dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to meet this need, a data export mechanism has been developed on the web version of Pl@ntNet, allowing each manager to extract and share their records with their partners. In the case of Lewa Conservatory, the multi-lingual and multi-platform of the Pl@ntNet app improved accessibility for a broad range of potential participants. 8 https://www. nrt-kenya. org/ 9 http://www. ngarendare. org/.
Data quality and validation Appropriate evaluation and validation methods must be designed to ensure the satisfactory quality of the data produced and used by managers  (Kosmala, Wiggins, Swanson, & Simmons, 2016). To improve data quality in citizen science programmes, it is essential to make understanding of the evaluation and validation processes easier for citizens and to design an appropriate computational interface  (Sharma, Colucci-Gray, Siddharthan, Comont, & Van der Wal, 2019). Indeed, one potentially important task for volunteers that is likely to generate errors is the identification of organisms to species level  (Ratnieks et al. , 2016). In the case of Pl@ntNet, validation covers both the visual quality of the images and the reliability of taxonomic determination. All images and public observations visible on the platform can thus be evaluated by any user with a user account.
User votes are weighted with a score assigned to each user, and the daily score of a particular user is estimated on the basis of the number of species with validated observations of this user. The validation of an observation is effective when the score of an image of this observation exceeds a fixed threshold. This validation allows selecting the more reliable observations for training and improving deep learning models used for species identification, as well as to provide more definitive maps. Evaluation of data quality is a dynamic process, which allows quality to be reviewed on an ongoing basis, by all authenticated platform participants (experts as well as novices). A validated observation can thus be invalidated at a later date, if there is any doubt. It is important for all participants to understand how human resources and expertise are invested to validate the data produced.
Acknowledging the motivation of participants Identifying the expectations of citizen participants is essential to increase their engagement. The expectations can be characterized by comments on app stores (tens of thousands in the case of Pl@ntNet), email/messages received, questionnaires produced beforehand or individual or group interviews. In the framework of Pl@ntNet, sharing as much local data and knowledge as possible, such as common names, local plant uses and ecology helps boost the benefits to, and motivation of, the participants. The early identification of benefits for all participants (managers, citizens, researchers, decision-makers, etc. ) avoids potential disappointment and decreasing participation. In this regard, it is important to provide appropriate communication tools to participants to support social learning, community building and sharing  (Jennett et al.
, 2016). For instance, a 'WhatsApp' plant group was considered for the Lewa Conservatory, in order to increase knowledge exchanges between the most involved participants, and to confirm species identifications. The implementation of continuous improvement methods, illustrated in Pl@ntNet by a regular update of the identification system based on validated observations, makes it possible to stimulate participation, and thus ensure increasing overall performance of the citizen science programme. The observation process, which is based on opportunistic data collection by citizen scientists, leads to heterogeneous, non-random sampling and spatial correlations in the data. In order to allow the use of this data for species distribution modelling, specific statistical methods must be implemented to resolve the biases related to the data collection process  (Botella, Joly, Monestiez, Bonnet, & Munoz, 2020).
producing, aggregating and disseminating botanical observations. Initiated in 2009, it is based on a web and mobile computational infrastructure, allowing the identification of plants by means of automatic visual recognition. The public version of this platform covers plants from all continents (America, Europe, Africa, Asia, Oceania). Identification performance depends on the volume and quality of visual data used for training the deep learning model of the identification service. Identification performance of Pl@ntNet is not a central topic of this article, but it is important to note that it is a major concern in the development of the platform. In particular, several performance indicators are computed each time the deep learning model is updated. Therefore, three validation image datasets have been created, one related to the French Alpine flora, one related to the English flora, and a third one related to Mediterranean herbs.
