dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion GnpIS provides an archive for phenotyping experimental data compliant to FAIR principles in terms of data access, traceability of the metadata, and citability of the datasets. It applies open data recommendations promoted by several national and international infrastructures, scientific societies, and funding agencies. It also allows for integrating different sets of data to support different types of researches in the field of the adaptation to environment or to the impact of climate change. As there is no global archive for phenotyping data, GnpIS has been built to be integrated in several federations of information systems accessible through common data portals, the oldest one being the WheatIS portal. This has been possible thanks to the continuous implementation of the current standards recommended by the international community, hence facilitating interoperability between information systems and data integration and providing strong foundations for new federations.
We also thank the INRA Gitlab team and especially Damien Berry, Maurice Baudry, and Christian Poirier for the FORGE MIA service. We have reused in the figures the images provided by INRA (Jean Weber © INRA and Raphael Segura © INRA). This work was supported by INRA, by the Infrastructure Biologie Santé 'Phenome-FPPN' supported by the French National Research Agency (ANR-11-INBS-0012), the TransPLANT project (EU 7th Framework Program, Contract no. 283496), the H2020 ELIXIR-EXCELERATE project (funded by the European Commission within the Research Infrastructures programme of Horizon 2020, Grant agreement no. 676559), and the "Investments for the Future programme" (PIA) (ANR-11-INBS-0012). Developments of Wheat, protein crops, rapeseed, and miscanthus ontologies have been supported by the Breedwheat (ANR-10-BTBR-03), BFF (11-BTBR-0006), Rapsodyn (11-BTBR-0004), and Peamust (11-BTBR-0002) PIA projects.
Acknowledgments We gratefully acknowledge the whole  INRA Ephesis User Committee  for inputs and help in specifying GnpIS-Ephesis functionalities:  P. Bertin ,  A. Charcosset ,  C. Lecomte ,  A. Klein ,  A. Raffin ,  B. Quillot ,  P. Burger ,  E. Costes ,  A. Dimouro ,  C. Pichot ,  J. B. Magnin-Robert ,  J. Quero-Garcia ,  E. Balsemin , and  A. Gauffreteau. We thank  C. Anger  and the whole  French Common Garden network (Plantacomp)  for their contribution to the specifications and data publication. We are grateful to the other members of the  Phenome-Emphasis. frinformation system team, including  V. Negre ,  A. Tireau , and in particular  P. Neveu , for continuous fruitful discussions. We are grateful to the  Straw Cereal Network leaders ,  G. Charmet ,  F. X. Oury , and  A. Gauffreteau , who entrusted GnpIS for the publication of their data that made the first important public.
In-kind contribution was made by the Crop Ontology project team supported by the Integrated Breeding Platform and by the CGIAR Platform on Big Data for Agriculture. Some developments were supported by the RDA Europe Project funded by the European Commission. Composed of a triplet of terms: Trait, Method, Scale Dataset (Trial set): A consistent phenotyping dataset includes one to many trials, with their data and description (MIAPPE Study) Trial: A phenotyping experiment in a single location for one (annual plants) to multiple (perennial plants) years Germplasm: The plant material being experimented, including its unambiguous identification Observation Unit: The objects being measured. It combines experimental factors, germplasm, and observation at a given scale Level (scale): Defines whether an observation has been made on a single plant, a group of plants (plot), a whole trial, or any other scale Observation Phenotype or Environment (Measure): Observation consists in triplets including an Observation Variable, a value, and an optional date or timestamp Interoperability pivot Objects and terms that can be (Key Resource): unambiguously identified and can be shared among datasets, hence allowing link building.
For instance, germplasms shared between genotyping and phenotyping datasets Unique IDs: Unambiguous identifiers that must be valid at the scale of the world wide web. They include a prefix to define the namespace and the actual ID. For instance, URI (Uniform Resource Identifier) and DOI (Digital Object Identifier) Open science: Movement and policy to give access to scientific publication, data, samples, and software. It encompasses open data and open source software Open data: Policy and means to access any data type. It is enabled by applying the FAIR data principles Open source: Software licensing principles that ensures free redistribution and modification of tools and systems source code. use.
