dataesr/scientific-paragraphs-categorization huggingface dataset:
The Demonstrators The four demonstrators developed for the Zooniverse platform are GWitchHunters [10] (Gravitational Waves), Deep Sea Explorers [11] (Neutrino Astronomy), New Particle Search at CERN [12] (High Energy Physics), and Cosmic Muon Images [13] (Muon Tomography). Each one of these demonstrators uses real data from experimental instruments that are considered to be in the forefront of their respective fields. GWitchHunters is utilizing data from the Virgo detector  [14]. It focuses on the identification of a specific type of event registered by the Gravitational Wave detector known as a "glitch. " A wide tool set has been developed so that citizen scientists can analyze these data and in doing so "help scientists to study and reduce the noise in the gravitational detectors, with the goal of improving their sensitivity and catching more gravitational waves" (from [10]).
COSMIC MUON IMAGES DEMONSTRATOR The Cosmic Muon Images demonstrator is the online platform for the Cosmic Muon Images citizen science project developed in the context of REINFORCE. The goal is to use data from various muon tomography experiments for cataloging by citizen scientists. The results of this cataloging will be used to further develop our experimental methods and to better understand the response of our detectors to cosmic particle atmospheric cascades. The primary data catalog consists of data collected during the 2018 ARCHé (Archeology with Cosmic Rays, to probe Hellenic tumuli) muon tomography mission at the Tumulus site of Apollonia in Chalkidiki  [21]. Every Zooniverse project follows a predefined structure for the citizen science website development. There are three basic parts, the introductory/welcome page, the main/about page, and the classification segment.
Research The first goal of the research page is to expose all the scientific aspects of a muon tomography project. The citizen scientists do not need prior knowledge of particle physics or detection techniques. Furthermore, the text has been written in simple terms and is adapted for middle schoolers. A few keywords are placed to stimulate curiosity on the different concepts that are important to the muography methodology in general. The second goal of the research section is to guide the readers seamlessly and quickly toward the workflows, by providing sufficient knowledge for them to perform data processing in a fulfilling and creative manner. This is very important for us since a well-informed individual is more likely to perform better and more data examinations. The research section text starts with some generalities on our team's projects and is followed by an image taken from one of our team's papers on the combined results of muography and gravimetry  [22].
Here, we also mention a few interesting facts on the resulting muon flux in order to paint a more complete picture for the reader. Moving on to The Method paragraph, we utilize an image to demonstrate the way a muon tomography measurement is done. The text describes the way these measurements are used to determine the inner-density distribution of a massive object. This description is given in simple terms so that the reader is not discouraged from continuing reading further. At the same time, the information is accurate and communicates the principle behind muography. The text continues with The Detector description of a typical muon telescope of the Diaphane type. An image provides information on the different segments of the detector. In this part, we also describe the data acquisition chain under which the detector operates.
Some studies  [24]  have revealed that only 30% of the appropriate signal should be considered as such in the data recorded by a muon telescope. This leads to a different perspective for the muon tomography experiments where the response of the detector can be studied against extensive air shower products rather than against just the muon component. These two perspectives are complementary and strive to increase the accuracy of the muography methodology. The two proposed workflows will help define patterns that can be cataloged. While there are several ways of using the newly found patterns, we plan to use them to train a machine-learning algorithm. This will allow us to identify similar patterns in new datasets. Other potential uses of the classifications will be investigated and might lead to the creation of new workflows within the demonstrator.
Furthermore, a Help text is provided in a pop-up window with very precise instructions. The Tutorial and the Help text have been created in such a way that they can work as standalone explanations but work in a complementary manner as well. Furthermore, a Field Guide accompanies the user through every step of his classification experience. The field guide includes more detailed explanations regarding the detector operation, the data acquisition, and the relationship between these two and the event visualization we already presented. Finally, if these materials are not enough and the citizen scientists need more help with a specific event, they can highlight it and bring our attention to it. This discussion takes place in the Talk Board section where we are provided feedback and answer questions on topics related to the project and the science behind it.
Scientific materials become widely available through visualization. Most of the people working with data are familiar with this method of realizing data through various kinds of plots (histograms, scatter plots, etc. ). REINFORCE partners understand that this excludes some people from accessing the data and deprive them so much from the information included within this data but from the joy of discovery as well. An answer to the problem of sonorization comes from the team behind the sonoUno software [20]. SonoUno can be used as a standalone program installed by the user or through its recently developed web interface. It gives the users the opportunity to explore datasets in real time by also facilitating the different sensory styles of its users. The sonorization it provides is not specific to the REINFORCE datasets and this makes its development "futureproof" for use outside the scope of REINFORCE as well.
