dataesr/scientific-paragraphs-categorization huggingface dataset:
A preliminary analysis suggested that earthworms are more abundant in younger soils, whereas termites dominate in older soils, and ants are abundant in the youngest and oldest soils  (Orgiazzi et al. , 2016). The amount of variance explained on each axis by individual environmental drivers is limited in all cases, although highly significant. The highest value recorded for plant cover (12. 3% of variance explained) probably expresses the strong overall effect of soil management, because >60% of the samples contained in the data base were derived from agricultural environments. Relationships with elevation (8. 9%), latitude (5. 3%), soil texture (3. 9%) and temperature (2. 3%) were also significant, although we do not yet know whether the differences found among these environmental factors might also be significant, allowing them to be classified by their importance.
The conversion of the data into discrete classes made it possible to homogenize data obtained from very diverse sources, those directly measured with great precision on site or those, less precise, drawn from cartographic documents. This approach is consistent with the global scale considered in the present study, which measures all variables at relatively general levels, such as orders for invertebrates or large latitudinal areas. It allows the use of robust statistical tools. Eighteen variables recorded the properties of the macroinvertebrate community: taxonomic richness (the number of taxa found at the site); total density per square metre; and density per square metre of earthworms, ants, termites, Coleoptera, Chilopoda, F I G U R E 1 Top panel, distribution of the 3,694 sampling sites of the Macrofauna database; bottom panel, number of records per country Diplopoda, Isopoda, Diptera, Blattaria, Hemiptera, Dermaptera, Lepidoptera, Orthoptera, Gastropoda, Arachnida and other groups.
Projection of (a) macroinvertebrate group densities and community parameters and (b) sites grouped in five clusters in a plane defined by factors 1 and 2 (F1 and F2) of a principal components analysis of macroinvertebrate community data of the Macrofauna database. ANT, ants; ARAC, Arachnida; BLA, Blattaria; CHILO, Chilopoda; COL, Coleoptera; DEN, total density per square metre; DERM, Dermaptera; DIP, Diptera larvae; DIPLO, Diplopoda; EWM, earthworms; GAST, Gastropoda; HEMI, Homoptera Hemiptera; ISO, Isopoda; MYR, Myriapoda; ORTH, Orthoptera; OTH, other; RT, taxonomic richness (number of orders found in the sampling); TER, termites F I G U R E 3 Left panel, taxonomic richness and right panel, composition of communities in the five clusters separated from the Macrofauna database All clusters comprised plant covers of all categories, although in different proportions, with relatively higher representation of temperate forests and grasslands in clusters 1-3 and tree-associated tropical crops and tropical forests in clusters 4 and 5 (Figure S4).
Temperate and tropical grasslands and tropical tree-associated crops still showed rather high values, >60, F I G U R E 4 Compositions of macroinvertebrate communities in different types of plant cover considered in the 3,964 sites of the Macrofauna database whereas temperate forests had a rather low value of 37. Minimum values were observed in temperate and tropical annual crops, with 28 and 29 significant interactions, respectively. Positive interactions prevailed except in temperate forests and annual crops, with maximum values in tropical forests (59), tropical pastures and temperate dry forests and shrubs (47) and temperate grasslands (41), whereas the largest occurrences of negative interactions (26) were observed in tropical forests and temperate grasslands and tropical tree-associated systems (24).
I G U R E 5 Respective coordinates of the 15 most influential environmental variables (of a total of five and their 41 modalities; left) and macroinvertebrate groupings (right) on axis 1 of a coinertia analysis among environmental and macroinvertebrate groupings of the 3,964 sites of the Macrofauna database. Groupings from both sides that have similar coordinates along factorial axes exhibit maximum covariation the separation. However, the large number of different projects mostly led by people long trained in the method led us to expect a rather homogeneous level of accuracy. Invertebrates from so many different groups are not all captured with the same accuracy owing to differences in colour, mobility and size. As a result, the data obtained should be considered accurate indicators of the state of communities, not exact evaluations of the densities of all individual groups.
files and indicator taxa by farmers is another priority goal for research. Such tools developed by scientists and other fieldworkers will allow measurement of the different ecosystem services provided by soils and the use of this knowledge to sustain novel public policies and thereby support the next generation of sustainable agricultural practices. Beyond these immediate practical issues, the Macrofauna database will allow the inclusion of macroinvertebrate communities in global to local comparisons of distribution patterns of different groups of organisms, soil-dwelling microorganisms, micro-and mesofauna and plants. Hence, it is expected that different patterns will appear in the relative importances and functional profiles of the groups, determined by environmental conditions. Although general climatic conditions are always expected to predominate, general soil chemical and physical fertility and the quality of organic resources provided by plants should be important modifiers in final patterns.
