dataesr/scientific-paragraphs-categorization huggingface dataset:
Global view of the data lake: entity (dataset) statistics Users are familiar with the data files they brought (PDF, Office formats, JSON, CSV, etc. ), but told us they felt "lost" once the system ingested their data, especially if the latter is large and/or complex. To help them get a first global view of the data lake (or graph, requirement (α)), we present them a set of entity and entity-dataset statistics, as follows: -The total numbers of entities of each type (Person, Location, Organization, date, URI, email, hashtag, mention), overall in the graph; -The total number of entities per type and per dataset in which they appear; -A tag cloud of the most frequent entities in the whole graph. -A summary of the entity-dataset associations: we show the entity type, label, and datasets where it appears, starting with the entities present in the highest number of datasets.
These entities are more interesting, because they allow making connections across datasets (potentially heterogeneous files), saving important manual efforts to journalists combining such data sources for their investigations. The above statistics give a first idea of what the datasets contain, and also suggest entity names to use as search keywords (requirement (β)). For instance, Figure  2a  shows the number of different types of extracted entities and the tag cloud in the dataset of Figure  1. Figure  2b  shows the frequent common entities in the two datasets.
As stated in requirement (γ), users requested more support in extracting tabular data from the graph. For instance, in the HATVP dataset, they may want to extract: for each elected politician, their name, elected office, election date, and companies they have stocks in; note that this query is not a path, since it returns many values. Users view the first three fields as required, but the last one is optional, i. e. , a politician should be part of the result even if they have no stocks. Optional query fragments correspond to outerjoins in database terms. To allow users to easily and intuitively express a much larger set of queries, we proceed as follows. (1) Upon loading, ConnectionStudio computes, from each dataset, a set of elementary paths that can be seen as "query building blocks". Each path reflects one or more consecutive edges in the data graph.
Correcting and improving the graph through a table view The paradigm of path querying also gives us two ways to improve and correct the graph (requirement (δ)). Editing value and entity labels As stated above, an elementary path ending in #val returns the set of values encountered in the data in certain positions, while a path ending in #val. extract:τ , for some entity type τ , shows entities extracted by ConnectionLens from the data, using trained language models  [3]. When visualising the result of such a query, users can edit entity or values shown in the query result, and propagate their modifications to the underlying database, thus updating the graph. ConnectionLens implements a set of similarity functions and (very conservatively) unifies entities whose labels are very similar, e. g.
, "L'Oréal" and "L'OREAL", once they are both recognized as organizations by the entity extractor. The ability to edit the data, offered by ConnectionStudio enables users to further normalize (uniformize) the label of value nodes, and/or of extracted entity nodes. This corresponds to a carefully restricted case of database update through views  [1]. As well known, such updates cannot always be propagated correctly to the underlying database. In ConnectionStudio, we allow updates only on values or entities at the destination end of a path. It is easy to see that such updates can always be propagated to the graph persistently stored in the underlying database. For instance, in Figure  4a , while inspecting the results of a query, when users find multiple versions of the same organization "Alstom", such as "Alsthom", "Als thom" or "Alstom grid", they can correct each of them by hand, then propagate the changes to the underlying database.
Perspectives and conclusion Data lakes such as  [9, 7, 12, 10]  and ConnectionLens  [2, 3]  aim to help users explore many heterogeneous data sources. ConnectionLens adopts a graph paradigm for integrating the sources, and extracts entities leading to inter-dataset connection opportunities. In this work, we describe novel data exploration and discovery paradigms we implemented in ConnectionStudio, following requirements expressed by journalists; they allow users to discover the graph, simplify querying for connections across sources, and as-you-go cleaning of the graph. We believe these features are useful additions to next-generation heterogeneous data lakes. Going forward, we plan to conduct an elaborate user-study in order to understand better how ConnectionStudio helps novice users explore graphs and also inculcate the feedback to further improve upon the features provided by Con-nectionStudio.
