dataesr/scientific-paragraphs-categorization huggingface dataset:
Apps and Games Apps on mobile devices are ubiquitous, and often within a person's reach 24-7. Using these to support augmented reality could also provide an interface to generate more detailed data with real-time diagnostics  [64] [65] [66]. In addition, apps that 'gamify' nature can motivate the public to interact with their environment, and thus provide more data on species and the environment. Overall, apps and games have the potential to transform how humans interact with nature (both positive and negative) and cause a fundamental shift in the quantity and quality of iEcology data.
Trends in Ecology & Evolution Trends Ecology & Evolution, July 2020, Vol. 35, No. 7 635 The vast majority of iEcology studies that we have identified have used multiple data sources to validate results, including data from field research, citizen science, online databases, scientific literature, or their combination  [12, 13, [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] 31, 32, 35, 43, 44]. In most cases, authors report a satisfying to excellent level of consistency among data sources. When ground truthing is difficult, as is often the case, other metrics could be developed to assess data robustness. We also strongly advocate cross-referencing results across multiple iEcology data sources, to test consistency of patterns  [54, 55]. Furthermore, culturomics can provide critical support to understand societal perceptions, interests, and values that affect the process of data generation  [8, 56].
Correct taxonomic identification in iEcology may be a cause for concern when compared with traditional ecological research. This may be true at several levelsfrom species misidentification by data producers to challenges that experts face when identifying species based on a limited number of images or videos of an individual organism. Furthermore, automated classification of species also generates misidentifications. Such embedded errors could also arise in other types of ecological data, such as life history traits, behavior, and abiotic variables. However, we expect that as iEcology sources increase in size, and methods to validate them improve, so will the ability to identify the extent and type of such problems in the data. Furthermore, we also suggest assigning a validity attribute to data that can be nonbinary, and dependent on the contributor's reputation and the likelihood of an observationas is currently practiced on some citizen-science platforms.
iEcology research would greatly benefit from collaborative efforts and sharing of data, resources, and tools. These could be aided by developing specific metadata standards for sharing such data, which could include APIs and specific machine-learning algorithms used to extract or manipulate the data. Such developments could draw from similar efforts that are already being carried out by big ecological databases (e. g. , www. gbif. org) to develop similar standards, which would make ecological data more interoperable  [57]. iEcology repositories could be either centralized or remain decentralized, with benefits associated with both options. Centralized repositories would greatly aid the maintenance of high standards, as well as providing better reproducibility, open access, and versioning. Nevertheless, necessary effort on pre-and postprocessing of data and metadata for uploading, and generally rigid structure of a centralized repository may actually deter people involved in more local, small-scale explorations, and may ultimately hinder data sharing and collaboration.
As iEcology is still in its infancy, there may be some advantages in its remaining decentralized and more flexible at this current stage. Yet, as the methods, tools, and associated data increase in breadth and scope, a move towards more collated, managed, and centralized repositories will become more natural and pertinent. Nevertheless, we advocate that good record keeping and maintaining high metadata standards is of particular importance to iEcology. Other considerations of iEcology data sources involve interpretation and reproducibility. Some sources lack transparency in the way the considered data were produced and manipulated (e. g. , search engines such as Google). Inability to publish raw data (as per provider guidelines) could also cause issues with scientific journal protocols that require making these available.
Furthermore, some sources lack stability in data scope, underlying algorithms, and access options. These are inherent issues with many online sources. To alleviate these concerns, we advocate: (i) good record keeping of protocols for data access, handling, versioning, and analysis; (ii) harmonization of methods  [58]  and standardization of metadata; (iii) publishing (when possible) raw data in freely accessible and stable repositories together with associated scripts; (iv) use of open-source data and software; and (v) keeping up to date with methodologies developed in other relevant fields (e. g. , computational sociology) for assessing and addressing such issues. iEcology research may give rise to several ethical issues, pertaining to both people and nature. Data shared online, especially on social media platforms, sometimes include explicit personal information, while implicit information could also be used to identify individuals or to extract sensitive information.