Ten intensive operating periods were organized during the JU2003 experiment, and in each intensive operating period, puff releases were made close to the ground. For each release, a known amount of the non-toxic and inert atmospheric tracer sulphur hexafluoride (SF 6 ), a passive gas that can be detected at concentrations as low as 10 pptv  (Clawson et al. 2005) , was instantaneously injected into the atmosphere by exploding balloons filled with the gas. Tracer concentration data collected after each puff release are used to evaluate nine ADMs under the UDINEE project. These data can be seen as representative of the dispersion process following an RDD explosion. The observations and model results were uploaded and made available to all participants through the ENSEMBLE web-based platform (http://ensemble.
List of Participants and Models Table  1  summarizes the models and groups that participated in the UDINEE project; six modelling groups from Europe and two from North America have applied their modelling systems. The nine models participating in this exercise are documented in the scientific literature referenced in Table  1. Differences in the model results are due to the use of different meteorological inputs or physical parametrizations (e. g. Hanna et al. 2006; Neophytou et al. 2011). It is beyond our scope to identify the individual causes of the similarities or differences between observations and model results. For more information on the characteristics of each model, see other articles in this special issue. Since the project aims at identifying the overall performance of the models analyzed and the state-of-the-art in urban dispersion modelling, each model is identified hereafter by an anonymous code (M1 -M10).
Observations The JU2003 database fulfils the requirements for evaluating urban dispersion models, as described in the COST ES1006Action documents (COST ES1006 2015c). The database resolves the short-time-scale variability in meteorological variables and pollutant concentrations occurring in the urban canopy layer. In our study, and after review of all release trials and model predictions under UDINEE, we work with two subsets of tracer concentrations, namely intensive operating period 3 (IOP3) and 8 (IOP8). This selection is based on the availability of results from all models participating in UDINEE and on the abundance of collected tracer measurements in each intensive operating period  (Clawson et al. 2005) , thus guaranteeing the largest statistical sample. Figure  1  shows the location of the release points (red stars) and of the fast-response tracer gas analyzers (blue dots)-for IOP3 and IOP8  (Clawson et al.
Data from eight puff releases (four during daytime and four during night-time) are used. We have considered as "acceptable" those pairs of observed and modelled concentrations where both were above 400 pptv. The results have shown large differences among models in the simulation of the puff passage at the measurement locations under the same or different meteorological conditions. The comparison of the modelled and observed concentration distributions shows that most of the models capture, reasonably well, the smallest concentrations (< 10th percentile) and the largest ones (> 90th percentile) in both intensive operating periods. In contrast, there are two models presenting large underestimates of the smallest concentrations. The predicted concentrations within a factor of two of observations are similar and ranged between 1-34% during daytime and 1-36% during night-time.
The large amount of data already generated under UDINEE and stored in the ENSEMBLE system is valuable not only for developing advanced tools for emergency -response situations but also for refining urban dispersion models. By building on the present database, future work on the comparison of modelled and observed concentrations will help to further improve the present modelling tools to match these good quality experimental data. To proceed to more realistic simulations of the consequences from RDD explosions, experimental data on dispersion and deposition of particles (of different sizes) are needed. However, it is difficult to obtain such experimental data to evaluate models. To this purpose, new experiments should be organized in urban areas to collect meteorological and concentration fields following deliberate releases.
Acknowledgments We gratefully acknowledge the  European Commission Directorate General for Migration and Home Affairs  ( DG HOME ) for their support to the  Urban Dispersion International Evaluation Exercise (UDINEE)  activity. Authors want to acknowledge the contribution of various groups to UDINEE. The following agencies have prepared the data sets used in this study:  U. S Army Dugway Proving Group  as manager of the JU2003 database. Data from tracer monitoring stations were provided by the  National Oceanic and Atmospheric Administration (NOAA) Air Resources Laboratory Field Research Division ; Data from meteorological monitoring stations were provided by  Dugway Proving Ground.