How the data were acquired Mutants and WT seeds were collected from Arabidopsis Col-0 genetic background. Briefly, RNA extraction was made from dry -and 6h imbibed seeds and RNA samples were sent to Beijing Genomics Institute (BGI, https://www. bgi. com ). The library construction was made by BGI and paired-end reads were sequenced using the sequencing platform DNBseq TM. Bioinformatic analyses were performed using Salmon (version 0. 14. 1) [1] , FastQC [2] and DESeq2 [3] programs. Results were illustrated using MultiQC tool [4] , the online Venn Diagram tool ( http://bioinformatics. psb. ugent. be/webtools/Venn/ ) and volcano plots using ggplot2 (version 3. 4. 0) [5] package in R studio (version 4. 2. 0). Data format Raw, processed and, analyzed reads Description of data collection Glucosinolate mutants ( qko, cyp79B2/B3 and myb28/29 ) and WT seeds were used in post-harvested dry state or in 6h imbibed conditions containing water or KNO 3 10mM or KSCN 8μM.
a b s t r a c t The presented RNAseq data were obtained from Arabidopsis seeds dry and 6h imbibed to describe, in wild-type and glucosinolate (GSL)-deficient genotypes, the response at the RNA level to nitrogen compounds, i. e. , potassium nitrate (KNO 3 , 10mM), potassium thiocyanate (KSCN, 8μM). The cyp79B2 cyp79B3 (cyp79B2/B3) double mutant deficient in Indole GSL, the myb28 myb29 (myb28/29) double mutant deficient in aliphatic GSL, the quadruple mutant cyp79B2 cyp79B3 myb28 myb29 (qko) deficient in total GSL in the seed and the WT reference genotype in Col-0 background were used for the transcriptomic analysis. Total ARN was extracted using NucleoSpin® RNA Plant and Fungi kit. Library construction and sequencing were performed with DNBseq TM technology at Beijing Genomics Institute. FastQC was used to check reads quality and mapping analysis were made using a quasi-mapping alignment from Salmon.
Gene expression changes in mutant seeds compared to WT were calculated using DESeq2 algorithms. This comparison with the qko, cyp79B2/B3 and myb28/29 mutants made it possible to identify 30220, 36885 and 23807 differentially expressed genes (DEGs), respectively. Mapping rate result was merge into a single report using MultiQC; graphic results were illustrated through Veen diagrams and volcano plots. Fastq raw data and count files from 45 samples are available in the repository Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI) and can be consulted with the data identification number GSE221567 at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE221567. © 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.
Data Description The dataset describes the response of the seeds to KNO 3 and to KSCN at the transcriptional level using a collection of GSL mutants. We sequenced transcripts from 45 samples corresponding to Arabidopsis seeds in Col-0 genetic background: the wild type (WT) and three GSL mutant lines ( qko: cyp79B2 cyp79B3 myb28 myb29 quadruple mutant; cyp79B2/B3: cyp79B2 cyp79B3 double mutant; myb28/29: myb28 myb29 double mutant). Four experimental conditions were analysed with dry and 6h imbibed seeds and with KNO 3 and KSCN treatments in three biological replicates. All fastq raw data files (and count) are available in the NCBI Sequence Read Archive (SRA) database under the repository name NCBI GEO with the data identification number GSE221567 (https: // www. ncbi. nlm. nih. gov/geo/query/acc.
RNA quantification was obtained using a NanoDrop ND-100 (NanoDrop Technologies, DE, USA) and the RNA Integrity was measured using a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). cDNA library constructions were performed and paired-end (PE100) reads were sequenced using the Sequencing Platform DNBseq TM  [15]  by Beijing Genomics Institute (BGI, https://www. bgi. com ). A total of 217 Gb of data corresponding to clean raw sequences (Phred + 33) was obtained. After quality control  [2]  , filtered reads were mapped to Arabidopsis transcriptome (Araport 11)  [6]  using quasi-mapping alignment from Salmon, version 0. 14. 1  [1]. Quality control files of these analyses were merged and summarized into a single report using MultiQC tool  [4]  (Supplementary Table  1 ). Gene expression changes from comparison between mutant versus wild-type seeds were calculated using the algorithm from DESeq2  [3]  , and identification of differentially expressed genes (DEGs) was based on log 2 FC and adjusted p-value thresholds.