Concluding Remarks and Future Perspectives The field of ecology is undergoing a rapid shift towards indirect, technology-based, and automated observations of nature and biodiversity  [53, 61, 62] , where iEcology is likely to play a critical role. Utilization of iEcology methods and data has greatly expanded recently, with most publications appearing during the past few years. iEcology is likely to experience rapid development over the coming years and become one of the major research techniques in ecology. While classical biodiversity research is irreplaceable for understanding the natural world through targeted observations and experiments, iEcology could provide novel and low-cost support for ongoing research efforts. The value of iEcology is likely to greatly increase as the global coverage of the Internet, mobile computing, sensor networks, and their users, expand.
This will be augmented by leaps in computational capabilities, emergence of new data sources and types (such as odors, obtained by electronic noses)  [63] , and other emerging tools and technologies for using these data (Box 1). Combining these with other Big Data sources and efforts to understand nature, such as ecoinformatics, could also prove valuable. In the near future, we foresee complete automation of all stages of data handling within iEcology, from access to visualization, to creation of ever-expanding datasets of biological entities, traits, behaviors, etc. This could give rise to a global digital monitoring initiative for the natural world. For example, an ecologist interested in animal behavior could produce tools to automatically scrape all uploaded YouTube videos for animal representations, automatically analyze them for different types of behaviors, include these into a constantly updated dataset, and ultimately analyze them in real time, to produce continuously updated research outputs.
However, good expertise regarding the organisms studied and underlying ecological mechanisms at play will always be invaluable to make sense of these rapidly accumulating data and their inherent biases. Furthermore, many of the examples presented above demonstrate imagination and creativity in using data sources for ecology that were collected for other purposes. Above all, iEcology will benefit from such creativity to find new ways to harness data beyond their original purposes. iEcology provides fertile ground for interdisciplinary collaborations, enhanced by a wide range of expertise and specializations. Furthermore, iEcology will create new opportunities for partnerships between academia, industry, governmental, and non-governmental organizations, working synergistically to produce original insights into the natural world.
Outstanding Questions How do iEcology insights differ from those uncovered by other ecological methods (in scope, reliability, applicability etc. )? Can insights and observed patterns from regions with high digital coverage be generalized to those without? How should we attach uncertainty to data from iEcology sources? Should we aim for centralized or decentralized repositories and datasets? Are there particular skills that should be taught to students to help them develop iEcology expertise and how can these be integrated in curricula? Are Linnean or Wallacean shortfalls manifested in iEcology sources similar to classical ecological sources? Will the future development of iEcology cause greater detachment from nature even among naturalists or ecologists and how could this be averted? Will the use of iEcology alleviate some of the ethical concerns of handling animals, and will it give rise to new ethical concerns?.
Acknowledgments I. J. 's work was supported by the  J. E. Purkyně Fellowship of the Czech Academy of Sciences. F. C. was supported through the Invacost grants by the  ANR , the  AXA Research Fund Chair for Invasion Biology and Biodiversa AlienScenarios. R. A. C. acknowledges funding from the  Helsinki Institute for Sustainability Science (HELSUS)  to E. D. M. E. D. M. thanks the  European Research Council (ERC)  for funding under the European Union's  Horizon 2020 research and innovation program  (grant agreement # 802933 ). A. T. S. acknowledges the  EU   Horizon 2020 research and innovation programme funding  (project grant No. 677039 ), and  ERDF/ESF  funding ( CZ. 02. 1. 01/0. 0/0. 0/16_025/0007417 ). U. R. is supported by the  Israel Science Foundation  (grant No. 406/19 ). J. A. F. was supported by a research fellowship from  Merton College  and  BBSRC  ( BB/S009752/1 ) and acknowledges funding from  NERC  ( NE/S010335/1 ).
Statistics SStatistical analyses were performed using the software XLSTAT 2011. We used a Spearmen correlation and Factorial Discriminant Analysis (FDA) to highlight if there is any seasonal dissimilarities and describe the main patterns of temporal variability of microphytoplakton taxa in relation to environmental factors. 2B, D ). Spring and autumn seemed to be transitional periods. However, we observed an important decrease in salinity (up to 31 in  November and December 2008 and up 32-33 in April 2009)  in Oued Laou estuary (Fig. 2B ). This decrease could be related to heavy rainfall events (Fig. 2D ) and the subsequent lower salinity of S1 seawater due to freshwater input from Oued Laou river. Along the seasons, pH was slightly basic and oscillated around 8 for both sites. The lowest values were measured during autumn (7.
