dataesr/scientific-paragraphs-categorization huggingface dataset:
AAA patients and controls Plasma samples were obtained from individuals participating in the VIVA study (ClinicalTrials. gov NCT00662480), a population-based image-screening trial for AAA in men aged between 64 and 74 years old from Denmark. The study recruitment protocol has been reported in detail elsewhere  [36, 37]. In brief, 50,156 male adults were randomized between October 2008 and October 2010 to receive either an invitation for vascular screening or participate as control subjects. The study protocol was performed in compliance with the Declaration of Helsinki and received approval from the Institutional Review Board from the Mid Region of Denmark (M20080028). All participants gave written informed consent prior enrolment. Study participants underwent an ankle-brachial index (ABI) measurement and an ultrasound scan of the abdominal aorta.
Global trends in the occurrence, toxicity and risk posed by harmful algal blooms to natural systems, human health and coastal economies are poorly constrained, but are widely thought to be increasing due to climate change and nutrient pollution. Here, we conduct a statistical analysis on a global dataset extracted from the Harmful Algae Event Database and Ocean Biodiversity Information System for the period 1985-2018 to investigate temporal trends in the frequency and distribution of marine harmful algal blooms. We find no uniform global trend in the number of harmful algal events and their distribution over time, once data were adjusted for regional variations in monitoring effort. Varying and contrasting regional trends were driven by differences in bloom species, type and emergent impacts.
The occurrences of harmful microalgal species are now routinely recorded in the Ocean Biodiversity Information System, OBIS (https:// www. obis. org), a global database on the diversity, distribution and abundance of all marine organisms. Since 1985, reports of harmful algal events impacting on human society have been collated in the Harmful Algae Event Database, HAEDAT (http:// haedat. iode. org). HABs are by no means a new phenomenon but have been reported since historic times. Japanese archives reported coastal 'red waters' ('akashiwo') as early as the year 731 (putatively by the dinoflagellate Noctiluca scintillans  3  ). Spanish explorers in the 1500 s described 'red water' (now presumed to be due to the dinoflagellate Karenia brevis) from the Gulf of Mexico  4  ; Captain Vancouver lost a crew member in 1793 after consumption of blue mussels from Poison Cove, Southeast Alaska, USA, contaminated by PST from the dinoflagellate Alexandrium catenella  5  ; in 1774 Captain Cook and his crew suffered from the symptoms of Ciguatera Poisoning (CP) after consumption of red bass from the New Hebrides (Vanuatu) (now known to be due to species of the dinoflagellate Gambierdiscus)  6  ; Cattle deaths from cyanobacterial toxins (from the brackish water Nodularia spumigena) have been known from Lake Alexandrina, Australia, since 1878  7.
A quantitative global assessment of the present status of phenomena related to harmful algae is long overdue, but has not been achieved previously because data on the presence and abundance of harmful species and the trends of HABs were scattered in the increasing literature. Such knowledge-based standpoint on the trends and distribution patterns of harmful species and events is fundamental to enable effective management of HABs at a time that some 3. 3 billion people rely on seafood for a fifth of their animal protein and with terrestrial systems becoming less hospitable for traditional agriculture (Food and Agricultural Organisation, FAO  18  ). We here mine the OBIS database to scrutinise trends in phytoplankton and HAB monitoring efforts, and compare them with trends in HAEDAT events impacting human society.
Methods We explored the following three databases: IOC-UNESCO Taxonomic Reference List of Harmful Microalgae 2 ; HABMAP-OBIS: Database on the geographic range of Harmful Species (https://www. obis. org); and notably HAEDAT, the Harmful Algal Event Database (http://haedat. iode. org). HAEDAT is the only existing database holding information about harmful algal events from across the globe, though the coverage of different regions varies substantially (Supplementary Table  1 ). HAEDAT data are classified into 'events' that lead to management actions or cause any negative economic impact or have ecological consequences of variable magnitude. A harmful algal event can include (i) water discoloration, scum or foam with socio-economic impact either due to the presence of toxic or non-toxic microalgae, (ii) biotoxin accumulation in various seafoods exceeding levels safe for human consumption, (iii) harmful algae abundance resulting in shellfish or other invertebrate harvesting ban or a beach closure as a precautionary measure to protect human health and (iv) any event where humans, animals or other organisms are negatively impacted by algae, either with or without human health consequences.