Genes with log 2 FC > 1 or < -1 and Benjamini-Hochberg adjusted p-values < 0. 05 were used to determine DEGs. Genes outside these parameters were defined as non-significantly differentially expressed ( Fig. 1  and Table  2  ). Venn diagram tool ( http://bioinformatics. psb. ugent. be/webtools/Venn/ ) was used to identify common DEGs between mutant seeds at different physiological conditions ( Fig. 2  ). Volcano plots were made using ggplot2 (version 3. 4. 0) [5]  package in R studio (version 4. 2. 0). The graphical abstract was created with BioRender. com.
Thus, we start the paper with details of the observational data of the cluster Abell S1063 in Sect. 2. We explain in Sect. 3 the analysis of the X-ray and galaxy spectroscopic data that are required as part of this new method. We, then, detail the building blocks of the mass model in Sect. 4, followed by the definition of our joint X-ray and lensing likelihood in Sect. 5. In Sect. 6 and Sect. 7, we analyse the mass distribution of Abell S1063 and the reconstruction of a mock cluster for comparison. We finally discuss the deviation from the hydrostatic equilibrium; a possible merging event and interrogate our modelling hypotheses in Sect. 8, and concluding with prospects for the application of this new method in Sect. 9. We adopt a flat ΛCDM cosmology with Ω Λ = 0. 7, Ω 𝑚 = 0. 3 and 𝐻 0 = 70 km s -1 Mpc -1 , and we use magnitudes quoted in the AB system throughout this paper.
ABELL S1063: OBSERVATIONAL DATA Abell S1063 (AS1063; also known as RXC J2248. 7-4431 and SPT-CL J2248-4431) is a massive galaxy cluster at 𝑧 = 0. 3475 first identified by  Abell et al. (1989). It is a bright X-ray source with one of the highest X-ray temperatures measured, 𝐿 𝑋 ≈ 2. 5×10 45 erg 𝑠 -1 in the [0. 1, 2. 4] keV band and 𝑇 ≈ 12 keV, respectively  (Rahaman et al. 2021). It also presents a strong Sunyaev-Zel'dovich (SZ) effect detection in the South Pole Telescope survey  (Williamson et al. 2011) , with an estimated SZ mass at the virial radius of 𝑀 200 = 2. 9 × 10 15 𝑀. Fig. 1  shows a colour image of the cluster core made with the Hubble Space Telescope (HST) broad band filters F435W (blue), F606W (green) and F814W (red). AS1063 is well-suited for our analysis as it has been observed in multiple wavelengths -X-ray and optical -from space, as well as with an integral field spectrograph from the ground.
X-ray observations As we are focused on a strong lensing analysis, we will model the core of AS1063. Thus, we choose observations from the Chandra X-ray observatory as it provides the best spatial resolution amongst X-ray telescopes with a field of view (FoV) covering the entire area of interest. In particular, we use three combined archival pointings of this cluster with a total exposure time of 123 ks in very faint 1 (VFAINT) mode only, with data from proposals IDs: 4966 (PI:  Romer, 2004), 18818 and 18611 (PI: Kraft, 2016)  As the cluster covers a large part of the FoV of each observation, we also retrieved the Blank-sky backgrounds  (Hickox & Markevitch 2006 ) associated with them to estimate the emission from the background sky. All of these data have been obtained with the Advanced CCD Imaging Spectrometer (ACIS;  Garmire et al.
Photometric data AS1063 has been widely observed with HST through the Cluster Lensing and Supernova Survey (CLASH;  Postman et al. (2012) ); the Hubble Frontier Fields (HFF;  Lotz et al. (2017) ) and the Beyond Ultra-deep Frontier Fields And Legacy Observations (BUFFALO;  Steinhardt et al. (2020) ). We use fully reprocessed mosaic images produced by the BUFFALO collaboration combining all available observations and including the HFF dataset. For more details about the different available filters, FoV and depth of the HST observations, we refer the reader to  Steinhardt et al. (2020). We use the publicly available measurements on galaxies in the FoV made on the HFF data for this cluster from three different catalogues. In particular, we use the multi-wavelength photometric catalogue produced by  Pagul et al.