Data Analysis Interface width-Interface width was measured from fluorescence intensity profiles extracted from midplane images of PAR-2 and PAR-6 in dual labeled zygotes from nuclear envelope breakdown (NEBD) to the onset of cytokinesis, with two interface measurements obtained for each embryo (Supplementary Figure  S3 ). We observed a general sharpening of the interface beginning 60-100 s prior to furrow ingression for PAR-2 (Supplementary Figure  S3 ), which coincided with onset of cytokinetic ring assembly and a period of active alignment of PAR domain boundaries with the ingressing furrow  [48]. No sharpening was observed for PAR-6 (Supplementary Figure  S3 ). The cortical profile was segmented for each timepoint using the available fluorescent channels and custom-built software in Matlab (Mathworks®), and subsequently straightened in Fiji [49], using a 20 pixel line thickness.
Intensity profiles were obtained by averaging the brightest three pixels at each membrane position. PAR-2 profiles were fit by I x = (a + b 2 ) + b 2 ⋅ erf x -c ⋅ 2/λ ; where erf is the error function as implemented in Matlab. In a first round of fitting, the inflection point (interface center) of the curve was determined. A second round of fitting was performed on a region of ± 20 µm around the center to determine σ. Fitting accuracy was then determined by smoothing the data using a Savitzky-Golay filter and subtracting the data from the fitting curve within the gradient region. If the maximum of the absolute difference exceeded an empirically chosen value (between 6% and 8% of the amplitude of the fitting function, depending on the noise level) the data were discarded. We averaged PAR-2 distributions at three consecutive timepoints spaced 20 s apart at approximately 3 min prior to furrow ingression coinciding roughly with NEBD.
Polarity of P cells from SPIM images-Polarity of P cells was assessed by first creating a 3D membrane rendering of PAR-2 fluorescence intensity obtained by diSPIM imaging, using custom-built Matlab (Mathworks®) software. Subsequently, the center of mass is determined by averaging all positions of the membrane rendering. Next, a plane that cuts the center of mass is rotated in all directions in steps of 5°, at each step dividing the cell into two halves. At each step the histogram of surface fluorescence intensity is determined on either side of the plane and the overlap of these (normalized) histograms taken as a measure of polarity. High overlap indicates the two halves on either side of the bisecting plane are very similar, while no overlap indicates perfect polarity. The plane with minimal overlap (when the two sides are most different) is defined as the plane of maximum polarity.
These values were then used to calculate the ASI as above, again normalized to wild-type controls. For Figure  5g  and Supplementary Table  S1 , membrane-associated GFP::PAR-2 was extracted as for SPIM analysis of P3 cell polarity above and histogram overlap (o H ) calculated to obtain a metric for asymmetry that was comparable to the Figure  5d. Diffusion Analysis-Tracking was performed in Python, using the trackpy package [50], and custom code developed for our analysis (see code availability ). Our analysis follows  [34]. Briefly, MSD was calculated for each particle and the first ten lag times were fit to MSD = 4Dt α. For every embryo, a mean value for D was obtained by averaging D for all particles between 0. 9 < α < 1. 2. Notably, we used 20 ms exposures and 60 ms intervals between frames, as opposed to continuous imaging every 33 ms in  [34].
All other parameters relating cytoplasm and membrane were as described previously  [7]. To simplify analysis, note that this system is symmetric with the same values for diffusion and reaction rates for both PAR species. This assumption is reasonable as empirical values for D and k off , the most relevant rates for gradient length, are similar for the two species. However, for calculating a realistic CPSS for comparison to experiments, we used the measured values for both species, see Stochastic PAR System below. To assess qualitative behaviour of the PAR network upon changing parameters, the governing system of partial differential equations was solved using an adaptive Runge-Kutta scheme [51], using custom-built Python code (see code availability ). Simulations were initialized with two opposing domains with a sharp boundary and run until t = 10000 s.
A simulation was said to break down within the time limit if the concentration of one species was larger than the other across the entire domain. Wave Pinning-The wave-pinning system was simulated using custom Matlab (Mathworks) code, using the pdepe function, with parameters similar to the ones described previously  [27]. For Figures 1 and 2 parameters were changed as indicated in figure legends with the following base set: δ = 1/9 s -1 , γ = 1/9 s -1 , D mem = 0. 1 µm 2 s -1 , D c = 100000 µm 2 s -1 , K = 1 and k 0 = 0. 067/9 s -1. Simulations were run until t = 10000 s. A simulation was said to have become unpolarized within the time limit if the difference between areas of high and low membrane concentration was less than 5%. Mass-conserved Activator Substrate-The mass-conserved activator substrate model (Otsuji, OT) was implemented in Matlab similar to Wave Pinning above, using Model I, previously described  [28] , with the following parameters: D v = 100000 µm 2 /s, a 1 = 1 s -1 , a 2 = 0.