HAEDAT does not hold records when there are no harmful events or no monitoring activities. Not all HAEDAT events have equal societal impacts. This study excluded freshwater harmful algae and macrophyte (seaweed) HABs. Global HAEDAT events were partitioned into 12 OBIS provinces (Fig. 1A ): 1. East Coast USA/Canada, Greenland (abbreviated as ECA), 2. Florida, Caribbean, Central America, Venezuela (CCA), 3. South America (SAM), 4. West Coast USA/ Canada, Alaska (WCA), 5. Australia/New Zealand (ANZ), 6. South East Asia (SEA), 7. North Asia (NAS), 8. Indian Ocean (IND), 9. Benguela (BENG, South Africa and Namibia), 10. Mediterranean (MED), 11. West/North Europe including Scandinavia (EUR) and 12. Pacific/Oceania (PAC). Each region was assigned a regional editorial team tasked with collating information on HAB species occurrences and impacts, with the data and interpretations published in a special issue of the journal Harmful Algae (vol.
102, February 2021)  [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30]. As more sampling could lead to more observations of HABs, we estimated sampling effort using OBIS, the most comprehensive dataset on marine species records, as a proxy for sampling effort. The following approach was utilised to estimate sampling effort from OBIS data. A total of 5,944,392 microalgal records were extracted from OBIS, including 289,668 distribution records of harmful species as defined by the IOC-UNESCO Taxonomic Reference list of Harmful Microalgae. OBIS records of microalgal species occurrence were matched with the HAEDAT regions using shapefiles (https://github. com/iobis/hab-regions) and a hexagonal discrete global grid constructed using https://github. com/r-barnes/dggridR. Within each region and each month, the number of grid cells with phytoplankton records was counted, and summed per year to obtain an estimate of sampling effort for that year.
The third meta-analysis also tried to correct for some of the potential bias associated with sampling effort by using the OBIS data on phytoplankton sampling. This was to adjust for the potential increase in sampling effort that could lead to a perceived increase in HAEDAT events. We used the ratio of the number of HAEDAT events to OBIS microalgal sampling over years to assess whether standardised HAB events might be increasing. Finally, the fourth meta-analysis tested the hypothesis that HAEDAT events were increasing as aquaculture has expanded. Our index of aquaculture intensity was the total tonnage of aquaculture produced seafood including fish, molluscs, crustaceans and aquatic plants in a region each year. Suitable aquaculture production data from FAO  18  , (https://ourworldindata. org/grapher/aquaculture-farmed-fish-production) were available for ANZ, SEA, MED and for the combined regions of ECA and WCA, and SAM and CCA (Supplementary Table  2 ).
Results and discussion The global database. As of 10 December 2019 a total of 9503 HAEDAT events had been entered from across the globe, comprising 48% seafood biotoxin, 43% high phytoplankton counts and/or water discolorations causing a socio-economic impact, 7% mass animal or plant mortalities and 2% others (including foam and mucilage production). In a number of HAEDAT records, a single incident was categorised into multiple event types, such as both water discoloration and high phytoplankton count (11% Fig. 2  Changes in different geographic regions of total number of HAEDAT events, and regional and global meta-analyses of HAEDAT events and HAEDAT geographic grids against year. A Changes in nine geographic regions in the period 1985-2018 in annual HAEDAT events (algal blooms impacting on human society); 95% confidence intervals are shown in grey; B Meta-analysis of relationship between number of HAEDAT events and Year; and C Meta-analysis of relationship between number of geographic grids with 1 or more HAEDAT events and Year.
AST has also been associated with marine mammal calf mortalities in Argentina  37. Of the global total of 3800 human Paralytic Shellfish Poisonings during the 1985-2018 period (Fig. 5I ), the largest number occurred in the Philippines, a country strongly dependent on aquaculture for human food protein, with 2555 poisonings recorded between 1983 and 2013 of which 165 were fatalities  23, 38  , predominantly caused by highly toxic Pyrodinium bahamense. Because of the increased use of molecular detection methods our knowledge on the global distribution of ciguateracausing organisms, selected species of the benthic dinoflagellates Gambierdiscus and Fukuyoa, has increased considerably (Fig. 6A ). The database on the presence of ciguatoxins in fish (Fig. 6B ) is still limited because of the complexity of the chemical analysis used to confirm the presence of ciguatoxins.
