dataesr/scientific-paragraphs-categorization huggingface dataset:
Abstract. Considering the magnetic reconnection and the viscous interaction as the fundamental mechanisms for transfer particles and energy into the magnetosphere, we study the dynamical characteristics of auroral electrojet (AE) index during high-intensity, long-duration continuous auroral activity (HILDCAA) events, using a long-term geomagnetic database , and other distinct interplanetary conditions (geomagnetically quiet intervals, co-rotating interaction regions (CIRs)/high-speed streams (HSSs) not followed by HILDCAAs, and events of AE comprised in global intense geomagnetic disturbances). It is worth noting that we also study active but non-HILDCAA intervals. Examining the geomagnetic AE index, we apply a dynamics analysis composed of the phase space, the recurrence plot (RP), and the recurrence quantification analysis (RQA) methods.
, 1987; Maizel and Lenk, 1981; Trulla et al. , 1996). They constitute proper tools to treat such nonlinear, non-stationary signals as in geophysics processes. Such analysis method is applied to the HILDCAA events, for the first time to our knowledge, allowing a comparison of dynamical characteristics. By applying the nonlinear tools, this work investigates AE under some distinct physical conditions of the interplanetary medium: Alfvénic fluctuations followed by HILDCAA, Alfvénic fluctuations not followed by HILDCAA (also related to corotating interaction regions (CIRs) and high-speed streams (HSSs)), other disturbed interplanetary conditions, and geomagnetically quiet time. This work proceeds as follows. Section 2 describes the methods for analysis. Section 3 presents the geomagnetic database and how we apply the methodology.
Database and methodology procedure For the present work, we have considered an updated list of 136 HILDCAA events occurring between 1975 and 2012, compiled by  Hajra et al. (2013). The events were detected from the geomagnetic AE and middle-to low-latitude disturbance Dst indices by using the four strict HILDCAA criteria  (Tsurutani and Gonzalez, 1987 ): (i) the events have peak AE intensities greater than 1000 nT, (ii) the events last for more than 2 days, (iii) high auroral activity lasts throughout the interval, i. e. AE never drops below 200 nT for more than 2 h at a time, and (iv) the events take place outside of the main phase of a geomagnetic storm. For a better understanding, the main phase is determined by the depression in the horizontal component, from middle to low latitudes, in the geomagnetic field.
This behaviour is identified and quantified using the hourly value equatorial Dst index, which represents ideally the axially symmetric disturbance magnetic field at the dipole equator on the Earth's surface. This index is derived by monitoring the equatorial ring current variations (http://wdc. kugi. kyoto-u. ac. jp/dstdir/dst2/onDstindex. From the list, the first 16 events were eliminated due to incomplete information. Among the remaining events, 33 % were preceded by geomagnetic storm main phase (Dst < -50 nT). Thus, 80 events were analysed in this work, because we selected the events classified as pure HILDCAAs, i. e. events not preceded by any geomagnetic storm main phase. As data sets, the high-time-resolution (1 min) AE indices were analysed to study the dynamical characterization of the HILDCAA events by the RQA method.
To eliminate any marginal influences, we considered a 2280 min interval centred at the middle point of a HILDCAA event. This number of records was determined by the least interval among the events. For a quantitative comparison of disturbance geomagnetic regimes, we also performed the same RQA during the geomagnetically quiet period listed in Table  1. The quiet days follow the criteria: K p ≤ 3 0 , Dst ≥ -50 nT, and AE ≤ 300 nT. The planetary 3 h range K p index was introduced by J. Bartels in 1949 and designed to be sensitive to any geomagnetic disturbance affecting the Earth (http: //www. gfz-potsdam. de/en/section/earths-magnetic-field/ data-products-services/kp-index/explanation/). It completes a set of indices to diagnose the level of geomagnetic disturbance in a global sense. The geomagnetic indices  (Rostoker, 1972)  can be obtained from the World Data Center, Kyoto, at http://wdc.