At the time of writing the accuracy measured for the first five species proposed for each image of these test sets ranges from 89% for the English flora to 63% for the most difficult dataset (i. e. the Mediterranean herbs). The identification service is updated monthly based on new data produced, shared and validated by the network of participants (i. e. users who have created an account and become members of the community). Pl@ntNet mainly concerns wild plants (i. e. propagating spontaneously in the natural environment) 2 https://plantnet. org/. F I G U R E 1 Data acquisition and management workflow implemented in the two analysed case studies. (a) In the Ramières Reserve, pioneer citizen scientists (i. e. Citizen scientist (A) ) have provided a large volume of visual data to allow accurate automatic identification by reserve managers and local citizen scientists (i.
ACKNOWLEDGEMENTS This work would not have been possible without the participation of the millions of Pl@ntNet contributors, who made this worldwide initiative possible. We thank all members of the Pl@ntNet community, scientists, resource managers and practitioners who contributed towards the development of this initiative. We thank all the reviewers for their comments on the first draft of this work, and  Simon Dawes  for his meticulous proofreading. We also thank  #DigitAG  for the Ph. D. grant of  Benjamin Deneu. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No •  863463  ( Cos4Cloud  project). Finally, authors would like to thank  Agropolis Fondation , which has supported the development of Pl@ntNet platform since 2009.
Since many years, the Instrumentation, Sensors and Dosimetry laboratory at CEA Cadarache has been working to propose innovative and robust design of fission chambers. In this framework, few simulation tools like Chester or PyFc were developed to make detector prototyping and optimisation possible. Up to now, the code experimental validations are scarce mainly because of the lack of flexibility of regular fission chambers. In order to set up an experimental database for code validation, an innovative modular twin fission chamber, the CFTM, was designed. It allows to set with precision the inter-electrode gap, the filling gas pressure as well as the fissile deposit used. This detector was irradiated in Minerve zero power reactor in order to gather results for code validation. Thanks to a calibration procedure, a bank of experimental results is available for simulation validation.
Third, most of the measurements performed are not absolute since electronic systems are not calibrated in charge or in current. An experimental work was made in order to produce data valuable for code validation. The major part of this effort consisted in the design of a new fission chamber which can be reconfigured on the fly. Additional work consisted in calibrating properly the measurement system to gather absolute charge and current estimations. In the first part of the paper, the problem of code validation is discussed and the CFTM, a modular fission chamber is presented. Then, experiments on the Minerve zero power reactor are summarised. In a third part, simplified simulations are performed to link measured quantities to the one derived by simulation. At last, experimental results are discussed.
III. SIMULATION The simulation of the detector is mandatory to determine the quantity of interest and how information can be gathered from experiments. A simplified simulation of the detector was built using the COSICAF simulation suite  [4]. Because of the specific mixture used in the CFTM, it was necessary to expand the database of COSICAF. W values for argon nitrogen mixture were taken from Tawara & al  [5]  while the electron drift velocity was computed by using BOLSIG software and the cross section from Phelps & pitchford for nitrogen and Yamabe & Buckman for argon  [6]. The Moderation laws used for slowing down fission chamber in the gas were also computed thanks to PRAL algorithm and SRIM stopping powers  [7]. Two pressures were investigated as well as two polarisation voltage: Direct polarisation, where the cathode is the deposit holder and inverse polarisation, where the anode correspond to the holder.
There are few limitations in this simulation. First only two mean fission fragment are simulated, hence the pulse height spectrum fails to capture the effect of U235 fission yield. Second, COSICAF is only able to handle one heavy ion per fission which limits the simulation to the detector with thick fissile deposit. At last, the code is designed only to simulate current pulse at the output of detector working in saturation regime; It fails to take into account recombination regime. Few pulses simulated with a direct polarisation are available in Fig 5. Their amplitude is around 1 µA, which makes them distinguishable by using the typical CEA large bandwidth current preamplifier. I onic p ulses a re r eally l ong and should not be distinguishable with current amplifier because of amplitudes of the order of nano amps.