Climate change thus adds a new level of uncertainty to seafood security and safety for an ever-growing human population. Being at the crossroad of several different societal and scientific issues, questions on HABs are best addressed on a species-byspecies and site-by-site basis, and considering the respective impacts on local human activities, rather than handled as aggregates of microalgal HAB cell or species numbers, phycotoxin concentrations, or their global distributions. This is only the first of hopefully many future metadata analyses of the available HAB information, which has clearly shown the potential of the HAE-DAT and OBIS data. However, the expansion and integration of these databases  52  with other information are needed to capture the ecology of individual noxious species, explain their trends and quantify their economic and health impacts to society.
Acknowledgements This work formed part of the  Global Harmful Algae Status Reporting (GHSR) initiative , conducted under the auspices of  IOC (Intergovernmental Oceanographic Commission of UNESCO) ,  ICES  ( International Council for the Exploration of the Sea) ,  PICES  ( North Pacific Marine Science Organisation) ,  IAEA  ( International Atomic Energy Agency) , and was sponsored by the  Government of Flanders (Belgium).
Data availability All individual OBIS and HAEDAT event data used in the present analysis are available via https://www. obis. org and http://haedat. iode. org, respectively. The FAO aquaculture production data are available from https://ourworldindata. org/grapher/aquaculturefarmed-fish-production. The full collated HAEDAT, OBIS and FAO dataset used for the present 1985-2018 meta-analyses are available from https://github. com/iobis/paper-habtrends. The results of the statistical analyses on these data sets are provided as.
d. , Fig. 1 ) and regional gas transfer coefficient (k) data. We obtained river and lake water surface areas in the Ob' River basin and two other major Western Siberian rivers (Pur and Taz) from published databases  1, 2  and upscaled rivers and lakes C emission rates within each permafrost zone. Because the databases did not cover water surface areas of streams (lotic systems < 90 m wide) and the smallest ponds (lentic systems < 0. 01 km 2 ), we estimated these areas using Pareto law  1, 30, 31  and upscaled streams and ponds C emission rates to the extrapolated areas. We summed C emission from all inland waters (rivers, streams, lakes, ponds) and assessed uncertainties using standard error propagation methods. The results allowed us to make the first assessment of the role of inland waters in the C cycle in one of the least studied but largest northern ecosystem in the world undergoing rapid permafrost thaw.
Likewise, total C emission from Western Siberian inland waters is ~4. 2-fold greater than total inland water C emission from the permafrost-affected Yukon River (0. 02 Pg C yr -1 ) derived based on field observations  14. These comparisons emphasize not only the fact that C emission from Western Siberian inland waters is high, but also highlight the need for additional regional estimates of inland water C emission from other major watersheds to better constrain their role in the global C cycle. The results of this study are based on an extensive dataset with direct measurements of C emission rates covering a wide range of river and lake sizes and spanning over a complete permafrost gradient of Western Siberia. Yet, our estimate of total C emission from Western Siberian inland waters contains uncertainties related to practical constraints in carrying out measurements of C outgassing rates at higher spatial and temporal resolution.
Methods Inland water area estimates. We used available Global River Widths from Landsat (GRWL)  1  as well as Global Water Bodies (GLOWABO)  2  databases to estimate river and lake area in the Ob', Pur, and Taz River basins. We first clipped the databases' files to the respective area of Western Siberia using ArcMap 10. 5. Then we overlaid GRWL river network with Ob' main channel mask derived from World Major Rivers file (https://www. arcgis. com/home/item. html?id=44e8358cf83a4b43bc863646cd695945) by selecting features that are within ~20 km distance from the Ob' main channel mask, and clipped Ob' main channel from GRWL river network. We further separated the Ob' main channel, river, and lake files to the respective permafrost zones using shapefiles of the Circum-Arctic Map of Permafrost and Ground Ice Conditions (http://nsidc.
org/ data/docs/fgdc/ggd318_map_circumarctic/). After that we merged the Ob' main channel, river, and lake files for each permafrost zone in R (version 3. 5), making three individual continuous spatial datasets with all permafrost zones present (Ob' main channel spatial dataset consisting of 201,874 observations of river area, river spatial dataset of 882,124 observations of river area, and lake spatial dataset of 973,780 lakes). We excluded rivers <90 m wide from both river and Ob' main channel datasets as done in Allen et al. 1. To include rivers and streams <90 m not present in the GRWL database, we estimated their surface area using Pareto extrapolation based on the specific Pareto shape parameter of 0. 93 ± 0. 0004 (±s. d. ) reported for the rivers of Ob' River basin  1  and the minimum width of the first-order streams of 0.