Acknowledgements. Margarete Oliveira Domingues  and  Odim Mendes  thank the  MCTIC/FINEP (CT-INFRA  grant  0112052700 ) and  FAPESP  (grant  2015/25624 -2 ) for the financial support. Odim Mendes ,  Margarete Oliveira Domingues  and  Ezequiel Echer  thank the  Brazilian CNPq agency  (grants  312246/2013-7 ,  306038/2015-3  and  301233/2011-0 , respectively). Rajkumar Hajra  thanks the  FAPESP 2012/00280-0  for a postdoctoral research fellowship at  INPE , and now the work supported by  ANR  under financial agreement  ANR-15-CE31-0009-01  at  LPC2E/CNRS. Varlei Everton Menconi  thanks the  MCTIC-PCI program  (grant  455097/2013-5 ) by the research fellowship at  INPE. The authors would like to thank the team of  Interdisciplinary Center for Dynamics of Complex Systems ,  University of Potsdam , for the RQA tools (http://tocsy.
The assembly contained a total of 164 scaffolds with an average read length of 299,210 bp. The N 50 was 1,408 kb, and the maximum contig length was 1,707 kb. The total sequence length of the resulting draft genome was 54,281,849 bp, with an overall GC content of 47. 08%. A total of 374,040 bp were repeats, representing 0. 76% of the assembled genome size as predicted by repeat masker tool (http://www. repeatmasker. org/). Gene prediction was performed using Augustus 3. 0 (7), producing 17,781 proteincoding genes. Our Blast2GO-based annotation analyses (8) have revealed 79% of annotated genes while 21% remained unannotated. The genome analysis of secondary metabolite biosynthesis gene clusters using antiSMASH 3. 0 software (9) highlighted the presence of 6 type I polyketide synthases, 5 nonribosomal peptide synthetases, 2 hybrid polyketide synthase/nonribosomal peptide synthesis (PKS/NRPS), and 2 terpene synthases genes.
Analysis of the sequence with the CAZy database (10) identified 230 genes with activity involving carbohydrates, including 101 glycoside hydrolases, 74 glycosyltransferases, 1 polysaccharide lyase, 10 carbohydrate esterases, 29 carbohydrate-binding modules, and 15 auxiliary activities. As expected, the array of genes encoding enzymes for the deconstruction of terrestrial plant cell walls is very reduced and the genome does not encode any cellulase. Interestingly, the genome of Cadophora malorum Mo12 encodes a candidate alginate lyase, perhaps reflecting adaptation to marine carbohydrates. Due to the significant potential for synthesis of secondary metabolites and the presence of a particular portfolio of genes encoding carbohydrate-active enzymes, we believe that the genome sequence of Cadophora malorum Mo12 (UBOCC 108058) will result in the discovery of useful gene products that may be exploited for biotechnological application.
, 2012). These higher resolution data sets are also needed to investigate species habitats, allowing catch per unit of effort (CPUE) standardization and more generally the relationships between the species distribution and the variability of climate and environment. These studies require standardized data sets of historical catch data, allowing inter-comparisons of results. The secretariat of the Indian Ocean Tuna Commission (IOTC) is collecting and publishing fishing data (catch, effort and size frequency of catch) for stock assessment analyses and estimations of fishing mortality. There are two data sets providing nominal and geo-referenced data. The nominal catch data set is the official annual catch declaration by each member country to the IOTC. It gives total annual catch by species and by fishing gear.
However, there is no georeferenced information on where the fish are caught. This information is partially provided in the second data set that gives subsamples of monthly geo-referenced catch and effort by fleet. A key objective of the present study, by examining all available information, is to build a geo-referenced data set, i. e. , with monthly catch spatially distributed, that matches the total (nominal) catch for fleets of fishing countries providing both geo-referenced and nominal catches, taking into account as far as possible the size selectivity of the fishing gears. This requires revising catch, effort and length frequency data of bigeye fishing in the Indian Ocean available from the public IOTC database (www. iotc. org), using a careful screening, standardization and validation approach.
There are many problems with such long time series of data due to changes in fishing practices and data reporting. The data sets are constructed from various spatial resolutions ranging between 1 • ×1 • and 10 • ×20 •. Catch and effort data are derived from various types of fishing gears characterized by different fishing methods and target species. Consequently, the catchability, a key coefficient that links the catch to fishing effort and fish abundance, varies from one fishing mode to another. Over time, a variety of catch and effort units have been used that prevent long time series analyses. Finally, for studies requiring computing total fishing mortality, the geo-referenced catch data need to be raised to match the nominal catch. Therefore, the objective is to provide a standardized data set -with a definition of the longline, purse seine and othergears fisheries -to researchers from various disciplines that may have not the necessary expertise in fisheries sciences to interpret these data correctly.
