns (MSPs) used until they reached a plateau at 5 MSPs for Ae. aegypti. Mosquitoes (n = 67) belonging to 11 species were used to create the MALDI-TOF reference database.
This method is one of the most sensitive and specific techniques but requires pre-existing DNA sequence information for species identification  [17]. This method remains also expensive and time consuming; it is therefore not suitable for routine mosquito identification  [19]. Recently, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) was introduced for mosquito species identification  [19, 20]. This method generates unique protein mass spectra, "fingerprints" specific to each species  [21]. When the spectrum is compared to a database of reference spectra, the software calculates a score that indicates the reliability of the identification provided. The score is then compared to a threshold value and allows species identification  [21]. This method is much less costly than the molecular method  [22].
In addition, the MALDI-TOF MS technique can be used for mosquito species complex identification (i. e. morphologically indistinguishable species)  [19, 20]. Although this technique is promising for mosquitoes' identification, some parameters have to be considered prior to MALDI-TOF MS analysis. Indeed, previous studies on several arthropods have underlined the impact of trap type used for sampling, the preservation methods and the body part used for protein extraction on the spectra profile  [23] [24] [25] [26]. The impact of trapping duration on MALDI-TOF identification, however, has never been evaluated. In addition, no clear consensus for the minimum number of specimens per species required for a robust MALDI-TOF database is available. Regarding the threshold value for mosquito species identification, there is no value registered by the software manufacturer.
The threshold value at 1. 8 established following mosquito blind testing  [19]  should be confirmed to allow routine mosquito species identification with MALDI-TOF MS. The overall aim of this study was to optimize the use of MALDI-TOF MS for mosquito identification, while taking into account the parameters previously described. The specific objectives were: (i) to determine whether trapping duration affects MALDI-TOF MS identification; (ii) to determine the best methods to use for short, medium and long-term preservation before MALDI-TOF MS analysis; and (iii) to determine the number of specimens per species required for the MALDI-TOF MS database creation. The threshold value for mosquito identification with the database was also determined, based on MALDI-TOF MS sensitivity and specificity.
Biological material Laboratory-reared mosquitoes were used to measure the effect of trapping duration and preservation methods on MALDI-TOF MS results. Aedes aegypti (Bora-Bora strain) were raised in the insectarium at 28 °C ± 2 °C and a relative humidity of 80 ± 10%. Larvae were reared until the pupal stage in a plastic tray containing 1. 5 l of water and fed with beer yeast ad libitum. Pupae were then collected and transferred to a mosquito cage. Adults that emerged were fed with a 10% glucose solution and were harvested when they were 3-5-days-old and killed before MALDI-TOF MS analysis. Eleven species of field-collected mosquitoes were used to create and validate the MALDI-TOF database (Table  1 ). Field-collection of both larval and adult stages was undertaken from March 2016 to April 2019.
Morphological and molecular identification of mosquitoes All mosquitoes used in this study were morphologically identified using dichotomous identification keys before all experiments  [11, [27] [28] [29]. As no mosquito species complex is currently reported to be present in New Caledonia, all mosquito species were morphologically distinguishable. Morphological identification was confirmed through double-blind evaluation by experienced entomologists. In addition to morphological identification, molecular analysis was also performed for all the specimens used as references for the creation of the MALDI-TOF database. For this, the head, thorax and abdomen of each mosquito were removed and transferred in 1. 5 ml microtubes for DNA extraction. DNA was extracted using a DNA Blood and Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer's recommendation.
To amplify cox1, cycling involved an initial denaturation at 94 °C for 3 min, 35 cycles of 94 °C for 1 min, 50 °C for 1 min and 72 °C for 1 min, followed by a final extension step at 72 °C for 10 min. To amplify ITS2, the thermal conditions were: 4 min at 94 °C, 35 cycles of 1 min at 94 °C, 120 s at 51 °C and 1 min at 72 °C, and a final extension step at 72 °C for 10 min. Finally, PCR products were sequenced (Genoscreen, Lille, France) and the sequences were assembled and analyzed using PREGAP and GAP software (version 4. 10. 2, 2019; The GAP Group, GAP-Groups, Algorithms and Programming, Aachen, Germany). Obtained sequences were compared with mosquito sequences available in the GenBank database using the BLAST platform (http:// blast. ncbi. nlm. nih. gov/Blast. cgi).