F Projection of (a) macroinvertebrate group densities and community parameters and (b) sites grouped in five clusters in a plane defined by factors 1 and 2 (F1 and F2) of a principal components analysis of macroinvertebrate community data of the Macrofauna database. ANT, ants; ARAC, Arachnida; BLA, Blattaria; CHILO, Chilopoda; COL, Coleoptera; DEN, total density per square metre; DERM, Dermaptera; DIP, Diptera larvae; DIPLO, Diplopoda; EWM, earthworms; GAST, Gastropoda; HEMI, Homoptera Hemiptera; ISO, Isopoda; MYR, Myriapoda; ORTH, Orthoptera; OTH, other; RT, taxonomic richness (number of orders found in the sampling); TER, termites F I G U R E 3 Left panel, taxonomic richness and right panel, composition of communities in the five clusters separated from the Macrofauna database All clusters comprised plant covers of all categories, although in different proportions, with relatively higher representation of temperate forests and grasslands in clusters 1-3 and tree-associated tropical crops and tropical forests in clusters 4 and 5 (Figure S4).
F I G U R E 5 Respective coordinates of the 15 most influential environmental variables (of a total of five and their 41 modalities; left) and macroinvertebrate groupings (right) on axis 1 of a coinertia analysis among environmental and macroinvertebrate groupings of the 3,964 sites of the Macrofauna database. Groupings from both sides that have similar coordinates along factorial axes exhibit maximum covariation the separation. However, the large number of different projects mostly led by people long trained in the method led us to expect a rather homogeneous level of accuracy. Invertebrates from so many different groups are not all captured with the same accuracy owing to differences in colour, mobility and size. As a result, the data obtained should be considered accurate indicators of the state of communities, not exact evaluations of the densities of all individual groups.
Taxonomic grouping Frequency (%) Arithmetic (mean ± SE) Median First quartile Third quartile Skewness coefficient Kurtosis coefficient Density and general statistical parameters of the main macroinvertebrate groups in the 3,694 sites represented in the Macrofauna database Earthworms 70. 7 123 ± 3. 8 29. 3 0. 0 157. 5 3. 6 18. 5 Ants 53. 2 303 ± 29. 3 6. 9 0. 0 99. 0 17. 5 397. 9 Termites 26. 4 187 ± 15. 2 0. 0 0. 0 3. 0 13. 0 254. 1 Coleoptera 65. 1 50 ± 1. 7 14. 8 0. 0 62. 1 8. 2 125. 8 Chilopoda 35. 8 13 ± 0. 6 0. 0 0. 0 14. 8 6. 6 60. 6 Diplopoda 33. 1 21 ± 2. 0 0. 0 0. 0 11. 6 17. 5 419. 9 Isopoda 19. 6 14 ± 1. 7 0. 0 0. 0 0. 0 16. 3 359. 6 Diptera 26. 9 8 ± 0. 5 0. 0 0. 0 2. 2 11. 9 214. 3 Blattaria 13. 6 2. 3 ± 0. 2 0. 0 0. 0 0. 0 15. 5 342. 8 Hemiptera 19. 1 3. 2 ± 0. 2 0. 0 0. 0 0.
)database. The index assesses whether each country has policies and practices in place to promote upward social mobility across its population. The index was calculated based on 10 pillars relating to social mobility (e. g. , access to health, education) which was scored from 0 to 100. The final index is the average score across the 10 pillars. Higher score on the index means the country is moving towards greater social mobility whereby more equal opportunities are available for people in the country. Scores in our sample range from 36. 00 to 82. 40 (please refer to World Economic Forum, 2020, for a list of indices for individual country). Legitimacy. The degree of status legitimacy in each society was measured using two indexes: (a) the Corruption Perception Index, and (b) the Democracy Index.
ACKNOWLEDGMENT This research was supported by an  Australian Research Council   Discovery grant  ( DP170101008 ), by the  Centre for Social Conflict and Cohesion Studies  ( ANID/FONDAL 15130009 ), by the  Center for Intercultural and Indigenous Research (ANID/FONDAP  # 15110006 ), and a  JSPS   KAKENHI grant  ( 19KK0063 ).