V. RESULTS Only a few experimental results obtained with current preamplifier a nd w ith P 2 a re p resented i n t he following section. To have absolute measurements, each preamplifier was calibrated. First, a simulation using SPICE software was made to estimate the theoretical transfer function. Even if the transimpedance order of magnitude is good, real preamplifier's gain seem to departure from the computed value because a stage of the preamplification is tunable through potentiometer. We then decided to use a arbitrary wave generator and a well characterized current injection device to measure the current preamplifier t ransimpedance. P ulses r ecorded w ith P2 detector polarised at 700 V with a filling pressure of 2 bar are shown in Fig 8. Rectangular and triangular pulse shapes are noticeable as predicted by the simulation.
VI. CONCLUSION A new detector, the CFTM, was designed and built for simulation validation purpose. Thanks to its reconfigurability, we showed it is possible to access intermediate quantities generated by simulation like charge pairs created or drift velocity. An experimental campaign took place in Minerve zero power reactor with the CFTM. At this occasion numerous data were gathered at different polarisation voltage and filling gas pressure. The calibration of the electronics measurement system allowed to estimate the absolute charge and current at the output of the detector. Unfortunately, due to our measurement system limitations, the signal to noise ratio is too low to provides most of the quantities awaited with a precision good enough to validate simulation codes. Still, it is possible to assess the global accuracy of a simulation with the help of charge spectra.
( a) Gaia DR2 (Gaia Collaboration et al. 2018) (b) 2MASS  (Skrutskie et al. 2006 ) (c) Computed from K2MASS with a bolometric correction of 3. 0 (d) The temperature is based on Gaia Bp/Rp photometric colours and was estimated for sources brighter than G=17 mag for 3000K<T eff <10000K. Due to the degeneracy of the photometric temperature with interstellar extinction, an empirically trained machine learning algorithm was used for external spectroscopic datasets in low-extinction regions in order to derive temperatures  (Andrae et al. 2018). However, due to the limited training set for cool stars, photometric temperatures have to be taken with extreme caution for these stars plex. In this context, a special emphasis has to be put on the problems with establishing their inter-dependence: a quantitative description of how the mass-loss rate depends on individual stellar parameters (and, consequently, how it changes as stars evolve) is essential for models of stellar and galactic chemical evolution  (Höfner & Olofsson 2018).
Data reduction We combined the light from all six telescopes to record three calibrated sets of data on each night on CL Lac using MIRC-X in PRISM50 mode, which covers the H band wavelength range  [1. 52, 1. 55, 1. 58, 1. 61, 1. 64, 1. 67, 1. 70]  µm. On each night, we recorded three sets of MIRC-X data on CL Lac. Each set consisted of 10-15 min to record fringe data and another 10 min to record backgrounds and shutter sequences. In between these sets, we observed calibrator stars to calibrate the interferometric measurements. The calibrators and adopted angular diameters are listed in Table  2. In order to verify, we calibrated the calibrators against each other on each night. We saw no evidence for binarity (closure phases consistent with 0 • within their uncertainties). A fit to the calibrator visibilities provided angular diameters consistent with the adopted values.
The data were reduced using the MIRC-X data reduction pipeline v1. 2. 0 2  to produce calibrated visibilities and closure phases. The MIRC-X detector is susceptible to bias in the bispectrum estimation as pointed out by  Basden & Haniff (2004). The data pipeline corrects for this bispectrum bias with a method similar to the one outlined in Appendix C of that paper. Figure  1  displays the visibility and closure phase data for all the spectral channels as well as the UV-coverage. The data, and in particular A&A proofs: manuscript no. CL_Lac the closure phases, denote the presence of large departure points from the centrosymmetric case (i. e. closure phases not equal to 0 or ±180 • ). The calibrated OIFITS files are available on the Optical interferometry DataBase 3. So as to obtain differential stellar radii in the optical and infrared, in order to retrieve information about the stellar dynamics and opacity run, we performed simultaneous observations with VEGA.