Rijal  Leblad et al. , (2017)  showed throughout laboratory experiments a partial and progressive elimination of PSP toxins in two investigated mollusks, with a slower elimination kinetic in the tuberculate cockle when compared with the sweet clam, needing 120 and 3 days to reach levels of 80 μg SXTeq 100g -1 of meat, respectively. In our study, Dinophysis caudata showed the highest abundance, followed by Dinophysis fortii, Dinophysis acuminata and Dinophysis rotundata. Even though Prorocentrum lima is known to be a benthic species, it showed abundances of up to 1300 cells. L -1 in the water column of Oued Laou Estuary during August 2008. The other recorded benthic dinoflagellate Ostreopsis showed low abundance not exceeding 90 cells. L -1. The occurrence of benthic HABs species have been well documented in Western Mediterranean  (Vila et al.
Scalograms are the dedicated tools for detecting the latter. They also have the advantage of better time localization for short-duration/high-frequency events, and better frequency localization for low-frequency, longer-duration events. Comparing the interaction between carbon fluxes and environmental parameters among different ecosystems requires concurrent time series of turbulent data obtained with a common methodology and standardized post-processing treatments. Thus far, this requirement has not yet been satisfactorily met for the various methodologies used either when measuring turbulent variables or for processing the data obtained. Moreover, concurrent time series of flux and meteorological data extending beyond 10 years were not available until recently. The present study is built upon time series of data from different types of ecosystems representative of the French network of stations contributing to the European ICOS Research Infrastructure.
1999; Mouillot et al. 2001; Loustau et al. 2005; Vuichard et al. 2007; Cabon et al. 2018, supplementary material § S2). The net ecosystem exchange (NEE) was determined using the eddy-covariance methodology which combines measurement at 20 Hz of the 3-D wind speed and  surrogate temperature using a sonic anemometer, and with the molar fractions of CO 2 /H 2 O using an infrared gas analyzer (see Table  2  for sensor references at each site). 2. 3. Eddy-covariance data processing 2. 3. 1. Flux computation. Half-hourly eddy-covariance fluxes from the five selected sites were calculated using EddyProV R (www. licor. com/eddypro). Standardized processing was applied for all the sites. i) Axis rotation of the wind vector was performed using the planar fit methodology  (Wilczak et al. 2001)  for Barbeau, Puechabon, Le Bray, Laqueuille and the double rotation for Aurad e because of the fast growth rate of the vegetation.
Statistical analyses 2. 4. 1. Random forest analyses. A random forest (RF) machine-learning algorithm  (Breiman 2001 ) was used to identify how the environmental controls affect the variability of NEE, GPP and R eco in the long term. The RF is a non-parametric statistical estimation technique based on the use of decision trees and multiple regressions. A detailed explanation of the algorithm is given by  Breiman (2001). Each of the decision trees that form an RF are built from bootstrap samples of the original dataset (i. e. , training data). For each tree, the data that are not included in the bootstrap samples are named Out-Of-Bag (OOB) samples (i. e. , test data). The training data is randomly chosen for the splitting rule at each node. Each tree is then fully grown or until each node is pure.
The higher the difference is, the more important is the explanatory variable. The package 'randomForest' (Version 4. 6-14, Liaw and Wiener 2018) available in R was used for these multiple regression analyses and is presented in the supplementary material (S3). The analysis was conducted first to compare five plant functional types (PFTs) at the half-hourly time-step. Secondly, we performed the analyses at weekly, monthly and seasonal scales for only two sites, those presenting contrasting results at the half-hourly time-step. Among the data presented in section 2. 2, the data used for the RF analyses were S d , T air , P, u, D, h EW , and carbon dioxide concentration (CO 2 ). To make the graphs easier to read at the aggregated time scales, we aggregated radiative parameters into an 'Energy' variable (S d þ T air ) and an Index of Stress relative to the air or soil water deficit constraint defined by 'IStress' (h EW þ D).