, 2012 )  were used to assess anxiety and suicidality respectively. The data were collected online and anonymously from April 2020 through March 2021, covering periods of full implementation of lockdowns as well as of relaxations of measures in countries around the world. Announcements and advertisements were done in the social media and through news sites, but no other organized effort had been undertaken. The first page included a declaration of consent which everybody accepted by continuing with the participation. Approval was initially given by the Ethics Committee of the Faculty of Medicine, Aristotle University of Thessaloniki, Greece and locally concerning each participating country.
Material The study sample included data from 40 countries ( Fig. 1  ) concerning 55,589 responses (64. 85% females; 34. 05% males; 1. 10% other) to the online questionnaire. The contribution of each country and the gender and age composition are shown in Table  1. Details concerning various sociodemographic variables (marital status, education, work etc. are shown in the webappendix, in webTables 1-9). The study population was self-selected. It was not possible to apply post-stratification on the sample as it was done in a previous study  ( Fountoulakis et al. , 2021 ) , because this would mean that we would utilize a similar methodology across much different countries and the population data needed were not available for all.
Strengths and limitations The strengths of the current paper include the large number of persons who filled the questionnaire and the large bulk of information obtained, as well as the detailed way of poststratification of the study sample. The major limitation was that the data were obtained anonymously online through self-selection of the responders. Additionally, the assessment included only the crosssectional application of self-report scales, although the advanced algorithm used for the diagnosis of probable depression corrected the problem to a certain degree. However, what is included under the umbrella of 'probable depression' in the stressful times of the pandemic remains a matter of debate. Also, the lack of baseline data concerning the mental health of a similar study sample before the pandemic is also a problem.
Methods Participants. Thirty-three healthy undergraduate students participated in the experiment. They were attendees of a non-compulsory university course where course credits could be obtained by participating in scientific experiments and were randomly assigned to the present study. The sample size was determined based on previous studies using similar experimental tasks in within-subject designs  23, 24  (for details, see the "Estimation of required sample size" section in Supplementary Information). Participants had normal or corrected-to-normal vision. None of them reported a history of any psychiatric or neurological condition, or substance use. One participant dropped out of the experiment due to technical errors during data collection. Another participant was excluded due to consistent outlier performance (± 2 SDs) on RT measures throughout the experiment.
Acknowledgements This research was supported by the  National Brain Research Program  ( 2017-1. 2. 1-NKP-2017-00002  to D. N. );  Hungarian Scientific Research Fund  ( NKFIH OTKA PD 124148  to  K. J. ,  NKFIH OTKA K 128016  to D. N. ),  Janos Bolyai Research Fellowship of the Hungarian Academy of Sciences  (to K. J. ), the  ÚNKP-20-3 New National Excellence Program of the Ministry for Innovation and Technology  from the source of the  National Research, Development and Innovation Fund  (to K. H. ), and the  IDEXLYON Fellowship of the University of Lyon  as part of the  Programme Investissements d' Avenir  ( ANR-16-IDEX-0005  to D. N. ). The authors thank  Anna Guttengéber ,  Ábel Gergely ,  Kirstóf Mikó ,  Péter Solymosi ,  Lilla Petrencsik  and  Dorottya Szily  for their help in data collection.
C opper (Cu) is a widely studied trace metal due to its importance for various biological functions  1. It also plays a role as a toxicant when present in high concentrations of the unbound ion, Cu  2+ 2-4. The cycling of dissolved Cu (Cu) in the oceans, estuaries and rivers has been relatively well-studied over recent years  [5] [6] [7] [8] [9] [10] [11] [12] [13]. Recent knowledge acquired largely thanks to the international GEOTRACES program (https://www. geotraces. org) and new biogeochemical models has allowed us to gain fresh insights into natural and anthropogenic Cu sources as well as removal processes  14, 15. However, despite growing datasets on dissolved Cu in seawater and the development of some of the first biogeochemical models for Cu  9, 12, 16, 17  , many of the internal cycling processes impacting global Cu distributions such as regeneration and scavenging are not well understood.