Aperture synthesis imaging For the image reconstruction, we used the MIRA software package developed by  Thiébaut (2008). This package is written in the scientific language yorick 4. It reads interferometry data in the form of oifits files  (Pauls et al. 2005) , and the reconstructed image is compared to the visibility and closure phase data by means of Fourier transforms and a few additional steps. The pixel values are modulated to minimise a so-called cost function, which is the sum of a regularisation term plus data-related terms (χ 2 ). The data terms enforce agreement of the model image with the measured data (visibilities and closure phases). The regularisation term is a distance minimisation between the reconstructed image and an expected image, called the prior. The data and the regularisation terms must be scaled to maximise the Bayesian evidence (the overall probability of the model integrated over all possible model parameter values).
2. For item (ii), we obtained the 100 azimuthally averaged intensity profiles from the synthetic maps using the method explained by  Chiavassa et al. (2009). They were constructed using rings that are regularly spaced in µ (where µ = cos(θ), with θ being the angle between the line of sight and the radial direction). Figure  4  shows that the temporal fluctuation from one snapshot to another (green curves) is large, each snapshot being approximatively 23 days apart. Afterwards, we calculated synthetic visibility amplitudes from the different 1D-MARCS models (purple curve) and the 3D temporally averaged (black curve) intensity profiles using the Hankel transform. Finally, we fitted the visibility data from Fig. 1  separately for the two spectral channels at 1. 52 and 1. 70 µm to obtain the apparent diameter of CL Lac.
Acknowledgements. This work is based upon observations obtained with the  Georgia State University Center for High Angular Resolution Astronomy Array at Mount Wilson Observatory. The  CHARA Array  is supported by the  National Science Foundation  under Grant No. AST-1636624  and  AST-1715788. Institutional support has been provided from the  GSU College of Arts and Sciences  and the  GSU Office of the Vice President for Research and Economic Development. MIRC-X  received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant No. 639889 ). We also thank the grants  NSF 1506540  and  NASANNX16AD43G. This work has made use of the MIRA software, kindly developed by  E. Thiébaut  who we thank for his great contribution to the community.
The ThreeHundred cluster project In this paper we use 324 simulations of massive clusters and their surrounding environment from The ThreeHundred project 1  (Cui et al. 2018; Wang et al. 2018; Mostoghiu et al. 2018; Arthur et al. 2019, Ansafari et al. in prep). The simulations are re-simulated zoom regions of the dark-matteronly MDPL2, Multi-Dark 1Gpc/h simulation  (Klypin et al. 2016). MDPL2 uses Planck cosmology (Ω M = 0. 307, Ω B = 0. 048, Ω Λ = 0. 693, h = 0. 678, σ 8 = 0. 823, n s = 0. 96) and 3840 3 dark matter particles per co-moving 1 h -1 Gpc box. The ThreeHundred project then selected the 324 most massive galaxy clusters at z = 0, followed them back to initial conditions and re-simulated them with higher resolution in regions of radius 15 h -1 Mpc. These simulations use the Gadget-X full-physics galaxy formation code incorporating star formation and feedback from both SNe and AGN.
Filament finding This work focuses on quantifying the bias of using galaxies as tracers of cosmic filaments in cluster outskirts. Filaments are identified from a density field. In our case, this refers to either the number density of simulated gas particles, which we use to extract a three-dimensional reference skeleton, or to the number density of mock galaxies, i. e. , halos matched to observable galaxies. The accuracy of the reconstruction of the filament network depends on the sampling of the data set. We therefore use the capacity of The ThreeHundred simulations to compare filament reconstructions from the underlying idealised case with a realistic setup of future cluster outskirt observations (Sec. 3).
This value was chosen to ensure that cluster centres and massive groups are detected as nodes, and filaments connected to the main halo terminate in saddle points 2  Subsequently, we cleaned and simplified the DisPerSE outputs for our purposes by matching the ends of segments and tracing the matches from each saddle point. We treat each node as owning its own network, connected by saddle points at the lowest density. Figure  3  shows the filamentary network associated with the central object of one of the clusters in The ThreeHundred database. The background shading shows the projected gas density; the white inner circle marks R 200 of the cluster. Most branches terminate within the sphere of 15 h -1 Mpc radius encompassing the cluster, shown as the outer grey circle. Within this region, the full treatment of the physics ensures a realistic and suitable representation of the filamentary structure around massive clusters.