Du s, s ð Þ ¼ ArgðW xy s, s ð ÞÞ (6) Eq. 6 equals the difference of individual phases, Du ¼ u x Àu y , when converted to an angle in the interval] Àp, p]. The package 'WaveletComp' (Version 1. 1, R€ osch and Schmidbauer (  2018 )) available in R was used for these coherence analyses. The parameters used in the analysis and the scalogram reading guidelines are provided in the supplementary documentation (Supplementary material: § S3. 2, S3. 3 and Table  S2 , Fig. S2 ). All the results are presented as image plots of both the individual wavelet power spectrum (and average) and cross-wavelet coherence, in the time-period domain for the period 2006-2011. This period was selected as a common period for the three selected sites and with the lowest gap-filled data percentage. Since, among the five sites considered, our Random Forest analysis allowed us to identify three main patterns and our power and wavelet results are shown for three sites selected out of the five original sites: a forest with permanent foliage, Puechabon (similar to Le Bray), a forest with seasonal foliage, Barbeau, and the crop site, Aurad e (similar to Laqueuille).
Temporal behaviour of CO 2 flux variance partitioning Our spectral analysis confirms previous conclusions extracted from the Fluxnet dataset  (Stoy et al. 2009 ). Both our cross-coherence analysis across temporal scales and our RF analysis confirm the changes in importance of climate drivers on ecosystem CO 2 fluxes, GPP and R eco , with decreasing time frequency. An important result of the RF analysis is to confirm the changes in importance of climate drivers of ecosystem CO 2 fluxes, GPP and R eco , with decreasing time frequencies commonly observed in literature. This conclusion is also supported by the cross-coherence analysis across temporal scales. The variance of half-hourly ecosystem fluxes exceeded the variance of driving variables, as observed previously by  Katul et al. (2001)  at the Harvard Forest site and by  Ouyang et al.
Acknowledgements This work used meteorological and eddy covariance raw data obtained within the framework of European projects:  Euroflux, Carboeuroflux, CarboEuropeIP, CarboAge, GHG Europe, IMECC. VM acknowledges the financial support from the national agency ADEME for the eddy covariance data processing and harmonization within the  CESEC  project (PI: B. Longdoz), as well as the financial support from the  European Union 's  Horizon 2020 research  for the  RINGO  project (grant agreement No  730944 ). The authors would like to thank all the PIs and technicians (e. g. Karim Piquemal ,  Franck Granouillac ,  Bartosz Zawilski  and  Nicole Ferroni ) of the eddy covariance sites, and site collaborators involved in ICOS-France who are not included as co-authors of this paper and finally the support of the 'Nouvelle Aquitaine' region, the  Regional Spatial Observatory (OSR) , the  CNRS  ( Centre National de la Recherche Scientifique ) and the  CNES  ( Centre National d' Etudes Spatiales).
05). For each site, we carried out a PCA on centered and reduced variables in four treatments (Ac, Euc, Ac-AE, E-AE) to get the correlation circles between Olsen data, Po and LMWOA forms. We carried out also a between-class analysis (BCA) on a matrix of Po forms and LMWOA concentrations determined for each treatment to assess the effect of the treatment on the profile of Po forms and LMWOAs. BCA maximizes the differences between the centroids of the classes of samples because its classification method is based upon the ordination of classes of samples rather than of samples. The statistical significance was assessed by permutation (Monte-Carlo test, 1000 permutations, p<0. 05). These functions are available in the ade4 package  (Chessel et al. , 2004). All statistical analyses were performed with R software version 3.