While much work is yet to be done, advances in laboratory experiments and a dramatic increase in Cu-binding ligand measurements in the ocean are beginning to shed light on the sources and sinks of these ligands  5, 7, 16, 23. Despite the recognition of the importance of ligands in dissolved iron (Fe) cycling, relatively little attention has been paid in the literature to Cu-binding ligands and their impact on the oceanic inventory of Cu. For example, it has been established that including a dynamic ligand cycle for dissolved Fe-binding ligands is essential for reproducing the measured in situ datasets of dissolved Fe profiles from GEOTRACES basin-scale sections  24, 25. However, a similar exercise has not yet been performed for Cu, despite the knowledge that the majority of dissolved Cu in seawater is associated with ligands.
Various other Cu-complexing compounds have also been identified in marine waters, such as domoic acid  70. Such specific compounds may be important to Cu bioavailability either on very localized scales or periodically, such as during mass feeding events or during large harmful algal bloom events (e. g. , domoic acid). Overall, we have only just begun to unravel the identities of Cu-binding ligands in seawater. Organic copper-binding ligands that have been observed to be produced by microbes in culture or in natural seawater. The "-" denotes that parameter was not determined. seawater. Then, we propose new operational definitions of Culigand classes based on measured stability constants and known model ligands, and call for a consensus on defining these ligand pools to facilitate comparisons across datasets and analyses in future studies.
The potential is then scanned in a negative direction and the reduction current of the Cu-AL complex is measured. This is repeated for each aliquot and the height of this peak is recorded and plotted against the concentration of dissolved Cu, ultimately producing the titration curve. This titration data is then interpreted using either linear  81, 85, 86  or non-linear  87  transformations to determine the ambient ligand concentration [L i ] (where i denotes ligand class) and binding strengths (K CuL i ;Cu 2þ or K CuL i ;Cu 0 ) of the natural Cubinding ligand pool. Conditional stability constants for these ligand classes are expressed as either K CuL i ;Cu 2þ or K CuL i ;Cu 0 , where K CuL i ;Cu 2þ = K CuL i ;Cu 0 × α Cu. The technique described above is known as a "forward" titration, but Nuester and van den Berg 88 describe a "reverse" titration in which the AL is spiked in increasing concentrations while the dissolved Cu remains constant.
Both the Cu(II)-IMAC method and the mass spectrometry methods that have been employed thus far to identify and characterize the organic Cu-binding ligand pool in seawater have the potential to rapidly accelerate our understanding of marine Cu complexation, but very few published studies exist  [95] [96] [97] [98] [99]. Importantly, new work also suggests that a large fraction of dissolved Cu (up to 90%) in deep waters may be inert, which has implications for these analytical methods as well as for dissolved Cu reactivity and bioavailability  91. Future work that aims to characterize Cu-binding ligands in the marine environment will be particularly insightful to our understanding of the marine Cu cycle. A call for an operational consensus on copper ligands. Although many methods have been used to characterize organic Cu speciation, we largely focused our discussion here based on lessons learned from voltammetry Cu speciation studies, which are the methods that have been employed for the bulk of the GEOTRACES studies and many other basin-scale datasets.
Advanc s in metal speciation data processing have facilitated high sample throughput leading to large-scale datasets of dissolved Cu speciation. Open-ocean data from the Pacific and Atlantic oceans shows patterns in Cu-binding ligand distributions that are similar to distributions of dissolved Cu, with low nanomolar concentrations near the surface and quasi-linear increasing concentrations with depth  5, 7, 8, 12, 16  (Fig. 3 ). The highest concentrations of Cu-binding ligands have been detected in the deep waters (>3000 m) from full depth ocean profiles, and near bottom sediments (concentrations up to 4-6 nmol L -1 ). In a comparison of Cu-binding ligand distributions between the Pacific and North Atlantic basins, higher concentrations of both dissolved Cu and Cu-binding ligands were observed on average in the Pacific basin.
Internal cycling. Despite recent ocean-basin-scale datasets on the distributions of Cu-binding ligands, we know very little about their internal cycling. A comparison of data from the North Atlantic  16  and the South Pacific 7 suggests that both dissolved Cu and Cu-binding ligands accumulate in the oldest waters of the deep Pacific, implying that ligands are likely produced along with dissolved Cu during regeneration of sinking particles  7, 16. Indeed, Fe and Cu-binding humic-like compounds were recently found to be directly produced during particle degradation  185. Deep profiles of Cu ligands from both the Atlantic and Pacific also suggest that Cu-binding ligands are scavenged onto sinking particles. Although Cu displays a nearly linear profile with depth indicative of an influence of reversible scavenging 9 , Cu-binding ligands do not have the same profile (Fig.