Data Nominal and geo-referenced data (catch, effort and size frequency of catch) are freely available on the IOTC website (http://www. iotc. org/data/datasets). The catch and effort data were classified accordingly to three groups of gear type: longline, purse seine, and other gears. Bigeye tuna data were extracted from each group and analyzed separately. A screening of the geo-referenced data set using a topographic mask led to excluding 6. 87 % of longline, 1 % of purse seine and 0. 97 % of other-gears data with position incorrectly located on land (i. e. all of the cell at a given resolution is on land).
Longline Initial geo-referenced data set of longline bigeye tuna catch included five categories: longline targeting bigeye tuna (LL), longline targeting swordfish (ELL), fresh tuna longline (FLL), exploratory fishing longline (LLEX) and longline targeting shark (SLL). The last category was ignored since it contained only five bigeye catch observations. In the four remaining, 93 % of bigeye tuna catch was due to the LL category, including Japanese, Taiwanese, Korean, Seychelles, Chinese, Thai, Mauritian, Maldivian, and Philippine fleets. The ELL category contributed to 4. 4 % of longline bigeye catch data by Australian, La Réunion, Seychelles, South African, Portuguese, Mayotte and Mauritian fleets. The FLL fleets of Taiwan, China and Malaysia caught the remaining 2. 6 %, and LLEX contained only a few Indian longline data (0.
Purse seine Geo-referenced purse seine fishing data consist of large (PS) and small purse seine (PSS). PS has carrying capacity of about 1000-1500 t, while PSS has less than about 200-250 t  (Joseph, 2003). The PS consists of geo-referenced data from fleets of Spain, France, the Seychelles, Japan, Mauritius, Thailand, Korea, the former Soviet Union, NEIPS and NEISU. NEIPS data are those collected by European scientists onboard non-European vessels, while NEISU data were collected by Russian scientists from purse seine vessels of Liberia, Belize and Panama. The small purse seiners data consisted only of Indonesian observations. Almost all (> 99. 9 %) purse seine fishing data were at the resolution of 1 • × 1 • , and a very small number of data had a resolution of 5 • × 5 •. These data were subdivided between sets on free schools (FS), associated with artificial (FAD) or natural logs (LS), mixed strategy (MIX) and unknown sets (UNCL).
There were 11 % of sets purely on free school and 70. 8 % associated with logs. A very small number of data (< 0. 2 %) for small purse seiners were reported as unknown. The remainder (17. 9 %) consisted of large purse seiners operating either on free school or log but reporting a single fishing effort without distinction of the fishing strategy. Purse seine fishery is dominated by Spanish and French fleets, which together provide 65. 9, 65. 5 and 59. 5 % of FS, LS and MIX sets, respectively. Catches of the purse seine data were uniformly expressed in total weight. The number of fishing hours (FHOURS) is the most used unit of fishing effort (87. 3 %), followed by the number of fishing days (FDAYS, 7. 6 %) and the number of days at sea (DAYS, 4. 0 %). A very small number of records (1. 1 %) used number of sets (SETS) or number of trips (TRIPS).
Length frequency The IOTC also maintains a database of length frequency of catch collected onboard fishing vessels by observers or during landing operations. These data provide key information for population dynamics models, as well as for extrapolation of nominal catch to subsampled spatial distributions. The length-frequency catch data are aggregated either monthly or quarterly first to assist in the catch data standardization. For the final data set provided with this study, they are all aggregated on a quarterly basis. All bigeye tuna size samples were measured in centimeter fork length (FL). The original size data were distributed in 150 classes starting at 10 cm length with 2 cm intervals between each class. In this study the maximum size was limited to 200 cm, a limit used in most stock assessment studies  (IOTC, 2015; Langley et al.