Protein extraction for MALDI-TOF analysis For protein extraction, the experiment was focused using only legs, which is the widely used method for MALDI-TOF MS mosquito database creation. This reduces bias caused by potential traces of bloodmeals or microbiota in the spectra  [19]. The sample preparation method used was adapted to the protocol described by Raharimalala et al. [32]. All the legs of each mosquito were removed and rinsed in a microtube with 1 ml of 70% ethanol for 60 s, followed by 1 ml of distilled water for 60 s. After total elimination of water, 3 metal beads of 2. 4 mm diameter, 15 µl of acetonitrile (50%) and 15 µl of formic acid (70%) were added to the microtube. Each sample was subsequently homogenized using a MagnaLyser, version 1. 1 (Roche, Mannheim, Germany) with 3 cycles of 30 s at a frequency of 3000× rpm.
MALDI-TOF parameters and analysis Spectra ranging from 2000 to 20,000 Daltons were acquired using a Microflex MALDI-TOF mass spectrometer (Bruker). Measurements were performed with flex-Control software, version 3. 3 (Bruker) with detection in the linear positive-ion mode at a laser frequency of 60 Hz and laser power between 40-50%. Each spectrum obtained corresponds to an accumulation of 240 laser shots from the same spot performed in 6 regions. For each reference sample used to create MSP, the 8 replicates were individually measured 3 times. The quality of the spectra obtained was verified with MALDI flex analysis software, version 3. 3 (Bruker). Then, a selection of 20-24 high quality mass spectra per sample, obtained with the 8 spots measured 3 times, were imported into the MALDI Biotyper compass software, version 3.
1 (Bruker). MSP containing the average of peak mass, the average of peak intensity and peak frequency information was subsequently calculated for each reference sample. For the sample intended to be queried against the MSP, one measurement was realized for each spot (one spot per sample). The mass spectrum of each sample was compared to the MSPs in the MALDI-TOF database with the MALDI Biotyper compass software. This software calculates a log-score value (LSV) ranging from zero to three, reflecting the similarity between sample spectra and MSP. A Log-score value greater than or equal to a threshold value supports the accuracy of the sample identification.
Determination of the required number of MSPs per species for database creation Analysis was performed in order to determine the required number of specimens per species for MALDI-TOF MS database creation. The assessment was investigated for three species from the field: Ae. aegypti; Cx. quinquefasciatus; and Cx. annulirostris. Nine MSPs were created for Cx. quinquefasciatus while 10 MSPs per species were created for Ae. aegypti and Cx. annulirostris. The spectra of 30 mosquitoes per species were compared with 1 to 9 corresponding MSPs for Cx. quinquefasciatus and from 1 to 10 MSPs for the other species. The objective was to test the number of required MSPs to obtain the best identification score. The LSV median was computed for the 3 species for each number of MSPs tested.
Creation and validation of the MALDI-TOF MS database of mosquitoes from New Caledonia The findings observed during these previous steps were taken into consideration during the database creation. For this, 11 mosquito species collected in the field were used to create the MALDI-TOF database of New Caledonian mosquitoes. A total of 67 fresh specimens were used to create MSPs entered in the database. Seven to 30 specimens from the field belonging to these 11 species underwent MALDI-TOF analysis to evaluate the accuracy of this newly created MALDI-TOF database. Their spectra were blindly queried, regardless of the presence of the species' MSPs in the database. The reliability of species identification was estimated using the LSVs obtained from the MALDI Biotyper software (Bruker).
Determination of the threshold value for mosquito identification with MALDI-TOF The database was used as a reference to determine the threshold value for which both the sensitivity and the specificity of the MALDI-TOF identifications were optimal. This was achieved by comparing the spectra of fieldcollected specimens in a blinded experiment. In this process, all the field specimens belonging to the 11 species were first blindly queried against MSPs for each species individually. Then, these were also queried against MSPs for a random selection of 6 species on the one hand, and of the 5 other species on the other hand. For these analyses, sensitivity (Se) and specificity (Sp) were calculated at various threshold values, ranging from 0 to 3. In addition, positive and negative predictive values were determined.