For instance, Sander and Koschinsky  22  and Stüeken  188  studied the impacts of hydrothermal sources on oceanic budgets of Cu-binding ligands. These models represent the thermodynamic mixing effects between hydrothermal fluids and the overlying water column. Both fluids have fixed Cu and ligand concentrations with fixed binding strength (logK CuL i ;Cu 2þ ). One fundamental hypothesis of these from available datasets for the Pacific and North Atlantic oceans  5, 7, 8, 12, 16, 247. models is that some ligands originate from hydrothermal vents, but that the majority are already present in the water column and that both ligand sources have similar complexing capacities. Using box models to integrate the results over the ocean, these studies revealed that 2-20% of total dissolved Cu in the modern ocean is supplied by hydrothermal vents and stabilized by organic ligands in the deep water column.
Several observations have shown that different phytoplankton species produce Cu ligands in conditions of Cu limitation or Cu toxicity (section "Microbial production of Cu-binding ligands"). However, quantitative estimates of Cu-ligand production rates are still missing. To our knowledge, Echeveste et al. 217  provided the only ligand production flux by the coccolithophore E. huxleyi (about 12. 5 fM cell -1 in high Cu conditions). Unfortunately, this estimate is difficult to generalize to the global ocean and more work on microbial ligand production would be necessary to quantify the magnitude of this source at the ocean scale. While it is likely that microbial ligand production is one of the dominant sources of ligands to the ocean, the current lack of rate measurements makes it impossible to estimate the value of this source given the current datasets.
Conclusions Organic Cu-binding ligands bind the majority of the dissolved Cu in the ocean and are recognized as being important for dissolved Cu cycling, and yet much remains to be discovered about their sources, sinks and internal cycling, and none of the existing Cu biogeochemical models include a dynamic cycle of Cu-binding ligands. Here, we summarized the current knowledge of Culigand cycling in the global ocean with a focus on large-scale open-ocean processes based largely on basin-scale GEOTRACES datasets and found that margin sediments and rivers are the major sources of Cu-binding ligands in seawater, and sedimentation, microbial uptake and photochemical degradation are the major sinks. Future studies that focus on understanding Cubinding ligand fluxes and identifying Cu-binding ligands in seawater will be particularly insightful for future modeling efforts, and for understanding the impact of Cu-binding ligands on Cu bioavailability to marine organisms.
Acknowledgements We would like to thank  Stan van den Berg ,  Kuo Hong Wong ,  Sylvia Sander  and one anonymous reviewers for their insightful comments on the manuscript. A. R. was funded by the  National Science Foundation Polar Programs  Postdoctoral Fellowship # 2138217 , R. M. B. by a  Life Sciences Simons Early Career Investigator in Marine Microbial Ecology Award  # 618401 , and C. R. was supported by  ISblue  project , Interdisciplinary graduate school for the blue planet  ( ANR-17-EURE-0015 ) and co-funded by a grant from the  French government  under the program " Investissements d'Avenir ", and by a grant from the  Regional Council of Brittany  ( SAD program ).
Acknowledgments The computations were performed using the Cactus cluster of the CIMENT infrastructure and the authors thank  Philippe Beys  who manages the cluster. We thank  Martin O. Lenz  from the  Cambridge Advanced Imaging Centre  for help with STED imaging. We thank  Bruno Monier  for the gift of the Sqh-GFP strain. Special thanks to  Claire Lye ,  Alexander Erlich  and Karin.
Funding: This work was supported by a  Wellcome Trust   Investigator Award  to BS ( 099234Z12Z  and  207553Z17Z ). ANB was also supported by a  University of Cambridge Herchel Smith Fund Postdoctoral Fellowship. AGF was supported by a  Vice-Chancellor's Fellowship  from the  University of Sheffield  and  BBSRC  grant  BB/R016925/1. All authors were additionally supported by a  PICS CNRS  travel grant and  ANR-11-LABX-0030 'Tec21 ' grant. The STED microscope was funded by  BBSRC  grant  BB/R000395/1. The Cactus cluster of the CIMENT infrastructure, where the computations were run, was supported by the  Rhone-Alpes region  (Grant  CPER07\_13 CIRA ) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.