Conversion of longline catch unit Length-frequency data were used to convert catch declared in numbers of individuals into catch in weight. The number to weight conversion is based on the length-weight relationship w = aL b , with w = weight (kg), a = 3. 661 × 10 -5 , L = fork length (cm), and b = 2. 901  (Nakamura and Uchiyama, 1966). The Japanese and Taiwanese longline length-frequency data were averaged to construct annual and single weight conversion factors on eight regions (see below). When temporal occurrence of catch and annual weight factors did not exist, the catches were converted using a single weight factor. This individual to weight conversion concerned the portion (30 %) of geo-referenced catch data of La Réunion ELL fleet that were declared in number of individual fish. It was also used for the geo-referenced catch data expressed in number of individual tuna in the Japanese LL (100 %) and Korean LL (75 %) fleets, before being raised to the nominal catch level (see below).
Raising geo-referenced catch and effort data to nominal catch level The IOTC geo-referenced data set provides a large subset of the total catch declared as nominal catch by each country and fleet. To compute total fishing mortality from geo-referenced fishing data, this catch needs to be raised to the level of nominal catch. This is a data processing step sometimes conducted directly by national fisheries statistics services before being provided to the regional fisheries management organizations (RFMOs;  Fonteneau et al. , 2013). When the difference between total annual nominal and geo-referenced catch was above 5 % for a given fleet, we used a raising factor I and added the product of I with the annual catch difference to the monthly catch of geo-referenced cell i, j. The factor I used to distribute the total annual catch differences was computed for each fleet and gear type using the following equation: I c, m, ij = C ij, m C ij, m , (1) where C i, j, m is the catch in the cell of indices i, j of a given month m.
The same approach and factor was used to raise the fishing effort associated with the catch C i, j, m. Unlike in geo-referenced data, the nominal catch data for purse seiners did not discriminate between type of sets (i. e. , FS or LS). To maintain this key information in the georeferenced data set the difference between total annual nominal and geo-referenced catch data were divided proportionally to the proportion of each set type available in the georeferenced data set. For the Japanese and Korean longline catch data expressed in number of fish (see below), we provide both the original catch data in number of individuals and the catch converted to weight and raised to nominal catch level (Sect. 2. 3).
Results Based on the nominal catch data, the majority of bigeye tuna landings in the Indian Ocean are provided by industrial longline (74. 2 %), followed by purse seine (18. 5 %) and other gears (7. 3 %). The Taiwanese, Japanese and Korean longline fleets together captured 68 % of longline catch. The Japanese fleet started to capture bigeye tuna in 1952, followed by the Taiwanese in 1954 and the Korean in 1965. The catch was largely due to the Japanese fleet during 1952 to the mid-1970s. Then, Korea until the mid-1980s and Taiwan became two other major players in longline fishery (Fig. 1 ). Finally, the Indonesian fresh longline and the NEI. FROZEN longline fleets respectively contributed to 11 and 5. 7 % of longline catch, but their geo-referenced catch data are unavailable. The nominal catches of the latter fleet were estimated by the IOTC secretariat from various non-reporting longline flags, including Honduras, Belize, Equatorial Guinea and Panama (Fig.
Purse seine There were 3472 outliers detected in the purse seine data set. For the largest fleet, i. e. , the Spanish PS-LS-FHOURS (17 586 data) and the French PS-LS-FHOURS (14 118 data), the outlier threshold value was set to 5 to avoid having too selective a criterion since variability in purse seine fishing CPUE can be much higher than with longline. With this threshold 5. 7 and 5 % of the records of the Spanish and French fleets, respectively, were detected as outliers (Table  S1 ). Fishing efforts of these outliers were corrected with the mean CPUE of neighboring records in a maximum radius (r) of 5 • (the resolution for purse seine data being 1 • ). For the associated log data set (LS), 94. 5 % of detected outliers were corrected, 31. 8 % were corrected using mean CPUE within radius 1 • , 36.
Effort unit Longline fishing efforts were converted to number of hooks from fishing days for the Portuguese ELL  (2006, 2007, 2013, 2014) ,  Mayotte ELL, Philippine LL, and Thai LL (2012, 2013) , as well as from the number of sets for the Thai LL  (2007, 2008). These efforts were converted using mean CPUE ratio for the period available with the reference unit. For the Mayotte ELL and the Philippine LL that only provided 1-year fishing-days data, the ratio was respectively calculated from the Portuguese ELL and the Thai LL number of hooks fishery. Three Spanish and two Japanese purse seine fleets required effort standardization. The Spanish efforts were standardized to number of fishing hours. It can be checked that converted efforts of the LS and the FS sub-data sets occupy ranges of the reference efforts (Table  S2 ).