Acknowledgments: The authors are grateful to the family of  Sergey Borisovich-Olga Ivanovna  and  Elena Sergeevna  for supporting the publication idea and providing photographs from their archive,  V. A. Danilov  and  G. I. Greisukh  for sharing the information from their article on 70th anniversary of  Sergey B. Odinokov  [ 55 ], and  A. V. Smirnov  for provided information materials. Thank you to everyone who shared photos (see Supplementary), giving the memories essential visual support. We apologize to friends and colleagues of  Sergey Borisovich  who wanted to participate in preparing this article if we didn't reach out to them.
Experimental dataset The field trials were carried out by the New Zealand Institute for Plant and Food Research and the Foundation for Arable Research at two farms located at Leeston (43° 45ʹ S, 172° 15ʹ E) and Wakanui (43° 58ʹ S, 171° 48ʹ E) in the Canterbury Region of the South Island of New Zealand  (Craigie et al. , 2015). The farm located at Wakanui has achieved the Guinness World Record for the highest wheat yield twice, in 2017 and in 2020. The local winter wheat cultivar 'Wakanui' was grown under non-stress conditions for six consecutive years, first at  Leeston (from 2012 Leeston (from -2013 Leeston (from to 2014 Leeston (from -2015) )  and then at Wakanui (from 2015-2016 to 2017-2018) (see Table  1 ). 'Wakanui' is a soft wheat with very high yield potential associated with a long grain-filling period.
The field management was adapted each year to obtain ideal growth conditions: crops received a yearly N fertilization of between 122 and 284 kg N ha -1 and irrigation up to 210 mm of water. Individual plots (12 m×1. 65 m) were drilled into a top worked seed bed. At both sites, the soil type was Temuka clay loam (Fluventic Endoaquents in the USDA classification), a deep, low permeability soil with high plant available water capacity  (Kear et al. , 1967; Craigie et al. , 2015). The Leeston site was characterized by a shallow water table at about 1 m below the soil surface. Weather data were collected at a weather station located within 2 km from the experimental field and provided daily minimum and maximum temperature, rainfall, solar radiation, and wind speed and relative humidity at 2 m. During all experiments the grain, stem, chaff, and leaf dry weight at maturity, ear number and grain number, grain unit dry weight, and harvest index were determined.
Crop growth models Twenty-nine currently used process-based wheat crop models of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat group (https://agmip. org/wheat/) participated in this study and contributed to the MME output (Supplementary Table  S1 ). Modelling groups were provided with daily weather data (minimum and maximum temperature, rainfall, solar radiation, wind speed at 2 m, relative air humidity at 2 m and average vapour pressure) and soil physico-chemical characteristics (soil water lower limit, drained upper limit, saturation, apparent bulk density, organic C and organic N concentration, and soil pH). Initial soil inorganic N amount was estimated for the upper 150 cm for each growing season, based on mineral nitrogen values measured in 2013 and 2014 in the upper 60 cm and 75 cm of soil, respectively.
The soil was represented by three layers of equal thickness (50 cm) and the distribution of the total initial amount of inorganic N in each layer was estimated at 55%, 30%, and 15%, from the top layer to the bottom layer. Initial soil water content was estimated at field capacity. The same initial values of soil inorganic nitrogen and soil water content were used to initialize the simulations, regardless of sowing dates. The models were calibrated with data measured during the 2014-2015 growing season, including a combination of four sowing dates and four sowing densities, for a total 16 different treatments. Supplied data were the mean of the four replicates. For each experiment, modellers were provided with phenological records: the date of beginning of stem extension  (Zadoks 31)  anthesis  (Zadoks 65 ) and physiological maturity  (Zadoks 87).
Funding This study was a part of the  Agricultural Model Intercomparison and Improvement Project (AgMIP ) Wheat Phase 4 and was supported by the  French National Research Institute for Agriculture, Food (INRAE)  and the  International Maize and Wheat Improvement Center (CIMMYT)  through the  International Wheat Yield Partnership (IWYP, grant   IWYP115 ). SD and PM acknowledge support from the metaprogram Agriculture and forestry in the face of climate change: adaptation and mitigation (CLIMAE) of  INRAE. YC and FT acknowledge support from the  National Natural Science Foundation of China  (No. 31761143006 ). RPR and GBM acknowledge financial support from  BARISTA  project ( 031B0811A ) through  ERA-NET SusCrop  under  EU-FACCE JPI. KCK was funded by the  German Federal Ministry of Education and Research (BMBF)  through the  BonaRes  project '' I4S '' ( 031B0513I ).