Stochastic PAR System-Stochastic simulations of the PAR system were performed using a Gillespie algorithm [52] implemented in Matlab. The governing equations are ∂ A ∂t = D A ∂ 2 A ∂x 2 + k on,A A cyto -k off,A A -k AP PA ∂P ∂t = D P ∂ 2 P ∂x 2 + k on,P P cyto -k off,P P -k PA A 2 P P cyto = ρ P -ψP A cyto = ρ A -ψ A; , (2) Note the different exponents conferring antagonism as well as different rate parameters for A and B compared to equation 1. Diffusion and dissociation rates were obtained from regressions in Figure  4. Surface-area-to-volume ratios were dependent on cell size, assuming a prolate-spheroidal geometry with aspect ratio 27:15. All other parameter values were as described above (Simplified 2-component PAR System) or as previously described  [7]. Breakdown of simulations at a given cell size was scored as described above for the deterministic system for averages of at least eight individual simulations.
Acknowledgements The authors wish to thank  Nic Tapon ,  Buzz Baum ,  Robert Endres ,  Christoph Weber ,  Julia Pfanzelter ,  Justin Bois  and members of the  Goehring Lab  for critical comments,  Hella Baumann ,  Ben Atkinson  ( 3i ) and  Ricardo Henriques  for providing access and training to a Marianas Light Sheet microscope, the  Salbreux Lab  for helpful discussions, and  Tony Hyman  and  Stephan Grill  in whose labs some of the initial observations were made.
Funding: This work was supported by the  Francis Crick Institute (NWG) , which receives its core funding from  Cancer Research UK  ( FC001086 ), the  UK Medical Research Council  ( FC001086 ), and the  Wellcome Trust  ( FC001086 ), the  EU   Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie grant agreement 675407  (NWG) and a  Bogue Fellowship  from  University College London (LH). NWG is a member of the  GENiE network  supported by  COST Action BM1408  and  EMBO. The authors also acknowledge the  Santa Barbara Advanced School of Quantitative Biology  and the  Kavli Institute of Theoretical Physics , supported by  NSF  Grant No. PHY-1748958 ,  NIH  Grant No. R25GM067110 , and the  Gordon and Betty Moore Foundation  Grant No. 2919. 01. Some strains were provided by the  CGC , which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ).
The expression of various reporter proteins, under the transcriptional control of the yeast upstream activating sequence recognized by Gal4, provided means for monitoring gene expression in a tissue-specific or temporarily triggered manner  9. However, this method is not quantitative due to nonlinear signal amplification, caused by the Gal4 transcription factor. Another interesting method has been specifically developed to directly assay mRNA levels in living cells. A combination of the MS2 phage coat protein fused to a fluorescent protein and an mRNA carrying MS2 binding sites enables direct visualization of transcripts in cells and tissues  10, 11. While powerful, this technique is quantitative only in combination with single-molecule imaging, thus suffers from similar issues as smFISH. There is a clear window of opportunity for improvement of current collection and analysis of gene expression data by allowing researchers to collect larger, more meaningful datasets and to further enhance our understanding of animal development.
Equally unknown is how the activity of transcription factors acting as binary switches of cell fate, such as Ato, is translated into a spatiotemporally patterned expression of target genes. Here, we present a novel approach to quantify the expression of transcription factors and their targets. We combine computational target gene predic-tions with high-resolution imaging of genetically encoded reporters to collect single-cell resolution data in the developing retina. In this manuscript, we present the complete solution for single-cell gene expression quantification in whole tissue, the eye disc datasets we acquired, and insights into the Ato targetome.