Going forward, we use this new redshift catalogue and new MUSE data cube for the rest of this work. We note 10 per cent of the objects have their first redshift measurement from this re-analysis. In addition, AS1063 is a target of the CLASH-VLT program (ESO ID: 186. A-0798; PI: P. Rosati) that obtained 200 hours of observations on clusters from the CLASH sample with the VIsible Multi-Object Spectrograph (VIMOS) previously installed on the VLT before its decommissioning. A catalogue containing almost 4000 redshifts has been produced by  Mercurio et al. (2021)  and released publicly. Thus, we use it to complete the MUSE catalogue mentioned previously on the HFF footprint.
X-ray spectra fitting To obtain the thermodynamic properties of the gas from its X-ray spectrum, we follow a similar approach to the one detailed in  Rossetti et al. (2007). We make a Voronoi tessellation of the X-rays images of the cluster field with the V B python packages  (Cappellari & Copin 2003)  from the photon counts associated with the cluster in the [4, 7] keV band. We need to rely on a tesselation to obtain enough photons on the X-ray spectra. We remove the signal associated from the background of the total signal, and we use the  Gehrels (1986)  approximation to obtain the uncertainty as the sum of the two associated variances. A signal to noise (S/N) threshold parametrises this tessellation, for which we use an S/N of 10 as it seems to be the best compromise between the uncertainty and the mapping resolution in the different S/N that we tried.
The extraction of the spectra is performed with the python package MPDAF  (Bacon et al. 2016 ) developed for the analysis of MUSE data. We use the optimal extraction algorithm for CCD spectroscopy implemented  (Horne 1986). Then, we limit our wavelengths of analysis to 4850 nm-7160 nm in the observer frame, which corresponds to the range used by B19. Measurements of the LOSVD are performed with the python package PXF (Cappellari 2017) modified to use the nested sampling engine pyMultiNest  (Feroz et al. 2019; Buchner et al. 2014a)  as the non-linear optimiser method. LOSVD parameters are obtained from a cross-correlation between the galaxy and a set of star spectra templates. In the original package, parameters are optimised through the minimisation of a 𝜒 2 statistic that has been modified here to maximise the associated Gaussian log-likelihood.
To perform the calibration of the plane and the Faber & Jackson relation, we use a combined approach inspired by the method outlined in  Cappellari et al. (2013). We use the same 𝜒 2 statistics that are defined to fit a plane or a line, taking into account the scatter on all datasets, but we sum the two associated likelihoods to perform the joint optimisation with M N. We assume the statistical independence of both relations in the combined likelihood, which is a priori not true, but allows us to derive a consistent slope between both relations. As shown in G22 and B19, the separate fit can lead to different slopes in the mass versus 𝜎 plane, and thus inconsistent modelling of cluster members depending on which relation is used. Regarding the outliers, we use an iterative approach applied on the fundamental plane only, where all data points that are at more than 2.
One object (system 303. 1) is already present in previous spectroscopic catalogues, while the other comes from these new measurements. In addition, we identify a close pair as a multiply-imaged system that was previously considered as a single image (𝑆𝑊 -54 in  Karman et al. 2015, 2017, and system  304 in this work) and a second pair from one confirmed image and the MUSE narrow band datacube (system 305). These new constraints bring our final dataset to a total of 67 images for 25 systems that are indicated with red circles in Fig. 1. The spectra and optical images of these new systems are presented in Appendix D as well as their coordinates and redshifts. The method used to find these new systems is detailed in  Richard et al. (2021).
This is only possible through the use of the Poisson-Gamma mixture, as the same model under-fits the data at more than 5𝜎 with a Poisson statistic. The 5𝜎 limit is at 1. 5 standard deviation from the obtained best-fit likelihood, but as we are not considering a Gaussian distribution, the 𝜎 rule of thumb does not scale linearly with the standard deviation. When both parametric components are defined, we merge them and proceed to a sampling run that is used as a starting point for the next step. This run, and the following, are performed with the dynamic nested sampling algorithm MLF  (Buchner et al. 2014b ; Buchner 2019) implemented in the python package U N 8  (Buchner 2021).
However, AS1063 does not look totally relaxed, as the temperature map presents some gradients in agreement with a perturbed state. From this analysis and the mass ratio of 300 between the two dPIEs haloes representing the main mass component and the NE one, the remaining plausible scenario is the fall of a much less 10 https://github. com/jeremysanders/ggm massive object, such as a galaxy group, into AS1063. Such events will produce some sloshing of the gas, as previously proposed, which can be constrained quantitatively from the ICM properties in the under/over estimation area of the X-ray surface brightness  (Ueda et al. 2021). In particular, it is expected that the entropy and temperature are higher in the negative excess region than in the positive one, with a difference higher for the first quantity.