AS and TG acknowledge funding by the  German Federal Ministry of Education and Research (BMBF)  through the  BonaRes Project  " Soil3 " ( FKZ 031B0026A ). KCK and JEO were supported by the  Ministry of Education ,  Youth and Sports of Czech Republic through SustES-Adaption strategies for sustainable ecosystem services and food security  under adverse environmental conditions ( CZ. 02. 1. 01/0. 0/0. 0/16_019/000797 ). FE acknowledges support by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's  Excellence Strategy -EXC 2070 -390732324 ". TKDW was funded by the  German Research Foundation  ( DFG, Grant  Agreement  SFB 1253/1 2017 ). MAS and NS at Rothamsted Research received grant-aided support from the  Biotechnology and Biological Sciences Research Council (BBSRC)  through  Designing Future Wheat  [ BB/ P016855/1 ] and  Achieving Sustainable Agricultural Systems  [ NE/ N018125/1 ] jointly funded with  NERC.
Objectives: 'Integrative and Conjugative Elements' (ICEs) and 'Integrative and Mobilizable Elements' (IMEs) are two classes of mobile genetic elements that are complex to detect and delineate. Therefore, they are yet poorly annotated in bacterial genomes. FirmiData provides to the scientific community of microbiologists and bioinformaticians a reference resource of annotated ICEs and of IMEs from Firmicutes. It illustrates their prevalence and their diversity but also gives information on their organization. FirmiData was designed to assist the scientific community in identifying and annotating these elements by using the sequences of these ICEs and IMEs for the identification of related elements in other genomes of Firmicutes. Therefore, Firmidata meets the needs of the scientific community.
Objective Unlike conjugative or mobilizable plasmids, ICEs and IMEs are difficult to detect because of their integration into replicons. This is why they have been neglected and even completely ignored for a long time. However, efforts were made in the last 10 years to develop semi-manual  [1, 2]  and automatic  [3] [4] [5]  methods to search for conjugative elements in bacterial genomes. These led to the identification of an increasing number of ICEs and IMEs and to the demonstration of their diversity. We currently know that these elements are highly widespread in bacteria and archaea  [6] [7] [8]. Despite these recent efforts, ICE and IME annotation is very rarely performed and released in public genome databases. The poor annotation of ICEs and IMEs within bacterial genomes is multifactorial.
It results both from the extreme variability of their gene content and from the low specificity of their signature proteins. It is also due to their complex organization since they often form composite elements, with ICEs/IMEs nested within each other and/or organized in tandems. The objective of FirmiData is to offer to both the communities of microbiologists and bioinformaticians a semi-manual annotation of a large dataset of ICEs and IMEs from Firmicutes. In this work, an expert analysis was done to verify, to characterize and to delimit all ICEs and IMEs they carry. The chromosomes of diverse groups of Firmicutes were chosen to illustrate the diversity of the elements as well as of their various organization into composite elements. The sequences of these elements can be used as reference for the identification of related elements in other genomes of Firmicutes.
Data description FirmiData is a set of 40 public annotated genomes of Firmicutes (information on strains are available in Table  1 , 'Data file 41' ,  [9] ). These genomes were extracted from the Refseq database for which ICEs and IMEs annotation was added using the standard annotation features and qualifiers used in the Genbank format  [9]. The search for ICEs and IMEs relies on data from the literature and on a semi-automated procedure that was described in  [1]  and  [2]. All annotations were carefully checked and corrected when necessary and almost all of these elements were delineated at the nucleotide level. ICE expert annotation is based on the identification of genes carried by their conjugation module and encoding three proteins (relaxase, coupling protein and VirB4) needed for their transfer.
Results A total of 98 ICEs and 148 IMEs were annotated. We consider as ICEs the elements that were delimited and that encode a relaxase, a coupling protein, a VirB4, and one to three integrases. IMEs are delimited elements that encode one or two relaxases and eventually a coupling protein, both distantly related or unrelated to those of ICEs. We also annotated slightly decayed ICEs and IMEs. The name of the elements indicates (i) their type (ICE or IME), (ii) the strain of the host bacteria and (iii) the target gene. Elements marked with an asterisk are not integrated in their primary site but in a secondary one. Those marked with "unk" are integrated in a gene of unknown function. The site of insertion of those labeled with ND or NS is non-determined and non-specific, respectively. In conclusion, the FirmiData dataset constitutes a valuable resource for (i) the microbiologist community interested in mobile genetic elements annotation (ii) the bioinformatics teams developing annotation tools and strategies to detect and annotate mobile genetic elements in bacterial genomes.
gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 10 Lph3177T. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 11 LspYL32. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 12 RhoA2-183. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 13 Sag018883. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 14 Sag201008. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 15 SagNEM316. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 16 SanC1051. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 17 ScoC1050. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 18 ScoC232.
gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 19 Sdy12394. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 20 SdyRE378. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 21 Seq10565. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 22 Seq35246. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 23 Sga2069. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 24 Sparas15912. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 25 Spn034183. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 26 SpnP1031. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 27 Spy2096.
gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 28 SpyHKU. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 29 SsaFDA259. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 30 Ssal25975. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 31 SsalJF. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 32 SsuBM407. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 33 SsuNSUI002. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 34 SsuSC84. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 35 SsuST1. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 36 SsuT15.
gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 37 SsuZYH33. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 38 Step12228. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 39 SthJIM8232. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 40 StpsHKU10-03. gb GenBank (gb) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9] Data file 41 Table 1. xlsx Excel (xlsx) Data INRAE, https:// doi. org/ 10. 15454/ VI7YRB [9].
During the CP MAS experiment, the MW irradiation field was either turned off or continuously on. During CP, the amplitude of the 1 H contact pulse was linearly ramped from 50% to 100% of the nominal 1 H radio-frequency (RF) field strength (90 kHz),  [42]  whereas the amplitude of the  13  C CP spin-lock RF field was 50 kHz. The CP contact time was adjusted to 1 ms, and SPINAL-64 was used for  1  H decoupling (with a 1 H RF field strength of 90 kHz). The 90 1 H RF pulse length was 2. 8 μs. In all experiments, 1024 complex data points were acquired with a 30-kHz spectral window, leading to an acquisition time of 34. 4 ms. The number of scans was set to 8. The recycle delay was set to five times the 1 H MW-driven polarization buildup time constant (τ DNP ) of the samples measured with a 1 H saturation-recovery pulse sequence (using a train of 50 90 1 H RF pulses separated by 1 ms) with the MW field on.
ACKNOWLEDGEMENTS This project received funding from the  French National Research Agency (ANR)  under the grant agreement number  ANR-20-CE29-0012  and from the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French Government program  and managed by the  ANR. We thank  Prof. Olivier Ouari  for providing the bTbK sample.
Discussion We developed a method to smooth one dimensional time-series consisting of successive censored measurements with outliers when the associated measurement uncertainty is not known and the measured quantities have an auto-regressive nature. By discretizing the state space of the monitored quantities, the proposed method has the advantage of being easily adaptable to the specificities of the data (such as measurement censoring and the occurrence of outliers). An experiment on artificial Figure  9 : Correlation of raw Obépine data and flow-adjusted and smoothed Obépine data with the logarithm of incidence rates of the corresponding cities for 15 cities with enough data available on both sides, for a lag time ranging from 1 to 3 days in both directions and for a period ranging from the beginning of the second wave to the 21 st of May 2021.
The proposed method could be further developed. First, the underlying X process could have a longer time dependency than an AR(1) process. We could thus develop an AR(p) version of this method to handle this (with p > 1). Besides, the behavior of the marginal X process, and thus its parameters, are expected to change as we move from a propagation of the epidemic stage to a decreasing stage  [Berestycki et al. , 2021]. Joint treatment of the WWTPs time-series could overcome the lack of individual data to face this problem. One could, for example, automatically detect common breakpoints corresponding to a change in the parameters η or δ. Another possibility would be either to use extrinsic knowledge of the reproduction factor of the epidemic as input data or to add it as a latent variable that slowly evolves from one day to another.
Acknowledgments We would like to thank the WWTPs and the laboratories that contribute day to day to the Obépine network. In particular, the  LCPME (Isabelle Bertrand  and  Christophe Gantzer ) produced three quarters of the raw data that was used to produce Figure 9. The experimental reflections of this work were fed by exchanges with the members of the  Obépine consortium and notably Karine Laurent.
This study measures inequality and inequity in the distribution of clinical trials on cancer drug development between 1996 and 2016, comparing the number of clinical trials with cancer need, proxied by prevalence, incidence, or survival rates for both rare and non-rare cancers. We leverage a unique global database of clinical trials activity and costs between 1996 and 2016, constructed for 227 different cancer types to measure for rare and non-rare cancers: i) inequalities and inequity of clinical trial activity, considering all trials as well as split by R&D stage; ii) inequalities and inequity in R&D investment proxied by trial enrollment and duration; iii) evolution of inequity over time. Inequalities are measured with concentration curves and indices and inequities measured with the health inequity index.