ACKNOWLEDGEMENTS This work has been made possible by The ThreeHundred collaboration 8 , which benefits from financial support of the  European Union 's  Horizon 2020 Research and Innovation programme  under the  Marie Sk lodowskaw-Curie  grant agreement number  734374 , i. e. the  LACEGAL  project. The ThreeHundred simulations used in this paper have been performed in the  MareNostrum Supercomputer at the Barcelona Supercomputing Center , thanks to CPU time granted by the  Red Española de Supercomputación. UK acknowledges support from  STFC , WC acknowledges supported from the  European Research Council  under grant number  670193. CM acknowledges support from the  China Scholarship Council (CSC). AK is supported by  MICIU/FEDER  under research 8 https://www. the300-project. orggrant  PGC2018-094975-C21.
The subfamily GH13_1 of alpha-amylases is typical of Fungi, but it is also found in some unicellular eukaryotes (e. g. , Amoebozoa, choanoflagellates) and non-bilaterian Metazoa. Since a previous study in 2007, GH13_1 amylases were considered ancestral to the Unikonts, including animals, except Bilateria, such that it was thought to have been lost in the ancestor of this clade. The only alpha-amylases known to be present in Bilateria so far belong to the GH13_15 and 24 subfamilies (commonly called bilaterian alpha-amylases) and were likely acquired by horizontal transfer from a proteobacterium. The taxonomic scope of Eukaryota genomes in databases has been greatly increased ever since 2007. We have surveyed GH13_1 sequences in recent data from ca. 1600 bilaterian species, 60 non-bilaterian animals and also in unicellular eukaryotes.
MATERIALS AND METHODS In order to further characterize the distribution of GH13_1 genes in Metazoa, we used the sequence of the sponge Amphimedon queenslandica GH13_1 (GenBank XP_019851448) as a query to perform BLASTP and TBLASTN searches on various online databases available in Genbank (nr, proteins, genomes, assembly, SRA, TSA, WGS), and also in the more specialized databases compagen. org, marinegenomics. oist. jp, reefgenomics. org, marimba. obs-vlfr. fr, vectorbase. org, PdumBase (pdumbase. gdcb. iastate. edu), AmpuBase (https:// www. comp. hkbu. edu. hk/$db/AmpuBase/index. php)  (Ip et al. 2018) , between October 2018 and August 2019. Fungi were not searched further in this study because they are known to have a GH13_1 member as the usual alpha-amylase. To increase the chances to retrieve potential cnidarian or ctenophoran sequences, the starlet sea anemone Nematostella vectensis amylase (XP_001629956) was also used to query those databases.
When only partial sequences could be retrieved using BLAST we collected a large genome region encompassing and flanking the BLAST hit and tried to reconstitute the full-size sequence. The stringent threshold was obviously not applied to constitutively small HSPs such as SRA (sequence read archives). These were only considered when several highly significant hits (typically 1e-10) covered a large part of the query sequence. Since SRA HSPs generally did not overlap, we could not assemble longer sequences and thus we did not use such sequences in alignments or phylogenies. SRA and transcriptome databases are prone to contamination, thus we checked by reciprocal BLAST that the retrieved sequences were not contaminations. SRA databases were used first when no or few assembled genomes or transcriptomes were available (e.
g. , Nemertea, Bryozoa). If a GH13_1 sequence was found in an annotated or assembled genome, SRA HSPs were also used in order to increase the sampling of related taxa and then added some support to the presence of GH13_1 in the lineage considered. On the other hand, we considered that within a given lineage, the absence of GH13_1 sequence in a reliable annotated or assembled genome, combined to the detection of GH13_1 in SRA databases from related species would suggest that the GH13_1 gene was lost within this lineage in some taxa but not all. Conversely, if no GH13_1 sequence at all was found in any annotated genome and in any other database, we considered that the gene was lost in an ancestor of this lineage. When working with "assembled genomes" (nonannotated), we reconstituted exon-intron gene structure as well as the the protein sequence from the TBLASTN results.