Hydrostatic equilibrium and gas fraction As we model with separate haloes, the intra-cluster gas and the rest of the matter, we do not assume the hydrostatic equilibrium. Thus, our method is well suited to analyse the hydrostatic bias by comparing its results with another method on the same observables. To perform this comparison with the associated hydrostatic mass of AS1063, we fit a Navarro-Frenk-White profile  (Navarro et al. 1996)  on the X-ray observations with the H 11 package based on  Ettori et al. (2010)  method. We use the same count, background and exposure maps as well as the plasma emission model, but we use a wider FoV than the one used for the L optimisation. Indeed, we fit the X-ray surface brightness up to almost 1 Mpc as we are less dominated by the background in the circular binning.
As demonstrated by  Mahler et al. (2022) , in the modelling of the JWST data of the first lensing cluster to be observed, SMACS J0723. 3-7327, the flexibility to include an additional mass component that correponds to the baryons locked into the ICL is likely to prove invaluable. Such addition could be done with a similar methodological extension as we presented here for the X-ray data in a self-consistent way. The prospects for comprehensive modelling of the multiple mass components of a cluster, robustly, to include all the baryonic contributors: stellar component of cluster members, X-ray gas and the ICL, in addition to the dominant DM component, is becoming feasible. Finally, we aim to provide one of the first steps of a new cluster mass modelling standard, which is based on a self-consistent and simultaneous inclusion of multi-wavelength data and multiple observables.
5, 12] keV one. Each fit is made in two steps; the first is an optimisation with a 𝜒 2 statistic with the Gehrels (1986) approximation implemented in S through a Levenberg-Marquardt method. During this optimisation, only the B-spline coefficients are not fixed, while the knots positions and their numbers are obtained by the splrep routine. The coefficient values given by the preceding routine are used to define starting points for the 𝜒 2 minimisation. Finally, the second step is a simplex optimisation from the results of the last one with the X Cstat. Initially, we wanted only to include this last step as a Poisson likelihood is more adapted to the considered datasets, but the optimisation was not converging to satisfying models in some cases. The resulting spectra models for the observation of 18611 on each CCD are presented in Fig.
ACKNOWLEDGEMENTS This work used data and catalogue products from  HFF-DeepSpace , funded by the  National Science Foundation and Space Telescope Science Institute  (operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS5-26555 ). This research has been made possible on observations made with the  NASA/ESA Hubble  Space Telescope obtained from the  Space Telescope Science Institute. These observations are associated with program  GO-15117. We also made use of observations collected at the  European Southern Observatory  under  ESO programme (s) 60. A-9345 (A),  095. A-0653 (A) and  186. A-0798. M. L. acknowledges the  Centre National de la Recherche Scientifique (CNRS)  and the  Centre National des Etudes Spatiale (CNES)  for their support.
These measures are implemented in addition to the established techniques implemented within the open source software package openQCD-2. and 2. 4  [3] , such as the Schwarz-alternating-procedure (SAP), local deflation, multi-grid, mass-preconditioning, multiple time-scale integrators and others. All generated gauge ensembles are in   = 2 + 1 QCD. To set the scale the gradient flow time criterion  [4]  is used and results are converted to physical units via √ 8 0 = 0. 414(5)fm  [5].
Data management plan Currently OpenLat maintains a repository of 100TB of data. This includes ∼ 20k saved configurations and is projected to grow to 500+TB with completion of stage 2. The repository is mirrored at currently two separate locations to ensure redundancy and a tape option is used for long term storage. All metadata is preserved on disk and in the main online repository at https://openlat1. gitlab. io. Currently this access is only internal, it will however be made open upon completion of stage 1. The internal metadata follows a detailed provenance policy that includes run and machine information along side the previously mentioned standard observables. To maintain data integrity a two part strategy is followed: the configurations contain the plaquette in the header, as per openQCD format standard, and is augmented by a separately kept list of checksums of sufficient complexity (e.