Region Period Detected time breaks from period of time Detected time breaks from breakpoint series 10-25 years (% of detection) fixed to 1-4 (% of detection) ble 3). Other longline fisheries are the Korean LL (L7) and then the more recent fleets defined according to the IOTC criteria: longline (LL), fresh longline (FLL), swordfish longline (ELL), and experimental longline (LLEX). A last file (L12) gathers all outlier data for which there is low confidence (Table  3 ). Eight purse seine fisheries (S13-S20) were defined based on the fishing strategy and effort unit (Table  3 ), including also a file for outliers (S20). Finally, 10 other fisheries were defined for the other-gears group (fishery O21-O30). Nine types of gear were assigned in separate fisheries (O21-O29). The last fishery (O30) is for non-corrected outliers (Table  3 ).
Catch of the Asian longline and European purse seine fleets The detailed and careful analysis of IOTC bigeye fishing data has shown several inconsistencies that we tried to resolve in the best possible way. Given the importance of Asian longline fleets in the history of bigeye tuna exploitation and the extensive series of geo-referenced catch subsamples declared in number of individual fish for Japanese and Korean longline fleets, we decided to provide both original data in num-bers and a conversion in weight raised to the nominal catch level. Unfortunately, length-frequency data did not cover the whole period of the Japanese and Korean catch, leading to application of less accurate single weight conversion for a short period, e. g. , in 1952-1964, of the Japanese LL. Nevertheless, before submitting its national annual fishing statistics to the IOTC, the Japanese National Research Institute of Far Seas Fisheries (NRIFSF) applies a raising procedure to provide geo-referenced data consistent with the nominal catch declaration  (Matsumoto et al.
Among the largest anomalies detected with this filtering method, there is a high peak of CPUE in 2003 for the Taiwanese longline fleet that has been already identified in previous analyses and potentially linked to misreporting of logbook data that occurred among the Taiwanese fleets operating in the Pacific, Atlantic and Indian oceans (see  Hoyle et al. , 2015). Unusually high CPUEs observed in the Spanish and French log-associated purse seine sets were detected in 1999. Since this was observed in both fleets, it is likely that this particular year was effectively highly fa-vorable. Despite a threshold value set to 5 in the Hampel identifier method for these purse seiner data, a substantial number of effort data were classified as outliers and corrected. It is possible that these high peaks in CPUE variability reflect some heterogeneity in the fleets, e.
Results Raw data. In total, we obtained 288,151 sequences that were assigned to individual ticks and deposited in the Sequence Read Archive (SRA) under the accession number SRP041191. Of those sequences, a total of 75,973 were assigned to the rplB gene and 144,604 were assigned to the ospC gene. For the 488 nymphs found to be infected, we obtained a mean of 155. 7 rplB sequences and 296. 3 ospC sequences per individual tick. Overall, we obtained sequences from 472 different nymphs; only a few individuals did not yield sequence data. We also used 16,222 sequences obtained from small mammals that were deposited in the SRA under the accession number SRP032755. A mean of 33. 2 rplB sequences and 37. 9 ospC sequences were obtained from each individual host  17.
Genotype delineation. Using the nearest-neighbor classification algorithm, 39 and 82 genotypes were delineated from a total of 83,266 rplB sequences and 152,807 ospC sequences, respectively. We realized rarefaction analyses based on the resampling of raw sequences, for which sequencing efforts were comparable for ticks and hosts at the whole-dataset scale. In these analyses, we therefore considered a much less important sequencing effort for ticks than what we actually obtained. The results revealed certain patterns. First, the relationship between the number of delineated genotypes and the number of sequences used quickly plateaued, which illustrated that we had captured most of the diversity of the species complex (Supplementary Fig. S3A ). More importantly, when a high number of sequences were used, we did not observe a decrease in the number of delineated genotypes, which might have resulted if there had been clustering of divergent sequences due to an accumulation of sequencing errors.