32 ± 0. 077m  49. Similarly, to include ponds <0. 01 km 2 not present in the GLOWABO database we used Pareto extrapolation based on the specific Pareto shape parameter of 1. 19 ± 0. 0004 for the lakes of Ob' River basin (obtained by fitting power law to Ob' basin lakes in GLOWABO) and the smallest measured pond area of 0. 000115 ± 0. 0001 km 2 (based on our field observations). There exist no data that enable to incorporate temporal variability in surface area across all systems and full region, and all areal estimates are assumed to represent average conditions over the open water season. Since it has recently been suggested that contrary to stream area, pond area distribution in the landscape does not follow Pareto law  31, 38  , we also quantified pond area using the fraction of land covered by these types of water bodies for each of the permafrost zones derived from lake satellite inventories of few sites within specific permafrost zones of the region from Muster et al.
We also estimated season length across entire region of Western Siberia by using a linear relationship between latitude (°N) and number of ice-free season days published for rivers  27  and lakes  28. C emission from Ob' main channel. The pCO 2 of Ob' main channel was collected at 0. 5 m depth every minute during 5 min at 10-min (yielding 4938 measurements) from a ship in summer 2016 (31 July to 11 August) by an infrared gas analyzer (Vaisala GMP222; accuracy ±1. 5%) connected to a Campbell logger. The probe was calibrated in the lab and the pCO 2 data were corrected for pressure and temperature (collected at same frequency) as described in Serikova et al. 27. We estimated molar concentrations of pCO 2 in water and in water in equilibrium with the atmosphere using Henry's constant and pressure, and the average atmospheric concentration of 390 ppm.
The pH values for the respective permafrost zones were derived from our published river seasonal data  27  , truncating pH values to <8 because observed seasonal river pH values never exceeded a pH value of 8  27. Thus, CO 2 emissions were calculated as: CO 2 emission rate ¼ α median k ð Þ pCO 2 water À pCO 2 atmosphere 10 À6 ð1Þ where α is the pH-dependent chemical enhancement factor of CO 2  50  , k is a median gas transfer coefficient of 4. 464 m d -1 measured in four largest rivers (June 2015) of the Ob', Pur, Pyakupur, and Taz Rivers (n = 39, each consisting of multiple measurements with floating chamber drifting in the middle of the river channel for 5 min), pCO 2 water is assigned pCO 2 in the water and pCO 2 atmosphere is the assigned pCO 2 in water in equilibrium with the atmosphere.
19% and summed up CO 2 and CH 4 emission rates to get the C (CO 2 + CH 4 ) emission rate (Supplementary Fig. 2 ). After that we multiplied C emission rate with respective season length and water areas for each of the data points, and summed all points together. We also added land area to different permafrost zones based on Circum-Arctic Map of Permafrost and Ground Ice Conditions 51 and estimated C yield for each of the data points by normalizing the C emission rate to the respective land area. C emission from rivers and streams. To quantify C emission for rivers, we used published data on daily rates of CO 2 outgassing from 58 rivers (n = 116)  27. We created five normal distributions with CO 2 emission rates for each permafrost zones with 10,000 values in each using the mean and s. d. for the respective permafrost zones from the observed CO 2 emission rates river data (Supplementary Table  1 ).
Then, for each observation of river area (n = 882,124), we randomly assigned a CO 2 emission rate by subsampling the permafrost-specific distribution of emission rates defined above. After that we estimated daily rates of CH 4 (i. e. , 1. 19% of total C emission) and C emission using the same approach as for Ob' main channel. Then we estimated season length for each data point using the linear regression with latitude (R 2 = 0. 99, F 1,114 = 7899. 51, p < 0. 01, Supplementary Table  4 ), and multiplied daily C emission rate with season length and water areas for each of the data points. Finally, we summed all points together to get the total C emission for rivers. We also added land area to different permafrost zones following the same approach as for the Ob' main channel and estimated C yield.