Ordination of organic P and LMWOA forms among treatments BCA of the complete dataset showed that the native areas were very different from the planted plots and this masked the effects of the various planted treatments on the ordination of P forms and LMWOAs (data not shown). We, therefore, chose to present only the BCA maps of the data for the planted treatments (Fig. 5 ). The separation between treatments was highly significant at both sites (Monte-Carlo test, p=0. 003 and 0. 002 respectively at Itatinga and Kissoko). In total, the ordination explained about 80% (Itatinga) and 93 % (Kissoko) of variance (Fig. 5 ). At Itatinga, the soils from Ac and Euc were clearly separated (Fig. 5B ). As shown in Figures  5A  and 5B , Ac had high concentrations of glucose-6-P and ATP, and low concentrations of AMP, phytate, Fruc bisP, malonate and malate.
Acknowledgments This study was supported by the  French Agency of National Research  through the  "Intens&Fix" project  ( ANR-10-STRA-004-04 ). K. W. was supported by a scholarship from the  Ministry of Science and Technology (2556001073) of Thailand and Silpakorn University  for her PhD project. We thank  Jean Thioulouse  for his advice on BCA. We warmly thank the reviewers for their comments that helped us to improve the manuscript. We would like to thank also the staff of the  Itatinga Experimental Station (ESALQ-USP ) and  Eder Araujo da Silva and Floragro , the staff of the  Pointe noire Experimental Station (CRDPI) , and  Manon Villeneuve  ( IRD-Eco&Sols ) for their technical support. The two sites belong to the  SOERE F-ORE-T network , which is supported annually by  ECOFOR, AllEnvi  and the  French National Research Infrastructure ANAEE  (http://www.
Data analysis The analysis was performed in R 3. 6. 0 (R Core team 2019). Three factors were considered: food hardness (peanut: N = 38 vs. walnut: N = 27), age (adult: N = 35 vs. juvenile: N = 30) and sex (females: N = 35 vs. males: N = 30). For each feeding behavior, two variables were investigated: (i) count per trial, calculated as the count of events involving this behavior per feeding trial; and (ii) contribution to the entire feeding repertoire, calculated as the total percentage of events where the behavior was observed. In order to take into account nonindependence of data due to animals being involved in multiple trials, the effect of food type, sex and age on count per trial was estimated from generalized linear mixed models built using the 'lme4' package, with a Poisson distribution and individuals as a random effect  (Bates et al.
Acknowledgments This work was supported by the  Guangzhou Zoo and Sun Yat-sen University. This research did not receive any specific grant from funding agencies in the public, commercial, or not-forprofit sectors. We are very grateful to  Chen Wu , the zoo caretakers,  Yu Chen Yu ,  Mao Yue  and two other students for their help with data acquisition, as well as students and postdocs from the  School of Sociology and Anthropology  for their support.
Maisch GmbH, Germany). The peptides were then eluted over 30 min with a linear gradient of 5 to 28% acetonitrile with 0. 1% formic acid in water ,15 min gradient of 28 to 95% acetonitrile with 0. 1% formic acid in water, and 15 min at 95% acetonitrile with 0. 1% formic acid in water, at a flow rate of 0. 15 l/min. The MS/MS analyses were performed with a Q Exactive Plus mass spectrometer (Thermo Fisher Scientific) in a positive mode using repetitively full MS scan followed by collision inducing dissociation of the 10 most dominant ions selected from the first MS scan. Raw data were subjected to the SEQUEST search algorithm using Proteome Discoverer Software version 1. 4 (Thermo Fisher Scientific). Detected peptides were searched against human and E. coli UniProt databases as fully tryptic peptides and in addition against the human caspase-3 sequence as semi-tryptic peptides.
ORCHIDEE land surface component ORCHIDEE is a global process-based model of the land surface and the terrestrial biosphere, that calculates water, energy, and carbon fluxes between the surface and the atmosphere. The model, initially described in  Krinner et al. (2005)  for the version used in the IPSL-CM5 model, has been significantly improved in version 2. 0 used in IPSL-CM6A-LR. We only summarize below the main characteristics of ORCHIDEE and key improvements from the CMIP5 version. The vegetation heterogeneity is described using fractions of 15 different Plant Functional Types (PFTs,  Prentice et al. , 1992)  for each grid cell. All PFTs share the same equations but with different parameters, except for the leaf phenology. The annual evolution of the PFT maps (including a wood harvest product) is derived from the LUHv2 database  (Lurton et al.