2ab ). To facilitate image registration, we created a disc coordinate system. This system is based on the mean diameter of nuclei in each sample, their position relative to the disc edge, and the line of maximum Ato expression in the morphogenetic furrow (MF) (Fig. 2cd  and Supplementary Fig. 3 ). From 389 imaged discs we obtained over 3. 4 million nuclei. However, approximately 21% of nuclei were excluded from analysis because they had a volume deviating from the sample mean by more than 50%. This deviation was likely a result of over-or under-segmentation. The mean nuclear volume of ˜30µm  3  , calculated from our data (Fig. 2e ), is on the same order of magnitude as those measured by others  26, 27. The nuclear volume was relatively consistent across imaged discs, with the sample mean ranging from 22-38µm 3 (Fig.
For each nucleus, we computed the following parameters: xyz position in disc coordinate system; normalized intensities of Ato[mCherry], T2A-Venus and DAPI; and the prominence of Ato[mCherry] and T2A-Venus intensities. We define the expression prominence as a ratio between signal intensity for a particular nucleus, and that of its 26 nearest neighbors. In summary, we obtained putative Ato target gene expression data from almost 2. 7 million segmented eye disc nuclei.
Expression of Ato and its targets in the eye disc With single nuclei data in hand, we sought to understand the relationships between Ato expression and that of its potential targets. To this end, we combined Ato[mCherry] expression data from all samples and found that our tagged protein reporter recapitulates Ato expression pattern very well. Ato is first expressed by most cells (except the peripodial membrane) in a narrow band along the MF (Fig. 3a ), later however its expression gets resolved only to R8 photoreceptors posterior to the MF (Fig. 3b ). The expression of the ato transcriptional reporter follows a similar pattern and is higher in the R8s. However, it's detectable much further posterior to the furrow (Fig. 3ce ), especially in the R8 photoreceptors (Supplementary Fig. 4 ), and the prominence of expression in the R8s is lower for mRNA than protein (Fig.
Classification of cell types during R8 specification We asked whether the quantity and quality of our dataset would allow the classification of cell state transitions during differentiation. Having detailed single-cell resolution Ato expression data from hundreds of samples, we classified cells based on their position along the anterior-posterior (A-P) axis, Ato expression level, and its prominence. We expected to primarily identify at least four classes of cells: cells anterior to the furrow (Pre-MF), Ato-expressing cells in the furrow (MF-medium), Ato-expressing R8 photoreceptors posterior to the MF (R8) and the remaining cells posterior to the furrow (post-MF). We hypothesized that the differences in Ato expression and prominence in these classes would be sufficient for clustering. Surprisingly, our clustering algorithm failed to identify these classes (classifying some cells in the MF as cells anterior to the MF or as R8s) unless the number of clusters was increased to six (Fig.
The SR Protein Kinase (Supplementary Fig. 6 : SRPK) is expressed at high levels throughout the disc, though higher in the MF-high and the R8 cells. Among the genes that do not clearly respond to the varying Ato levels, the expression of three (Supplementary Fig. 6 : βTub60D, rau, and scrt) onsets in MF proximity, with the highest levels in Ato-negative cells. Four genes (Supplementary Fig. 6 : Abl, CG17724, CG32150, DAAM) do not exhibit differential expression in the Atopositive cells. Five genes (Supplementary Fig. 6 : CG15097 -nSyb) were not expressed in the proximity of the furrow. With detailed high resolution expression data in hand, we asked how well expression profiles of Ato target genes correlate with Ato binding to target gene enhancers. To this end, we performed Ato ChIP-seq in the eye discs.
Keeping in mind the lower cellular resolution of ChIP data, we find an overall strong correlation (ρ=0. 94, p=4. 14E-04, excluding nvy as an outlier) between the degree of binding in ChIP experiments and gene expression regulation in our imaging datasets. Based on the expression data in different classes and the enrichment of Ato binding sites in the gene vicinity (based on the i-cisTarget analysis), we propose that 13 out of 31 analyzed genes (ato, Brd, CG2556, CG9801, E(spl)mδ-HLH, Fas2, nvy, sca, sens, seq, CG13928, Lrch, and SRPK) are immediate and direct targets of Ato in the eye disc. Our data on five genes (CG31176, DAAM, dila, rau, and spdo) contradicts previous predictions  17  , as we did not find sufficient evidence supporting their direct regulation by Ato. However, as the expression levels of these genes in the MF area was close to our detection threshold (0.