We quantified C emission for streams using the published  27  median C emission rate of 5. 67 g C m -2 d -1 for watersheds <100 km 2 (assuming the same 1. 19% fraction of CH 4 in C emission rate). We then multiplied this value with extrapolated stream area and a median season length of 180. 6 days was observed in our river data  27. C emission from lakes and ponds. For permafrost-affected lakes we used published lake C (CO 2 + diffusive CH 4 ) emission rates data (76 lakes, n = 228, Supplementary Fig. 3 )  28. Since we did not observe linear dependence of C emission rates with lake size (n = 182, R 2 = 0. 00, F 1,179 = 0. 598, p > 0. 05), we utilized a similar approach for upscaling C emission rates as in river upscaling. We created five normal distributions with C emission rates data representing different permafrost zones with 10,000 values in each using mean and s.
d. for the respective permafrost zones from lake data (Supplementary Table  1 )  28. Then, for each observation (n = 612,003), we randomly assigned a CO 2 emission rate by subsampling the permafrost-specific distribution of emission rates defined above. Then we estimated season length for each data point using the linear regression with latitude (R 2 = 0. 96, F 1226 = 6012. 09, p < 0. 01, Supplementary Table  1 ), and multiplied daily C emission rate with season length and water areas for each of the data points. Finally, we summed all points together to get the total C emission for permafrost-affected lakes. We also estimated C yield following methods described above. When quantifying C emission for lakes in the permafrost-free zone, we used published C emission rates from 13 permafrost-free lakes (n = 13)  29.
Considering that the linear dependence of C (CO 2 + diffusive CH 4 ) emission rates on lake size in permafrost-free area was very weak (log 10 -transformed, n = 13, R 2 = 0. 20, F 1,11 = 2. 801, p > 0. 05), we used the median C emission rate of 0. 6 g C m -2 d -1 when upscaling to lakes (n = 361,777) located in permafrost-free area of Western Siberia (using same approach as described above for permafrost-affected lakes). We also estimated C yield using the same approach as above. We quantified C emission for ponds using the published median C emission rate for permafrost-affected (1. 12 g C m -2 d -1 ) and permafrost-free (0. 6 g C m -2 d -1 ) smallest size class lakes of Western Siberia, and multiplied this with extrapolated pond area and median season length for respective region. Since it has been recently suggested that pond area distribution in the landscape does not follow Pareto law  31, 38  , we also quantified C emission for ponds using the fraction of land covered by these water bodies types for each of the permafrost zone derived from dataset available in Muster et al.
Note that our sampling campaign on lakes was conducted during open water period (May-October) 2016 and the Ob River was studied in July-August 2016. We imported these data files to R, extracted the mean NEE rate as well as 1 s. d. of the mean NEE rate data from these data files (representing in total 730 individual data of h5 format), projected them, converted them to GeoTiff format, and clipped the area matching the location of Ob', Pur, and Taz River basins (Supplementary Fig. 1 ). We then quantified the mean NEE rate and mean s. d. across all 71,280 of 9 × 9 km cells covering the region, for each day separately, and after that we estimated the NEE for entire Western Siberia as a sum of products of each 71,280 individual cells' NEE rates and respective cells' resolution (also for each day separately).
The monthly NEE and annual NEE were quantified as the sum of daily and monthly NEE, respectively (Supplementary Table  2 ). We assessed uncertainty in monthly and annual NEE using Eqs. (  2 ) and (3). Net C uptake by the Kara Sea. To estimate net C uptake by the Kara Sea, we used published grid (1 × 1 degree) data for CO 2 uptake by the Arctic Ocean (60-90°N, 0°-360°) for year 2014  43, 44  (http://www. jamstec. go. jp/res/ress/yasunaka/co2flux/#! prettyPhoto). We downloaded the data file, imported it to R, and clipped the grid cells matching the GPS boundaries of the Kara Sea (2784 observations). We then quantified the mean annual CO 2 uptake rate encoded in these grid cells (-7. 64 mmol m -2 d -1 , from January to December) and multiplied it by the water surface area of the Kara Sea and by 365 days (as done in Yasunaka et al.